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<?xml version="1.0" standalone="yes"?> <Paper uid="P06-3001"> <Title>Sydney, July 2006. c(c)2006 Association for Computational Linguistics A Flexible Approach to Natural Language Generation for Disabled Children</Title> <Section position="4" start_page="1" end_page="2" type="metho"> <SectionTitle> 2 The Proposed Approach </SectionTitle> <Paragraph position="0"> The present system is intended to be used by children with severe speech and motorimpairment. It will cater those children who can understand different parts of a sentence (like subject, object, verb etc.) but do not have the competence to construct a grammatically correct sentence by properly arranging words. The intended audience offers both advantages and challenges to our NLG technique. The advantage is we can limit the extent of sentence types that have to be generated. But the challenges overwhelm this advantage. The main challenges identified so far can be summarized as follows.</Paragraph> <Paragraph position="1"> head2right Simplicity in interacting with user due to limited mental maturity level of users head2right Flexibility in taking input head2right Generating sentences with minimum number of keystrokes due to the limited physical ability of the users head2right Generating the most appropriate sentence in the first chance since we do not have any scope to provide users a set of sentences and ask them to choose one from the set.</Paragraph> <Paragraph position="2"> In the next few sections the NLG technique adopted in our system will be discussed in details. Due to limited vocabulary and education level of our intended users, our NLG technique will generate only simple active voice sentences. The challenges are also tried to be addressed in developing the NLG technique.</Paragraph> <Paragraph position="3"> Generally an NLG system can be divided into three modules viz. Text Planning, MicroPlanning and Realization. In (Callaway and Lester, 1995), the first two modules are squeezed into a planning module and results only two subtasks in an NLG system. Generally in all the approaches of NLG, the process starts with different parts of a sentence and each of these parts can be designated as a template. After getting values for these templates the templates are arranged in a specified order to form an intermediate representation of a sentence. Finally the intermediate representation undergoes through a process viz. Surface realization to form a grammatically correct and fluent sentence. Thus any NLG technique can be broadly divided into two parts head2right Templates fill up head2right Surface realization Now each of these two steps for our system will be discussed in details.</Paragraph> <Section position="1" start_page="1" end_page="2" type="sub_section"> <SectionTitle> 2.1 Templates fill up </SectionTitle> <Paragraph position="0"> We defined templates for our system based on thematic roles and Parts of Speech of words. We tagged each sentence of our corpus (the corpus is discussed in section 4.1) and based on this tagged corpus, we have classified the templates in two classes. One class contains the high frequency templates i.e. templates that are contained in most of the sentences. Examples of this class of templates include subject, verb, object etc. The other class contains rest of the templates. Let us consider the first class of templates are designated by set A={a1,a2,a3,a4....} and other class is set B={b1,b2,b3,b4,..............}.</Paragraph> <Paragraph position="1"> Our intention is to offer simplicity and flexibility to user during filling up the templates. So each template is associated with an easy to understand To achieve the flexibility, we show all the templates in set A to user in the first screen (the screenshot is given in fig. 1, however the screen will not look as clumsy as it is shown because some of the options remain hidden by default and appear only on users' request). The user is free to choose any template from set A to start sentence construction and is also free to choose any sequence during filling up values for set A. The system will be a free order natural language generator i.e. user can give input to the system using any order; the system will not impose any particular order on the user (as imposed by the Sanyog Project). Now if the user is to search for all the templates needed for his/her sentence, then both the number of keystrokes and cognitive load on user will increase. So with each template of set A we defined a sequence of templates taking templates from both set A and set B. Let user chooses template ak. Now after filling up template ak, user will be prompted with a sequence of templates like ak1, ak2, ak3, bk1, bk2, bk3, etc. to fill up. Again the actual sequence that will be prompted to user will depend on the input that is already given by user. So the final sequence shown to the user will be a subset of the predefined sequence. Let us clear the concept with an example. Say a user fills up the template <Destination>. Now s/he will be requested to give values for template like <Source>, <Conveyance>, <Time>, <Subject> etc, excluding those which are already filled up. As the example shows, the user needs not to search for all templates as well as s/he needs not to fill up a template more than once. This strategy gives sentence composition with minimum number of keystrokes in most of the cases.</Paragraph> </Section> <Section position="2" start_page="2" end_page="2" type="sub_section"> <SectionTitle> 2.2 Surface Realization </SectionTitle> <Paragraph position="0"> It consists of following steps Each of these steps is described next. The verb form will be modified according to the person and number of the subject and the tense choice given by the user.</Paragraph> <Paragraph position="1"> The sense will decide the type of the sentence i.e. whether it is affirmative, negative, interrogative or optative. For negative sense, appropriate negative word (e.g. No, not, do not) will be inserted before the verb. The relative position of the order of the subject and verb will be altered for optative and interrogative sentences.</Paragraph> <Paragraph position="2"> The mood choice changes the main verb of the sentence to special verbs like need, must etc. It tries to reflect the mood of the user during sentence composition.</Paragraph> <Paragraph position="3"> Finally the templates are grouped to constitute different phrases. These phrases are ordered according to the order of the input given by the user. This step is further elaborated in section 3.2.</Paragraph> </Section> </Section> <Section position="5" start_page="2" end_page="3" type="metho"> <SectionTitle> 3 A Case Study </SectionTitle> <Paragraph position="0"> In this section a procedural overview of the present system will be described. The automatic language generation mechanism of the present system uses the following steps Taking Input from Users The user has to give input to the system using the form shown in fig. 1. As shown in the form the user can select any property (like tense, mood or sense) or template at any order. The user can select tense, mood or sentence type by clicking on appropriate option button. The user can give input for the template by answering to the follow- null After selecting a thematic role, a second form will come as shown in Fig. 2. From the form shown at Fig 2, the user can select as many words as they want. Even if they want they can type a word (e.g. his /her own name). The punctuations and conjunction will automatically be Template fill-up After giving all the input the user asks the system to generate the sentence by clicking on &quot;generate sentence&quot; Button. The system is incorporated with several template organizations and a default</Paragraph> </Section> <Section position="6" start_page="3" end_page="4" type="metho"> <SectionTitle> TION WITH COAGENT </SectionTitle> <Paragraph position="0"> The system select one such template organization based on user input and generates the intermediate sentence representation.</Paragraph> <Paragraph position="1"> Verb modification according to tense The intermediate sentence is a simple present tense sentence. According to the user chosen tense, the verb of the intermediate sentence get modified at this step. If no verb is specified, appropriate auxiliary verb will be inserted.</Paragraph> <Section position="1" start_page="3" end_page="3" type="sub_section"> <SectionTitle> Changing Sentence Type </SectionTitle> <Paragraph position="0"> Up to now the sentence remain as an affirmative sentence. According to the user chosen sense the sentence gets modified in this step. E.g. For question, the verb comes in front, for negative sentence not, do not, did not or does not is inserted appropriately.</Paragraph> </Section> <Section position="2" start_page="3" end_page="3" type="sub_section"> <SectionTitle> Inserting Modal Verbs </SectionTitle> <Paragraph position="0"> Finally the users chosen modal verbs like must, can or need are inserted into the sentence. For some modal verbs (like can or need) the system also changes the form of the verb (like can or could).</Paragraph> </Section> <Section position="3" start_page="3" end_page="4" type="sub_section"> <SectionTitle> 3.1 Example of Sentence Generation using Our Approach </SectionTitle> <Paragraph position="0"> So the final output will be &quot;I am going to school with father&quot;. It is same as the user intended sentence. null Since no action is specified, the auxiliary verb will be selected as the main verb. Here the sub-ject is second person and tense is present simple, so the verb selected is 'are'.</Paragraph> <Paragraph position="1"> Step 4: Since the selected sentence type is 'Question', so the verb will come in front of the sentence. Again, since a Wh-word has been selected, it will come in front of the verb. A question mark will automatically be appended at the end of the sentence.</Paragraph> <Paragraph position="2"> Step 5: There is no change of the sentence due to step 5.</Paragraph> <Paragraph position="3"> So the final output will be &quot;How are you?&quot;</Paragraph> </Section> <Section position="4" start_page="4" end_page="4" type="sub_section"> <SectionTitle> 3.2 Phase ordering to reflect users' inten- </SectionTitle> <Paragraph position="0"> tion An important part of any NLG system is pragmatics that can be defined as the reference to the interlocutors and context in communication (Hovy, 1990). In (Hovy, 1990), a system viz. PAULINE has been described that is capable of generating different texts for the same communicative goals based on pragmatics. In PAULINE, the pragmatics has been represented by rhetorical goals. The rhetorical goals defined several situations that dictate all the phases like topic collection, topic organization and realization. Inspired from the example of PAULINE the present system has also tried to reflect users' intention during sentence realization. Here the problem is the limited amount of input for making any judicious judgment. The input to the system is only a sequence of words with correspondence to a series of questions. A common finding is that we uttered the most important concept in a sentence earlier than other parts of the sentence. So we have tried to get the users' intention from the order of input given by user based on the belief that the user will fill up the slots in order of their importance according to his/her mood at that time. We have associated a counter with each template. The counter value is taken from a global clock that is updated with each word selection by the user. Each sentence is divided into several phrases before realization. Now each phrase constitute of several templates. For example let S be a sentence. Now S can be divided into phrases like P1, P2, P3..... Again each phrase Pi can be divided into several templates like T1, T2, T3....Based on the counter value of each template, we have calculated the rank of each phrase as the minimum counter value of its constituent templates i.e.</Paragraph> <Paragraph position="1"> Rank(Pi)=Minimum(Counter(Tj)) for all j in Pi Now before sentence realization the phrases are ordered according to their rank. Each of these phrase orders produces a separate sentence. As for example let the communication goal is 'I go to school from home with my father'. If the input sequence is (my father -> I -> go -> school -> home), the generated sentence will be 'With my father I go from home to School'. Again if the input sequence is (school -> home -> I -> go -> my father), then the generated sentence will be 'From home to school I go with my father.' Thus for the same communicative goal, the system produces different sentences based on order of input given by user.</Paragraph> </Section> </Section> class="xml-element"></Paper>