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<?xml version="1.0" standalone="yes"?> <Paper uid="J87-1002"> <Title>MODUS BREVIS FORMS (MODUS PONENS): Given Premises Conclusion Normal P ~ Q, P Q Missing Minor P--,- Q Q</Title> <Section position="1" start_page="0" end_page="0" type="metho"> <SectionTitle> ANALYZING THE STRUCTURE OF ARGUMENTATIVE DISCOURSE </SectionTitle> <Paragraph position="0"> Consider a discourse situation where the speaker tries to convince the hearer of a particular point of view.</Paragraph> <Paragraph position="1"> The first task for the hearer is to understand what it is the speaker wants him to believe - to analyze the structure of the argument being presented, before judging credibility and eventually responding.</Paragraph> <Paragraph position="2"> This paper describes a model for the analysis of arguments that includes: * a theory of expected coherent structure which is used to limit analysis to the reconstruction of particular transmission forms; * a theory of linguistic clues which assigns a functional interpretation to special words and phrases used by the speaker to indicate the structure of the argument; * a theory of evidence relationships which includes the demand for pragmatic analysis to accommodate beliefs not currently held.</Paragraph> <Paragraph position="3"> The implications of this particular design for dialogue analysis in general are thus:</Paragraph> </Section> <Section position="2" start_page="0" end_page="0" type="metho"> <SectionTitle> 1 THE PROBLEM AREA </SectionTitle> <Paragraph position="0"> Consider the task of designing an &quot;argument understanding system&quot;, a natural language understanding system (NLUS) where the input is restricted to arguments.</Paragraph> <Paragraph position="1"> Consider as well arguments constructed in a dialogue situation, where a speaker (S) tries to convince a hearer (H) of a particular point of view. The hearer patiently listens; hence, the input is &quot;one-way communication&quot;.</Paragraph> <Paragraph position="2"> The argument understanding system therefore plays the role of the hearer, and tries to analyze the structure of the argument being presented. This task is isolated as a necessary first step for a hearer, in order to be a successful participant in a conversation. In other words, the hearer must have some representation of what it is the speaker wants him to believe, before judging credibility and eventually responding.</Paragraph> <Paragraph position="3"> Note that this language problem is relatively new and yet feasible. It is distinct from other NLU endeavors, such as story understanding, which appeal to a stereotype of content in order to reduce processing. In arguments, one is never sure what points the speaker will address; content can't be stereotyped. However, arguments have a defining characteristic - they are necessarily goal-oriented. The speaker wants to convince the hearer of some overall point. Thus, there is an overall logical structure to the input and this fact may be used by a hearer to control analysis.</Paragraph> <Paragraph position="4"> For our model, the representation for the structure of the argument is restricted to an indication of the claim and evidence relations between the propositions. The notion of evidence is discussed in more detail in section 4. A useful starting definition is: &quot;A proposition E is evidence for a proposition C if there is some rule of inference such that E is premise to C's conclusion - in other words, there is some logical connection between E and C&quot;.</Paragraph> <Paragraph position="5"> In order to design an argument understanding system, what is then required is a computational model for the analysis of arguments. This in turn necessitates a theory of argument understanding, as a basis for the model. We suggest the following three components for the model: * a theory of expected coherent structure, used to drive a restricted processing strategy. Analysis is kept to a computationally reasonable task, by limiting the input to be recognized to a characterization of expected coherent forms of transmission.</Paragraph> <Paragraph position="6"> Copyright1987 by the Association for Computational Linguistics. Permission to copy without fee all or part of this material is granted provided that the copies are not made for direct commercial advantage and the CL reference and this copyright notice are included on the first page. To copy otherwise, or to republish, requires a fee and/or specific permission. 0362-613X/87/010011-24503.00 Computational Linguistics, Volume 13, Numbers 1-2, January-June 1987 11 Robin Cohen Analyzing the Structure of Argumentative Discourse * a theory of linguistic clue interpretation, including insight into both the occurrence of clue words and their possible function in overall discourse. Clue words are those words and phrases used by the speaker to directly indicate the structure of the argument to the hearer (e.g. connectives). Detecting clues can thus also serve to constrain the processing for the hearer. Moreover, it is important to have some facility for recognizing and interpreting clue words, to build a model that is robust enough to process a wide variety of input.</Paragraph> <Paragraph position="7"> * a theory of evidence relationships. The most important observation is that pragmatic analysis is mandatory for an analysis model in order to recognize beliefs of the speaker, not currently held by the hearer. Evidence connections between propositions often appeal to unstated information not currently in the hearer's set of beliefs, but recognizable as an intended support relation on the part of the speaker.</Paragraph> </Section> <Section position="3" start_page="0" end_page="4" type="metho"> <SectionTitle> 2 RESTRICTED PROCESSING STRATEGY </SectionTitle> <Paragraph position="0"> Consider the following framework for the model. An argument is considered to be a set of propositions. The model is then designed to analyze the argument a proposition at a time, incrementally building a representation for the underlying structure. The representation developed is a tree of claim and evidence relations comprising the argument, where a claim node is father to its evidence sons. In order to assign an interpretation for a given proposition, one must thus simply assign it a place in the tree. In this way, one can tell to which propositions it serves as evidence and from which other propositions it receives support.</Paragraph> <Paragraph position="1"> A key design decision is to separate the two main operations of determining for each proposition (i) where it fits with respect to the argument so far, and (ii) howit relates to some prior proposition. The question of how two propositions relate in this framework is one of verifying that an evidence relation holds between the propositions. This task is extracted and relegated to an evidence oracle, which, passed two propositions A and B, will respond &quot;yes&quot; or &quot;no&quot;, as to whether A is evidence for B.</Paragraph> <Paragraph position="2"> With the problem of evidence determination factored, the model must still cope with the question of where a proposition may fit. This is handled by characterizing possible coherent transmissions (ordering of propositions) on the part of the speaker and then limiting analysis to reception algorithms designed to recognize these coherent transmissions. In the section below we illustrate possible coherent strategies from the speaker, and present the associated reception algorithm required to recognize the input.</Paragraph> <Paragraph position="3"> Note that the computational model for the analysis of arguments is designed with certain aims and limitations.</Paragraph> <Paragraph position="4"> In particular, the model is to provide for analysis of &quot;spontaneous discourse&quot;, demanding construction of a representation for the argument as each new statement is processed. The following restriction to the processing model is thus applied: the processor does not weigh all possible interpretations for a proposition; if the oracle sends back a yes answer to the question Is P evidence for Q?, then P is attached as a son to Q in the tree. In other words, the model simulates a hearer who does not have the luxury of &quot;looking back&quot; and re-interpreting previous statements. Moreover, the model aims to provide an interpretation for the current proposition, so that once an interpretation has been found (e.g. that P supplies evidence for Q) that proposition has been processed. The evidence relation is also taken to be transitive - i.e., if P is evidence for Q and Q is evidence for R, then P is evidence for R. (See Section 4 for more detail on the evidence relation).</Paragraph> <Section position="1" start_page="0" end_page="4" type="sub_section"> <SectionTitle> 2.1 PRE-ORDER </SectionTitle> <Paragraph position="0"> Pre-order transmissions are those where the speaker consistently presents a claim and then states evidence.</Paragraph> <Paragraph position="1"> The example below illustrates this form: With the tree representation: Jones would make a good president He has lots of experience He's been on the city board 10 years And he's very honest He refused bribes while on the force</Paragraph> </Section> <Section position="2" start_page="4" end_page="4" type="sub_section"> <SectionTitle> 2.2 POST-ORDER </SectionTitle> <Paragraph position="0"> Another coherent strategy is post-order, where the speaker consistently presents evidence and then states the claim. Consider the comparable example below: Jones has been on the board 10 years He has lots of experience And he's refused bribes So he's honest He would really make a good president In this example, the claim is the same as in EX1, that Jones would make a good president, but the evidence precedes the claim in each case - i.e., 1 is evidence for 2; 3 is evidence for 4; and 2 and 4 together are evidence for the final claim 5.</Paragraph> </Section> </Section> <Section position="4" start_page="4" end_page="4" type="metho"> <SectionTitle> 12 Computational Linguistics, Volume 13, Numbers I-2, January-June 1987 </SectionTitle> <Paragraph position="0"> Robin Cohen Analyzing the Structure of Argumentative Discourse The associated reception algorithms for pre- and post-order are described in detail in Cohen (1981). Both can be shown to operate in linear time (linear in the number nodes of the tree) and so are quite efficient. The pre-order reception essentially finds an interpretation for the current proposition of an argument by searching for a father, up the right border of the tree. The post-order algorithm employs a stack; the current proposition tests to be father to the top of the stack; then all sons are popped off the stack, and the resulting tree pushed on the stack.</Paragraph> <Paragraph position="1"> We trace the construction of the trees of EX1 and EX2 below, to provide details of the pre-order and post-order reception algorithms.</Paragraph> </Section> <Section position="5" start_page="4" end_page="4" type="metho"> <SectionTitle> EXI: PRE-ORDER Algorithm: </SectionTitle> <Paragraph position="0"> - The current proposition is NEW.</Paragraph> <Paragraph position="1"> - The proposition immediately prior is LAST. 1) Try NEW as evidence for LAST.</Paragraph> <Paragraph position="2"> 2) If that fails, try NEW as evidence for each of LAST's ancestors, in turn, up to the root of the tree. 3) If the test in 1 succeeds, stop.</Paragraph> <Paragraph position="3"> Consider as well a dummy root (D) for the tree for which all nodes are evidence (to place the first proposition). evidence candidates oracle in order test for NEW response 1,D 2 ev. for 1? yes 2,1,D 3 ev. for 2? yes 3,2,1,D 4 ev. for 3? no 4 ev. for 2? no 4 ev. for 1? yes 4,1,D 5 ev. for 4? yes EX2: POST-ORDER Algorithm: - Keep a stack of elements eligible to be evidence for a current proposition, with the latest one as TOP. - To interpret the current proposition NEW: 1) Test if TOP is evidence for NEW 2) If yes, then pop TOP (TOP:= TOP - 1) and make it a son of NEW (build a tree under NEW); then repeat step 1 with NEW value of TOP.</Paragraph> <Paragraph position="4"> 3) If no, make the tree with NEW as root the new TOP of stack (push NEW onto stack). In essence, all sons for a proposition are picked up at once.</Paragraph> <Section position="1" start_page="4" end_page="4" type="sub_section"> <SectionTitle> 2.3 HYBRID </SectionTitle> <Paragraph position="0"> As a first approximation to a general processing strategy, consider designing a reception algorithm to accept hybrid pre- and post-order arguments (i.e., any given sub-argument may be transmitted in pre- or post-order). An example of hybrid transmission is EX3 below.</Paragraph> <Paragraph position="1"> EX3: Jones would make a good president He has lots of experience He's been on the board 10 years And he's refused bribes So he's honest</Paragraph> <Paragraph position="3"> Here, the first sub-argument is in pre-order, the second one in post-order, and the argument overall is still coherent. null The reception algorithm for accepting hybrid transmissions is basically a combination of techniques from the pre- and post-order receptions. Now, to process a current proposition both a father and possible sons must be searched for. But the search is still restricted - certain propositions get closed off as possible &quot;relatives&quot; to the current one (e.g., earlier brothers of an ancestor). Thus, the complexity of the algorithm is still reasonable; it can also be shown to be linear. Once more, see Cohen (1981) for further examples.</Paragraph> <Paragraph position="4"> A full description of the hybrid algorithm is included below.</Paragraph> <Paragraph position="5"> L is kept as a pointer to the lowest node of the tree still eligible to receive evidence. It is initially set to a dummy root node to which all other nodes succeed as evidence. Consider as well a labelling where the last proposition in the stream that succeeds as evidence for a node is stored as the rightmost son.</Paragraph> <Paragraph position="6"> For each node NEW in the input stream do: /* find father */ do while (NEW is not evidence for L and L is not dummy root) set L to father of L end; Computational Linguistics, Volume 13, Numbers 1-2, January-June 1987 13 Robin Cohen Analyzing the Structure of Argumentative Discourse /* see if there are any sons to re-attach */ if (rightmost son of L is not evidence for NEW ) /* no sons to re-attach */ then do; attach NEW to L set L to NEW end; else do; /* some son will re-attach; attach all sons of L which are evidence for NEW below NEW */ do while (rightmost son of L evidence for NEW) attach rightmost son of L to NEW remove rightmost son of L from L end; attach NEW to L end; Note that the hybrid algorithm, in searching for both sons and father to the current node, must contend with cases where a proposition is attached to a higher ancestor and must be re-attached to its immediate father. This occurs in a framework where the evidence relation is considered transitive. The kinds of orderings that involve this &quot;re-location&quot; are of the form: A, C, B - where C is evidence for B, B evidence for A (hence C also succeeds as evidence for A when tested).</Paragraph> <Paragraph position="7"> For EX3:</Paragraph> </Section> <Section position="2" start_page="4" end_page="4" type="sub_section"> <SectionTitle> 2.4 SUMMARY OF PROCESSING STRATEGY </SectionTitle> <Paragraph position="0"> The processing strategy proposed for our model of argument analysis is designed to produce a selective interpretation. The particular restrictions to processing chosen for the model are, moreover, both * useful for measuring the efficiency of the model, since expressed in a framework of an algorithm operating on a tree structure, where complexity of the algorithm can be studied and * well-motivated, since based on a characterization of coherent input.</Paragraph> <Paragraph position="1"> In fact, it is an expression of a theory of argument coherence that serves to produce a model that is both efficient and robust (can handle a wide variety of input).</Paragraph> <Paragraph position="2"> Note that the particular restrictions suggested are drawn from analysis of a body of examples from rhetoric texts, together with some &quot;naturally occurring&quot; arguments (letters to the editor in newspapers). The aim is to characterize coherent transmission forms (ordering of propositions) that can be understood, without additional &quot;clue words&quot; to the underlying structure. We feel that the hybrid model is a good first approximation because examples of various forms of hybrid were encountered, and exceptions to the form all involved additional clue information. This leaves the study of clues and possible extended transmission forms to the next section. (In Cohen (1983) a few longer examples are run through the model to illustrate the forms it can accept and to motivate provision for the recognition of a wide variety of argument forms. Note that an actual implementation was not produced. There is now a scaled-down first implementation (Smedley 1986), which will be discussed further in section 6.)</Paragraph> </Section> </Section> <Section position="6" start_page="4" end_page="4" type="metho"> <SectionTitle> 3 LINGUISTIC CLUES </SectionTitle> <Paragraph position="0"> The second main component of the argument understanding model is a theory of linguistic clues. These are the words and phrases often used by the speaker to directly indicate the structure of the argument to the hearer. It is important to specify * what kinds of clues exist * the function of these clues in analysis - i.e., what interpretation can be assigned to a proposition that contains a clue word, and * (a more difficult question) when clues are necessary to ensure comprehension of the argument structure by the hearer.</Paragraph> <Paragraph position="1"> This approach to the study of clue words is much more detailed than the initial suggestions of Hobbs (1976) on how to interpret a few connectives such as and in his framework. It is also distinct from the investigations of Reichman (1981) and (recently) Grosz and Sidner (1986). Grosz and Sidner acknowledge the existence of clues and discuss various discourse structures that can be formed in the presence of clues. Reichman also gives a longer list of clue words and the particular conversational moves they signal. But there is no systematic proposal for interpreting a clue word that may occur (classification), and there is little discussion of how to process some of these more complex discourse structures without clues (suggesting when clues are necessary). In this section we clarify more deeply some of the discussion of clues in Cohen (1983, 1984b).</Paragraph> <Section position="1" start_page="4" end_page="4" type="sub_section"> <SectionTitle> 3.1 CLUES OF RE-DIRECTION </SectionTitle> <Paragraph position="0"> One type. of clue is expressions that specifically re-direct the hearer to an earlier part of the argument. Consider: Here, the clue in proposition 5, returning to city problems signals additional support for proposition 1. In the absence of a clue, the specifications for the hybrid algorithm would have 5 test to be a son for 4, 2, and 1 (up the right border of the tree). So, adding clues should reduce the processing effort of the hearer. (In fact, if clues are consistently used by the speaker after long chains of evidence, the processing complexity of the reception algorithm can reduce from linear to real-time).</Paragraph> </Section> <Section position="2" start_page="4" end_page="4" type="sub_section"> <SectionTitle> 3.2 CONNECTIVES </SectionTitle> <Paragraph position="0"> Another popular type of clue word is the connective. We present a classification or taxonomy of connectives, and then associate a common interpretation rule within the claim and evidence framework for each category of the taxonomy. In this way, the interpretation of any proposition containing a connective can be determined on the basis of the clue word's classification. For example: EX5: 1) This city is a disaster area 2) Houses have been demolished 3) Trees have been uprooted 4) As a result, we need national aid With the representation: Here,the connective as a result in proposition 4 belongs to a category known as inference, indicating that there should be some prior proposition that connects to 4 and serves as son to 4 - i.e., supplies evidence for &quot;the result&quot;. In this example, in fact, 1 acts as the son. The interpretation rules are necessarily default suggestions for translating the semantic relations between propositions into our claim and evidence classification. (See further discussion on the evidence relation (section 4) for possible exceptions).</Paragraph> <Paragraph position="1"> The taxonomy and its associated interpretation rules are presented below.</Paragraph> <Paragraph position="2"> Each category is a classification of connectives, made on the basis of semantic meaning of the connective e.g., the parallel category would include all words that extended a list, including next, then, first, secondly, thirdly, etc. The set of classes was produced by considering the categories proposed in Quirk et al. (1972), and merging some classes that had similar semantics and suggested the same interpretation rule for claim and evidence. The inference category covers phrases that suggest one proposition can be inferred from another - e.g., as a result, because of this, etc. The detail category moves in the other direction, and includes connectives that specify further - e.g., in particular, specifically, etc. The summary category is used for phrases that conclude a list. Reformulation captures clue words that repeat an earlier idea e.g., in other words, once more, etc. Finally, contrast covers the phrases that introduce comparisons, like on the other hand or but.</Paragraph> <Paragraph position="3"> In conjunction with the semantic classes, evidence interpretation rules were then assigned as default interpretations. (See Section 4.3 for pragmatic &quot;overrides&quot; to the defaults). Note that some words may fall into more than one category, based on the meaning used - e.g., then meaning 'next' (in a list of actions) compared with then meaning 'as a result'. Clue interpretation thus requires a classification process as well.</Paragraph> <Paragraph position="4"> P is prior proposition; S is the proposition with the clue CATEGORY RELATION: P to S EXAMPLE parallel brother First, Second inference son As a result detail father In particular summary multiple sons In sum reformulation son (& father) In other words contrast brother or father On the other hand The taxonomy is described in detail in Cohen (1984b). We include discussion of one category here, as an example. The detail class is one case where connectives with a range of meanings were merged into one category. The title &quot;detail&quot; suggests that the connective will further specify some prior proposition. Included cases are: for example, in particular, and as another instance. The interpretation rule assigned to this category is that the proposition with the connective provides evidence for the earlier connecting statement. The motivation is that an accumulation of specific cases leads to a conclusion of a general statement (a form of reasoning used very often in naturally occurring arguments).</Paragraph> <Paragraph position="5"> EX6: 1) The people in this town deserve a city-wide holiday 2) For example, Old Man Jones has worked non-stop since Christmas 3) And Mayor Flood is still recovering from all his efforts for the tornado relief 4) In short, all of us are tired</Paragraph> </Section> <Section position="3" start_page="4" end_page="4" type="sub_section"> <SectionTitle> 3.3 THE FUNCTION OF CLUE WORDS </SectionTitle> <Paragraph position="0"> So far we have discussed two types of clue words that can occur in conjunction with arguments transmitted according to the specifications of the hybrid algorithm presented in section 2 (our characterization for coherent discourse). We point out that re-direction clues provide additional information concerning which of the eligible propositions is related to the current one, and that connective clues specify the kind of relation that must be found between the current proposition and one of the eligible priors.</Paragraph> <Paragraph position="1"> In certain cases these restrictions will prevent some of the tests for evidence that would Otherwise have been conducted, thus saving some processing effort. For example, consider the following: EX7: 1) The city is in serious trouble 2) There are some fires going 3) Three separate blazes have broken out 4) In addition, a tornado is passing through with the representation: The clue in 4 prescribes an interpretation for 4 as brother to some prior proposition. This means that 4 must act as evidence for some different proposition. Thus, even though 3 is technically the first proposition to test out when interpreting a new proposition (NEW evidence for LAST is the test), in this particular case this option is not possible. Thus, one round of work for the evidence oracle is avoided. In fact, for this example, the test &quot;4 evidence for 2&quot; fails, and the final test of &quot;4 evidence for 1&quot; succeeds. (Note that the brother relation is tested by way of son relation to a (common) father. This is because the model only processes evidence relationships).</Paragraph> </Section> </Section> <Section position="7" start_page="4" end_page="4" type="metho"> <SectionTitle> 3.3.1 THE NECESSITY FOR CLUES </SectionTitle> <Paragraph position="0"> We now examine the use of clue words in conjunction with transmissions that violate the specifications of the hybrid base case. The hypothesis is that more complex transmissions can be accommodated by the argument analysis model provided there exist clues to assist the hearer in recognition. In these cases, the clues are there by necessity. Their function in the discourse is not to merely add detail on the interpretation of the contained proposition, but to allow that proposition an interpretation that would otherwise be denied.</Paragraph> <Paragraph position="1"> There is an advantage to adhering to the base ease of acceptable argument structures and treating the use of clues with other transmission forms as exceptional. In the first place, this provides a framework for detecting one interpretation for an argument when another possible interpretation could be generated if further testing occurred. In other words, in this model a representation drawn using the rules of the hybrid reception will always be accepted as the intention of the speaker, unless clues specifically override possible tests.</Paragraph> <Paragraph position="2"> To explain, consider the following example: EX8: 1) The park benches are rotting 2) The parks are a mess 3) The highways are run down 4) (And another problem with the parks is that) the grass is dying 5) This city is in sad shape Without the clue phrase in 4, re-directing to proposition 2, to add more evidence out of turn, a coherent representation could be built just the same, as below: ,J If the speaker intends 4 to add detail to the parks problem, he cannot expect the hearer to make this connection without more information, simply because a more effortless interpretation of 4 is possible (and the speaker should realize that it is this representation that the hearer will draw).</Paragraph> <Paragraph position="3"> Another important reason for separating the base case of acceptable transmissions is to allow for input that can be characterized as somehow a coherently generated plan of the speaker. Recall that the proposition analyzer will continuously call on an evidence oracle to determine if some proposition A acts as evidence for some other proposition B. Suppose there were no restrictions in the testing of evidence relations. Then, tests for evidence that would be interpreted as positive would return this response, regardless of when asked. In other words, a totally random display of propositions would result in the same representation for the argument as the reception of a coherently ordered presentation. Consider the follow- null ing example: EX9: 1) Yogi's been a shrewd manager 2) He hired a hitter who now bats .400 3) He traded in a pitcher who is now 0 and 12 4) But he got involved in drug deals to the players 5) And that's inexcusable for a manager 6) He really needs to be axed 16 Computational Linguistics, Volume 13, Numbers 1-2, January-June 1987 Robin Cohen Analyzing the Structure of Argumentative Discourse with the representation: 4 j 2 j ~3 (where 5 and 1 are contrasting evidence to 6).</Paragraph> <Paragraph position="4"> EX9B: The argument as above, presented in the order:: 1) Yogi hired a .400 hitter 2) (And) that's inexcusable for a manager 3) He traded in a pitcher who is now 0 and 12 4) Yogi really needs to be axed 5) He got involved in drug deals to the players 6) He is a shrewd manager This argument now appears incoherent. One reason is that there is contrast overall that must be clearly separated. In any case, the ordering does not conform to the specifications of the hybrid and as such is a candidate for an unacceptable t/'ansmission.</Paragraph> <Paragraph position="5"> This example suggests that the use of clue words, though helpful to signal exceptional transmissions, should still be studied as a systematic process of the speaker to assist the hearer in comprehension. In EX9B, could any number of clue words be added to still make this recognizable? Consider the following attempted repair to EX9B: EX9C: 1) Yogi hired a .400 hitter 2) But he's done some things inexcusable for a manager null 3) Although he also did other smart things like trading a pitcher who is now 0 and 12 4) No, Yogi really has to be axed 5) He got involved in drug deals to the players 6) Though he still is a shrewd manager The question is whether an argument of this form would still be judged coherent. Our preference will be to specify particular types of exceptional transmissions that may be judged coherent. To this end, we have tried to isolate a few specific cases where clues can be used in conjunction with coherent orderings beyond the specifications of the hybrid algorithm. These are highlighted in the next sub-section below.</Paragraph> <Paragraph position="6"> One more point about the last example above is that it emphasizes our concern that the analysis of arguments be done efficiently. In this case, we want to avoid making tests for evidence relations that could not exist between certain propositions as part of a coherent input. In other words, we don't want the model to simply test out all possible combinations of evidence relations (even though this would only be n*n vs. k*n number of operations), because an input that is not coherent would then be accepted.</Paragraph> <Paragraph position="7"> We would also not want to derive computationally a representation for an argument that involved more computational effort, if a simpler interpretation of the same argument could be derived. (Again, the speaker is to assume that the hearer will not be searching unnecessarily). This is illustrated in EX8 above.</Paragraph> </Section> <Section position="8" start_page="4" end_page="4" type="metho"> <SectionTitle> 3.3.2 CASES OF NECESSITY </SectionTitle> <Paragraph position="0"> Three kinds of acceptable extended transmission strategies are studied in Cohen (1983): parallel evidence, multiple evidence (a proposition acting as evidence for several claims, in restricted conditions) and mixed-mode sub-arguments (with evidence both preceding and following a claim). We present an example of the parallel construction below. See Cohen (1984b, 1983) for further examples.</Paragraph> <Paragraph position="1"> Here, the argument breaks the rules of hybrid transmission by adding evidence for an earlier brother (proposition 2); however, this shift is signalled with a phrase of intention in proposition 5, and the hearer may then expect a parallel expansion of additional support for each of the earlier brothers, in turn. (Note that without the clue, the argument structure derived would simply record all of 2 to 6 as evidence for 1, in the same vein as EX8). This example illustrates another interesting feature of clues - the variety of possible surface forms that can signal the same evidence relation between propositions.</Paragraph> <Paragraph position="2"> In EX10, the clue in 5 could also have been The problem with the parks is... or I will now explain why the parks are such a problem - .... A range of explicitness is thus possible. In cases other than this parallel construction, in fact, a signal to a relation between propositions may be advocated by the simple use of an anaphor. For example: EXI 1: 1) The mayor hasn't'done much for this city 2) He doesn't seem to want to do much 3) That man is a complete loser Here, the phrase that man signals a link to the mayor. It is difficult to decide whether the phrase qualifies as a clue word. The problem is determining a &quot;bottom line&quot; - i.e., &quot;can't every sentence be seen to have some clue, from semantics, to its interpretation within the argument?&quot;. For now, we do not consider the cases of anaphora as above.</Paragraph> <Paragraph position="3"> Computational Linguistics, Volume 13, Numbers 1-2, January-June 1987 17</Paragraph> <Section position="1" start_page="4" end_page="4" type="sub_section"> <SectionTitle> Robin Cohen Analyzing the Structure of Argumentative Discourse 3.4 SUMMARY AND FUTURE DIRECTIONS OF CLUE THEORY </SectionTitle> <Paragraph position="0"> Our theory of clue interpretation so far has outlined the following principles: * Clues may occur with expected coherent transmissions or to signal exceptional cases.</Paragraph> <Paragraph position="1"> * Connective clues can be assigned common interpretation rules according to the semantics of the clue. * To distinguish helpful versus necessary clues, the preference will always be to recognize the hybrid transmission; if clue rules or semantics force an exceptional reading, only certain exceptional structures should be accepted.</Paragraph> <Paragraph position="2"> * In all cases, a reading that can be derived with less computational effort will always be taken as the intended reading.</Paragraph> <Paragraph position="3"> * The cases where clues are necessary to force a certain interpretation provide insight into the function of clues; their use in conjunction with acceptable transmissions suggests a function of additional processing reductions. In order to recognize clues and incorporate their interpretation into a larger model that derives argument structure, we propose a separate clue interpretation module, interacting with the basic reception algorithm and the calls to the evidence oracle. Exactly how this module would function is left as future work. We do have a few initial insights, to suggest that clue interpretation is not only quite useful (argued previously in this section) but feasible.</Paragraph> <Paragraph position="4"> For connectives, the clue can be recognized from a classification. Then, determining whether a related proposition available from the list of eligibles is in fact related can be tested, according to the restrictions of the interpretation rule (as suggested in EX7). Further, the required semantic relation to the prior proposition can be passed as additional information to the oracle. The problem is that this oracle must do some kind of search for connections between facts and axioms of a knowledge base. How this semantic analysis is achieved depends on the underlying representation, but additional semantic constraints should help to restrict operations.</Paragraph> <Paragraph position="5"> For re-direction clues, a processor would first have to recognize the appropriate phrase used. Some standard list (e.g., returning to, on the topic of) may be specified as a start. Then, the phrase should suggest some particular semantic content to the prior proposition (e.g., returning to the parks mentions parks as central). Now it is the form of representation of the propositions which may influence what is acceptable on a list of eligibles. If this semantic processing can be done very broadly, some calls to the oracle may be avoided, and this would be an improvement, assuming the oracle's operations encompass a more extensive search.</Paragraph> </Section> </Section> <Section position="9" start_page="4" end_page="4" type="metho"> <SectionTitle> 4 EVIDENCE DETERMINATION </SectionTitle> <Paragraph position="0"> The third main component of the argument analysis model is a theory of evidence, to govern the verification of evidence relations between propositions.</Paragraph> <Paragraph position="1"> An initial definition for evidence offered in Section 1 is: &quot;A proposition P is evidence for a proposition Q if there is some logical connection from P to Q - i.e., some rule of inference such that P is premise to Q's conclusion&quot;. The main problem in establishing evidence relations is that not all the premises are stated. For example, one common rule of inference used in arguments is Modus Ponens, of the form: P ~ Q, P therefore Q. The way this rule is most often used, the speaker will simply state P and Q and leave out the major premise &quot;P ~ Q&quot;, expecting the hearer to be able to fill in the unstated connection to recognize the evidence relation from P to Q. Omitting certain premises is referred to as Modus Brevis and studied in Sadock (1977).</Paragraph> <Paragraph position="2"> We list below the rule of inference frames included for our model. Each rule has a slot for major premise, minor premise, and conclusion, to be filled by stated or unstated propositions, in recognizing an evidence relationship.</Paragraph> <Paragraph position="3"> The form most often used is Modus Ponens. When the major premise is missing, this is the rule of inference to consider as the intended link from P to Q.</Paragraph> <Paragraph position="4"> EXI2: The Jays had a fantastic team this year All their players had averages over .250 then that team is fantastic The common form for arguments, then, is one where the hearer must supply missing statements in order to establish the connections for the representation of the argument. null There are several possible Modus Brevis forms for each frame above. The possible missing parts are classified below for the case of Modus Ponens.</Paragraph> </Section> <Section position="10" start_page="4" end_page="4" type="metho"> <SectionTitle> 4.1. OVERVIEW OF ORACLE'S PROCESSING </SectionTitle> <Paragraph position="0"> It is important to demonstrate that the part of processing relegated to the evidence oracle within the overall model is not insurmountable, to defend the model as useful. In this section we examine more closely the operation of the</Paragraph> </Section> <Section position="11" start_page="4" end_page="4" type="metho"> <SectionTitle> 18 Computational Linguistics, Volume 13, Numbers 1-2, January-June 1987 </SectionTitle> <Paragraph position="0"> Robin Cohen Analyzing the Structure of Argumentative Discourse oracle, opening up the &quot;black box&quot; just enough to suggest how it operates.</Paragraph> <Paragraph position="1"> In general, the oracle is asked to test two propositions to be in an evidence relationship, responding &quot;yes&quot; or &quot;no&quot; to a question of the form: &quot;is P evidence for Q?&quot;. We sketch the operation of the oracle for the example D:is-dangerous). (Note that we are not addressing certain questions here such as the significance that is a shark is definitional, while is dangerous is really assertional). null Recall that the argument analysis model seeks to recognize intended argument structures. So, in this example the hearer can at least recognize that &quot;P is evidence for Q&quot; would follow through if All sharks are dangerous were believed (i.e., for-all(x) (S(x) ~ D(x) ).</Paragraph> <Paragraph position="2"> It is thus proposed that the oracle: 1. identify missing premises (the Modus Brevis form of the argument being presented); 2. verify plausibility of these missing premises (that they could be intended by the speaker to be believed by the hearer); and 3. conclude that an evidence relation holds if the miss null ing premises are plausible.</Paragraph> <Paragraph position="3"> For step 2, we try to specify more precisely in the remainder of this section the kind of tests the hearer can apply. A summary is provided below: a) Identify the missing premise within a knowledge base of shared knowledge.</Paragraph> <Paragraph position="4"> b) Identify a &quot;relaxed version&quot; of the missing premise within own private knowledge.</Paragraph> <Paragraph position="5"> c) Identify the missing premise within a model of the speaker's beliefs.</Paragraph> <Paragraph position="6"> d) Judge the beliefs of a hypothetical third party (which could be simplified if the bottom line is &quot;believe it, unless there's reason to strongly doubt, from within one's own beliefs&quot;).</Paragraph> <Paragraph position="7"> We have found in simulations of the model on a variety of examples that most of the tests for evidence can be answered through (a) and (b). We hypothesize that, given a specification of a knowledge base, the search for connections between propositions can be controlled. This hypothesis would be best verified with an implementation of the oracle and extensive analysis of examples, and could be the focus of the next stage of our implementation (beyond Smedley (1986)). (The two large examples dissected in Cohen (1983) do have this property.) In addition to the problem that the major premise may be unstated is the problem that this premise should really be tempered by the beliefs of the speaker. In other words, the missing major premise that the hearer must fill in is really of the form: H believes that S wants H to believe (P ~ Q). In other words, this premise is not necessarily one of the hearer's beliefs. It is important to emphasize the importance of pragmatic processing in establishing evidence relations. The tree of claim and evidence relations built as a representation for the argument is really an indication of the plan of the speaker, in the sense that each evidence relation recorded is the bearer's conception of a support connection intended by the speaker.</Paragraph> <Paragraph position="8"> Note that it is difficult to specify how a plan of a speaker is determined during analysis. What we are advocating is to interpret the intention of each proposition of the argument, the other propositions for which it provides evidence. The result of processing the entire discourse is not a complete plan of the speaker, in the sense that each of the &quot;steps&quot; could be executed and the top level goal (convince the hearer of some overall point) would then follow. It is more an indication of the motivation behind each utterance towards the ultimate goal of convincing the hearer. The difficulties in plan inference for discourse are discussed in more detail in Grosz and Sidner (1986), and are in fact a topic of our current concern (see discussion of future work in Section 6).</Paragraph> <Paragraph position="9"> There is in fact a whole spectrum of problems the hearer must face in recognizing evidence relationships between propositions. The four main tests for the hearer can be described as:</Paragraph> <Section position="1" start_page="4" end_page="4" type="sub_section"> <SectionTitle> 4.2 LOGIC AND RELAXED LOGIC </SectionTitle> <Paragraph position="0"> In example EX14, all the premises of the Modus Ponens argument are present. The hearer should realize that 1 and 2 are evidence for the claim in 3. Then EXI5 illustrates the more typical case of missing major premise. If the hearer fills in All machine candidates win, the connection from 1 to 2 can be seen. The problem is that the speaker probably believes something more along the lines of: Most machine candidates win. And yet, one couldn't record a Modus Ponens relation in the argument with a quantifier such as most instead of for all. Thus, the hearer must use some relaxation to the rules of logic to recognize the evidence relation. The detection of evidence through &quot;relaxed logic&quot; can be accomplished by having the hearer judge the unstated connection as a plausible generalization, based on a few known cases.</Paragraph> <Paragraph position="1"> For example, if the hearer tries to recognize an evidence relation from 1 to 2 in EX16 below, by filling in All Computational Linguistics, Volume 13, Numbersl-2, January-June 1987 19 Robin Cohen Analyzing the Structure of Argumenlative Discourse sharks are dangerous, and the hearer doesn't believe this &quot;axiom&quot; but knows of a few sharks that are dangerous, he may reason that the missing major premise is reasonable. null The point is that the hearer is still able to recognize connections he does not believe. In EXI7, the hearer should be able to understand an evidence relation from 1 to 2, upon filling in a missing premise of the form &quot;If a person stands for apple pie and Morn then he is great&quot;. If the hearer does not believe this statement himself, he may still consider it to be a reasonable belief of the speaker; having stereotyped knowledge of the speaker's views may thus be use'ful.</Paragraph> <Paragraph position="2"> The idea of recognizing an intended connection from some other conversant based on one's own beliefs is not necessarily simple to implement. There has been some recent work by Pollack (1986) that suggests a more concrete foundation for this operation. Pollack discusses the problem of inferring a questioner Q's plan from his discourse. A first process has the responder R ascribing to Q a belief about some connection (&quot;conditional generation relation&quot;) that she herself believes true. Occasionally, R will need to recognize a connection that is not one of her beliefs. Then Pollack suggests there is a rule where &quot;R ascribes to Q a belief about a relation between act-types that is a slight variation of one she herself has&quot;. In particular, one slight difference possible has Q believing a stronger conditional generation relation, which is missing one of the required conditions.</Paragraph> <Paragraph position="3"> This related research is quoted here, not to argue that this problem is solved, but to acknowledge that it is important to specify this &quot;relaxed&quot; connection between one's own beliefs and those attributed to another. Some groundwork is being laid with more formal descriptions of plans such as Pollack's.</Paragraph> </Section> <Section position="2" start_page="4" end_page="4" type="sub_section"> <SectionTitle> 4.3 DIFFERENCE IN BELIEFS </SectionTitle> <Paragraph position="0"> The other type of problem faced by a hearer in recognizing evidence relations arises because of difference in beliefs between speaker and hearer. As mentioned, the hearer is actually discerning intended relations on the part of the speaker, and must often reason outside his personal framework of knowledge. Again, an issue is raised of how to discern intentions from discourse. Some work has been done at the level of one utterance (e.g., Allen 1979). We are mostly concerned with advocating the inclusion of reasoning beyond one's own beliefs, without a full theory of how to infer another person's beliefs. Instead, we advocate a simplified framework, discerning evidence relations and allowing a connection to be drawn as intended if it is plausible to the hearer.</Paragraph> <Paragraph position="1"> For future work, we are studying how to specify this process more precisely. (See also Grosz and Sidner 1986).</Paragraph> <Paragraph position="2"> Finally, if the hearer is testing a possible evidence relation between two propositions, does not believe the missing premise, and has no prior knowledge of the speaker, the best option available is to try to judge the plausibility of the unstated information. Essentially, the hearer must postulate new facts (which he may not be sure he wants to also believe) and consider relationships between these facts as plausible or not. It is in this sense that he adopts a &quot;hypothetical person's&quot; beliefs. Note that it is often the case that one will accept new facts unless something from one's own beliefs suggests a contradiction. In this sense, the judgement of plausibility does relate back to the hearer's own beliefs.</Paragraph> <Paragraph position="3"> An example with an implausible missing connection is EX18 below. If the hearer tests 2 as possible evidence for 1, a major premise of the form &quot;All sharks like tap dancing&quot; would establish the relation. But the hearer would not regard this as a plausible belief of the speaker, and so would fail to recognize an evidence relation between the two propositions.</Paragraph> <Paragraph position="4"> EXId: 1) Joey likes to tap dance 2) He is a shark The problem of judging plausibility is difficult. To make this process more computationally tractable, one suggestion is to incorporate into the model some tracking of mutual belief between speaker and hearer. (See Cohen (1978) for further discussion on the use of mutual belief in natural language processing.) Then, certain tests for evidence relations can be blocked in the oracle, based on mutual belief.</Paragraph> <Paragraph position="5"> For instance, rules can be postulated regarding the use of claims and evidence that are mutually believed. Two sample rules are: (i) &quot;If P is mutually believed, it can't be used as claim&quot;.</Paragraph> <Paragraph position="6"> (ii) &quot;If ~P is mutually believed, P can't be used as evidence&quot;.</Paragraph> <Paragraph position="7"> In addition, some of the default interpretation rules associated with the taxonomy of linguistic clues can be overruled by pragmatic considerations. The idea is to possibly override the default semantic interpretations of evidence relations otherwise specified, by testing whether</Paragraph> </Section> <Section position="3" start_page="4" end_page="4" type="sub_section"> <SectionTitle> 20 Computational Linguistics, Volume 13, Numbers 1-2, January-June ! 987 Robin Cohcn Analyzing ihc Siructurc of Argumcntalivc Discourse </SectionTitle> <Paragraph position="0"> propositions are already mutually believed. Note that the idea of a &quot;pragmatic override&quot; is also employed in Gazdar (1979) for the problem of determining presuppositions. The importance of pragmatic processing for argument analysis is once more emphasized, as it is a critical component to the difficult procedure of judging plausibility. null While considering mutual belief will help to eliminate some potentially difficult tests for the oracle, the model would require a more detailed specification of the maintenance and use of mutual belief. This is left as a topic for future work. Some current ideas are explored in more detail in Cohen (1985).</Paragraph> </Section> <Section position="4" start_page="4" end_page="4" type="sub_section"> <SectionTitle> 4.4 SUMMARY OF EVIDENCE THEORY </SectionTitle> <Paragraph position="0"> The &quot;theory&quot; of evidence relationships, developed to specify the operation of the evidence oracle component of the argument analysis model, really presents insight into the problems relevant to evidence relations, rather than offering solutions. Still, the fundamental question of how connections between propositions can be verified has not been studied to any extent by other researchers.</Paragraph> <Paragraph position="1"> It is extremely worthwhile to acknowledge that it is not sufficient to indicate what relations do occur, without also suggesting how these relations could be established during analysis.</Paragraph> <Paragraph position="2"> In addition, we have provided some insight into the more general question of how to accommodate a possible difference in beliefs between conversants in a natural language dialogue processing system. We also suggest that a less sophisticated oracle can be constructed that merely searches known facts and axioms, possibly including relaxations, to handle a large amount of naturally occurring arguments.</Paragraph> </Section> </Section> <Section position="13" start_page="4" end_page="4" type="metho"> <SectionTitle> 5.2 REFERENCE RESOLUTION AND FOCUS </SectionTitle> <Paragraph position="0"> Some of the work on using focus for reference resolution contains similarities to the model presented here for analyzing argument structure. Sidner (1979) maintains a focus stack of possible items in focus and an alternate focus list to support shifts of focus. The candidates for resolving references are thus restricted and ordered. The point is that these restrictions are drawn from a characterization of coherent discourse, the same approach taken for our control of processing. Grosz (1977) presents a model of focus spaces which may be used for several purposes, including the resolution of definite noun phrase resolution. The spaces are organized into a hierarchy, thus similar to our tree representation for argument structure. Both active and 9Pen spaces are tracked, similar to our tracking candidates eligible to be relatives to the current proposition.</Paragraph> <Paragraph position="1"> Because of similarities in the representations and techniques for controlling search for interpretations, it is worth investigating as future work the precise relationship among coherence, reference resolution and focus determination for dialogues (some of this is being done (Grosz and Sidner 1986)).</Paragraph> <Section position="1" start_page="4" end_page="4" type="sub_section"> <SectionTitle> 5.3 PSYCHOLOGICAL RESEARCH </SectionTitle> <Paragraph position="0"> Although our model is not designed according to psychological studies of discourse comprehension, there are some interesting parallels with existing psychological research. Labov and Fanshel (1977) investigate therapeutic discourse, dialogue between a psychologist and his patient. The research describes several properties of the arguments advanced by the patients including: the use of poor logic, the tendency to omit premises in arguments, a variety of transmission forms (claims before and after), and the existence of statements about the structure of the argument (clues). Since our characterization of input provides for all these forms, it strengthens our case for having a robust model.</Paragraph> <Paragraph position="1"> Geva (1981) investigates the usefulness of flowcharting text structure to assist students in comprehending the underlying structure. The top-down influence of building and using a representation is mentioned as important.</Paragraph> <Paragraph position="2"> The fact that many texts do not follow a strict linear ordering of connections between statements again confirms our concern with a variety of possible coherent transmissions.</Paragraph> <Paragraph position="3"> In brief, discovering psychological experiments that agree with the constraints of our model serve to defend its design. Further, some suggestions we make about computational measures of discourse processing may serve to inspire new experiments into the nature of human processing. So, the relationship with psychology should be a two-way exchange, and suggests future work.</Paragraph> </Section> </Section> class="xml-element"></Paper>