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<Paper uid="P99-1031">
  <Title>Measuring Conformity to Discourse Routines in Decision-Making Interactions</Title>
  <Section position="1" start_page="0" end_page="241" type="abstr">
    <SectionTitle>
Abstract
</SectionTitle>
    <Paragraph position="0"> In an effort to develop measures of discourse level management strategies, this study examines a measure of the degree to which decision-making interactions consist of sequences of utterance functions that are linked in a decision-making routine. The measure is applied to 100 dyadic interactions elicited in both face-to-face and computer-mediated environments with systematic variation of task complexity and message-window size. Every utterance in the interactions is coded according to a system that identifies decision-makmg functions and other routine functions of utterances. Markov analyses of the coded utterances make it possible to measure the relative fi'equencies with which sequences of 2 and 3 utterances trace a path in a Markov model of the decision routine. These proportions suggest that interactions in all conditions adhere to the model, although we find greater conformity in the computer-mediated environments, which is probably due to increased processing and attmfional demands for greater efficiency, The results suggest that measures based on Markov analyses of coded interactions can provide useful measures for comparing discourse level properties, for correlating discourse features with other textual features, and for analyses of discourse management strategies.</Paragraph>
    <Paragraph position="1"> Introduction Increasingly, research in computational linguistics has contributed to knowledge about the organization and processing of human interaction through quantitative analyses of annotated texts and dialogues (e.g. Carletta et al., 1997; Cohen et al., 1990, Maier et al., 1997; Nakatani et al., 1995; Passonneau, 1996; Walker, 1996). This program of research presents opportunities to examine the relation between linguistic form and pragmatic functions using large corpora to test hypotheses and to detect covariance among discourse features. For example, Di Eugenio et al. (1997) demonstrate that utterances coded as acceptances were more likely to corefer to an item in a previous turn. Grosz and Hirschberg (1992) investigate intonational correlates of discourse structure. These researchers recognize that discourse-level structures and strategies influence syntactic and phonological encoding. The regularities observed can be exploited to resolve language processing problems such as ambiguity and coreference, to integrate high level planning with encoding and interpretation strategies, or to refine statistics-based systems.</Paragraph>
    <Paragraph position="2"> In order to identify and utilize discourse-based structures and strategies, researchers need methods of linking observable forms with discourse functions, and our focus on discourse management strategies has motivated similar goals. Condon &amp; (~ech (1996a,b) use annotated decision-making interactions to investigate properties of discourse routines and to examine the effects of communication features such as screen size on computer-mediated interactions (~ech &amp; Condon, 1997). In this paper we present a method for measuring the degree to which an  interaction conforms to a discourse routine, which not only allows more refined analyses of routine behavior, but also permits fine-grained comparison of discourses obtained under different conditions.</Paragraph>
    <Paragraph position="3"> In our research, discourse routines have emerged as a fundamental strategy for managing verbal interaction, resulting in the kind of behavior that researchers label adjacencypaJrs such as question/answer or request/compliance as well as more complex sequences of functions. Discourse routines occur when a particular act or function is routinely continued by another, and as &amp;quot;predictable defaults,&amp;quot; routine continuations maximize efficiency by requiring minimal encoding while receiving highest priority among possible interpretations.</Paragraph>
    <Paragraph position="4"> Moreover, discourse routines can be exploited by failing to conform to routine expectations (Schegloff, 1986). Consequently, interactions will not necessarily conform to routines at every opportunity, which raises the problem of measuring the extent to which they do conform Condon et al. (1997) develop a measure based on Markov analyses of coded interactions, * and the measure is employed here with a larger corpus in which students engage in a more complex decision-making task. These measures provide evidence for the claim that participants in computer-mediated decision-making interactions rely on a simple decision routine more than participants in face-to-face decision-making interactions. The measures suggest that conformity to the routine is not strongly affected by any of the other variables examined in the study (task complexity, screen size), even though some participants in the computer-mediated conditions of the more complex task adopted turn management strategies that would be untenable in face-to-face interaction.</Paragraph>
    <Section position="1" start_page="238" end_page="239" type="sub_section">
      <SectionTitle>
Data Collection
</SectionTitle>
      <Paragraph position="0"> The initial corpus of 32 interactions involving simple decision-making tasks was obtained under conditions which were similar, but not identical, to the conditions under which the 68 interactions involving a more complex task were obtained. One obvious difference is that participants in the first study completed 2 simple tasks planning a social event (a getaway weekend, a barbecue), while participants in the second study completed a single, more complex task: planning a televised ceremony to present the MTV music video awards.</Paragraph>
      <Paragraph position="1"> Furthermore, all interactions in the first study were mixed sex pairs, whereas interactions in the MTV study include mixed and same sex pairs. All participants were native English speakers at the University of Southwestern Louisiana who received credit in Introductory Psychology classes for their participation.</Paragraph>
      <Paragraph position="2"> In both studies, the dyads who interacted face-to-face sat together at a table with a tape recorder, while the pairs who interacted electronically were seated at microcomputers in separate rooms. The latter communicated by typing messages which appeared on the sender's monitor as they were typed, but did not appear on the receiver's monitor until the sender pressed a SEND key. The soft-ware incorporated this feature to provide well-defined turns and to make it possible to capture and change messages in future studies.</Paragraph>
      <Paragraph position="3"> In addition, to minimize message permanence and more closely approximate face-to-face interaction, text. on the screen is always produced by only one participant at a time.</Paragraph>
      <Paragraph position="4"> In the original study, the message area was approximately 4 lines long, and it was not clear how much this factor influenced our results. Consequently, in the MTV study, the message area of the screen was either 4, 10, or 18 lines. Other differences in the computer-mediated conditions of the two studies include differences in the arrangement of information on the screen such as a brief description of the MTV problem which remained at the bottom of the screen. We also used an answer form in the first study, but not the second. More details about the communication systems in the two studies are provided Condon&amp; ~ech (1996a) and (~ech &amp; Condon (1998).</Paragraph>
    </Section>
    <Section position="2" start_page="239" end_page="240" type="sub_section">
      <SectionTitle>
Data Analysis
</SectionTitle>
      <Paragraph position="0"> Face-to-face interactions were transcribed from audio recordings into computer files using a set of conventions established in a training manual (Condon &amp; Cech, 1992). All interactions were divided into utterance units defined as single clauses with all complements and adjuncts, including sentential complements and subordinate clauses. Interjections like yeah, now, well, and ok were considered to be separate utterances due to the salience of their interactional, as opposed to propositional, content.</Paragraph>
      <Paragraph position="1"> The coding system includes categories for request routines and a decision routine involving 3 acts or functions (Condon, 1986, Condon &amp; (~ech, 1996a,b). We believe that the decision routine observed in the interactions instantiates a more general schema for decision-making that may be routinized in various ways. In the abstract schema, each decision has a goal; proposals to satisfy the goal must be provided, these proposals must be evaluated, and there must be conventions for determining, from the evaluations, whether the proposals are adopted as decisions. Routines make it possible to map from the general schema to sequences of routine utterance functions. Default principles associated with routines can determine the encoding of these routine functions in sequences of utterances.</Paragraph>
      <Paragraph position="2"> According to the model we are developing, a sequence of routine continuations is mapped into a sequence of adjacent utterances in one-to-one fashion by default. If the routine specifies that a routine continuation must be provided by a different speaker, as in adjacency pairs, then the default is for the different speaker to produce the routine continuation immediately after the first pair-part. Since these are defaults, we can expect that they may be weakened or overridden in specific circumstances. At the same time, if our reasoning is correct, we should be able to find evidence of routines operating in the manner we have described.</Paragraph>
      <Paragraph position="3"> (1) provides an excerpt from a computer-mediated interaction in which utterances are labeled to illustrate the routine sequence. P 1 and P2 designate first and second speaker (an utterance that is a continuation by the same speaker is not annotated for speaker).</Paragraph>
      <Paragraph position="4">  (1) a. P1: \[orientation\] who should win best Alternative video.</Paragraph>
      <Paragraph position="5"> b. P2: \[suggestion\] Pres. of the united states c. PI: \[agreement\] ok d. P2: \[orientation\] who else should nominate.</Paragraph>
      <Paragraph position="6"> e. \[suggestion\] bush. goo-goodolls oasis f. Pl: \[agreement\] sounds good, \[...1 we and (2) provides an annotated excerpt from a face-to-face interaction.</Paragraph>
      <Paragraph position="7"> (2) a. Pl: \[orientationl who's going to win? b. \[suggestion\] Mariah? c. P2: \[agreement\] yeahprobably d. PI: \[orientation\] alright Mariah winswhat song? e. P2: \[suggestion\] uh Fantasy or whatever? f. Pl: \[agreement\] that's it that's the same song I was thinking of g. \[orientation\] alright alternative? h. \[suggestion\] Alanis?  Coded as &amp;quot;Orients Suggestion,&amp;quot; orientations, like (la,2a) establish goals for each decision, while suggestions like (lb,e) and (2b, e,h) formulate proposals within these constraints. Agreements like (lc,f) and (2c,f), which are coded &amp;quot;Agrees with Suggestion,&amp;quot; and disagreements (&amp;quot;Disagrees with Suggestion&amp;quot;) evaluate a proposal and establish consensus. The routine does not specify that a suggestion which routinely continues an orientation must be produced by a different speaker: the suggestion may be elicited from a different speaker, as in (la,b) and (2d,e) or it may be provided by the same speaker, as in (ld,e) and (2a,b). However, an agreement that routinely continues a suggestion is produced by a different speaker, as (lb,c), (le,f), (2b,c) and (2e,f) attest.</Paragraph>
      <Paragraph position="8"> Other routine functions are also classified in the coding system. Utterances coded as &amp;quot;Requests Action&amp;quot; propose behaviors in the speech event such as (3).</Paragraph>
      <Paragraph position="9">  (3) a. well list your two down there (oral) b. ok, now we need to decide another band to perform (computer-mediated) c. Give some suggestions (computer-mediated) metalanguage, and orientations somewhat less reliable.</Paragraph>
    </Section>
    <Section position="3" start_page="240" end_page="241" type="sub_section">
      <SectionTitle>
Results
</SectionTitle>
      <Paragraph position="0"> were Utterances coded as &amp;quot;Requests Information&amp;quot; seek information not already provided in the discourse, as in (la,2a). Utterances that seek confirmation or verification of provided information, however, are coded as &amp;quot;Requests Validation.&amp;quot; The category &amp;quot;Elaborates-Repeats&amp;quot; serves as a catch-all for utterances with comprehensible content that do not function as requests or suggestions or as responses to these.</Paragraph>
      <Paragraph position="1"> Two categories are included to assess affective functions: &amp;quot;Requests/Offers Personal Information&amp;quot; for personal comments not required to complete the task and &amp;quot;Jokes Exaggerates&amp;quot; for utterances that inject humor. The category &amp;quot;Discourse Marker&amp;quot; is used for a limited set of forms: Ok, well, anyway, so, now, let's see, and alright. Another category, Metalanguage, was used to code utterances about the talk such as (3b,c).</Paragraph>
      <Paragraph position="2"> In the initial corpus, the categories described above are organized into 3 classes: MOVE, RESPONSE, and OTHER, and each utterance was assigned a function in each of these three groups of categories. In cases involving no clear function in a class, the utterance was assigned a No Clear code. A complete list of categories is presented at the bottom of Figure 1 and more complete descriptions can be found in Condon and Cech (1992). In the modified system used to code the MTV corpus, the criteria for classifying all of these categories remain the same.</Paragraph>
      <Paragraph position="3"> The data were coded by students who received course credit as research assistants.</Paragraph>
      <Paragraph position="4"> Coders were trained by coding and discussing excerpts from the data. Reliability tests were administered frequently during the coding process. Reliability scores were high (80-100% agreement with a standard) for frequently occurring move and response functions, discourse markers, and the two categories designed to identify affective functions. Scores for infrequent move and response functions, In the initial study, the 16 face-to-face interactions produced a corpus of 4141 utterances (ave. 259 per discourse), while the 16 computer-mediated interactions consisted of 918 utterances (ave. 57). In the MTV study, the 8 face-to-face interactions produced 3593 utterances (ave. 449), the 20 interactions in the 4-line condition included 2556 utterances (ave. 128), the 20 interactions in the 10-line condition produced 3041 utterances (ave. 152) and the 20 interactions in the 18-line condition included 2498 utterances (ave. 125). Clearly, completing the more complex MTV task required more talk.</Paragraph>
      <Paragraph position="5"> Figure 1 presents proportions of utterance functions averaged per interaction for each modality in the initial study. Analyses of variance that treated discourse (dyad) as the random variable were performed on the data within each of the three broad categories, excluding the No Clear MOVE/RESPONSE/ OTHER functions where inclusion would force levels of the between-discourse factor to the same value. We found no significant effect of problem t?/pe or order (for details see Condon &amp; Cech, 1996). However, the interaction of function type with discourse modality was significant at the .001-level for all three (MOVE, RESPONSE, OTHER) function classes. Tests of simple effects of modality type for each function indicated that only four proportions were identical in the two modalities: Requests Validation in the MOVE class, Disagrees in the RESPONSE class, and, in the OTHER class, Personal Information and Jokes-Exaggerates.</Paragraph>
      <Paragraph position="6"> Figure 2 presents the proportions of utterance functions for the MTV corpus using the same categories of functions as in Figure 1. The similarity of the results in the two figures is remarkable, especially considering differences in methods of data collection described above. First, it can be observed that</Paragraph>
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    </Section>
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