Planning multisentential English text using communicative acts

The goal of this research is to develop explanation presentation mechanisms for knowledge based systems which enable them to define domain terminology and concepts, narrate events, elucidate plans, processes, or propositions and argue to support a claim or advocate action. This requires the development of devices which select, structure, order and then linguistically realize explanation content as coherent and cohesive English text. With the goal of identifying generic explanation presentation strategies, a wide range of naturally occurring texts were analyzed with respect to their communicative sttucture, function, content and intended effects on the reader. This motivated an integrated theory of communicative acts which characterizes text at the level of rhetorical acts (e.g., describe, define, narrate), illocutionary acts (e.g., inform, request), and locutionary acts (e.g., ask, command). Taken as a whole, the identified communicative acts characterize the structure, content and intended effects of four types of text: description, narration, exposition, argument. These text types have distinct effects such as getting the reader to know about entities, to know about events, to understand plans, processes, or propositions, or to believe propositions or want to perform actions. In addition to identifying the communicative function and effect of text at multiple levels of abstraction, this dissertation details a tripartite theory of focus of attention (discourse focus, temporal focus, and spatial focus) which constrains the planning and linguistic realization of text. To test the integrated theory of communicative acts and tripartite theory of focus of attention, a text generation system TEXPLAN (Textual EXplanation PLANner) was implemented that plans and linguistically realizes multisentential and multiparagraph explanations from knowledge based systems. The communicative acts identified during text analysis were formalized as over sixty compositional and (in some cases) recursive plan operators in the library of a hierarchical planner. Discourse, temporal, and spatial focus models were implemented to track and use attentional information to guide the organization and realization of text. Because the plan operators distinguish between the communicative function (e.g., argue for a proposition) and the expected effect (e.g., the reader believes the proposition) of communicative acts, the system is able to construct a discourse model of the structure and function of its textual responses as well as a user model of the expected effects of its responses on the reader's knowledge, beliefs, and desires. The system uses both the discourse model and user model to guide subsequent utterances. To test its generality, the system was interfaced to a variety of domain applications including a neuropsychological diagnosis system, a mission planning system, and a knowledge based mission simulator. The system produces descriptions, narrations, expositions, and arguments from these applications, thus exhibiting a broader range of rhetorical coverage than previous text generation systems

A Robust and Efficient Three-Layered Dialogue Component for a Speech-to-Speech Translation System

We present the dialogue component of the speech-to-speech translation system VERBMOBIL. In contrast to conventional dialogue systems it mediates the dialogue while processing maximally 50% of the dialogue in depth. Special requirements like robustness and efficiency lead to a 3-layered hybrid architecture for the dialogue module, using statistics, an automaton and a planner. A dialogue memory is constructed incrementally.Comment: Postscript file, compressed and uuencoded, 15 pages, to appear in Proceedings of EACL-95, Dublin

Planning Text for Advisory Dialogues: Capturing Intentional and Rhetorical Information

To participate in a dialogue a system must be capable of reasoning about its own previous utterances. Follow-up questions must be interpreted in the context of the ongoing conversation, and the system's previous contributions form part of this context. Furthermore, if a system is to be able to clarify misunderstood explanations or to elaborate on prior explanations, it must understand what is has conveyed in prior explanations. Previous approaches to generating multisentential texts have relied solely on rhetorical structuring techniques. In this paper, we argue that, to handle explanation dialogues successfully, a discourse model must include information about the intended effect of individual parts of the text on the hearer, as well as how the parts relate to one another rhetorically. We present a text planner that records this information, and show how the resulting structure is used to respond appropriately to a follow-up question. 1 1 Introduction Explanation systems m..

Generation of anaphors in Chinese

The goal of this thesis is to investigate the computer generation of various kinds of anaphors in Chinese, including zero, pronominal and nominal anaphors, from the se¬ mantic representation of multisentential text. The work is divided into two steps: the first is to investigate linguistic behaviour of Chinese anaphora, and the other is to implement the result of the first part in a Chinese natural language generation system to see how it works.The first step is in general to construct a set of rules governing the use of all kinds of anaphors. To achieve this, we performed a sequence of experiments in a stepwise refined manner. In the experiments, we examined the occurrence of anaphors in humangenerated text and those generated by algorithms employing the rules, assuming the same semantic and discourse structures as the text. We started by distinguishing between the use of zero and other anaphors, termed non-zeroes. Then we performed experiments to distinguish between pronouns and nominal anaphors within the nonzeroes. Finally, we refined the previous result to consider different kinds of descriptions for nominal anaphors. In this research we confine ourselves to descriptive texts. Three sets of test data consisting of scientific questions and answers and an introduction to Chinese grammar were selected. The rules we obtained from the experiments make use of the following conditions: locality between anaphor and antecedent, syntactic constraints on zero anaphors, discourse segment structures, salience of objects and animacy of objects. The results show that the anaphors generated by using the rules we obtained are very close to those in the real texts.To carry out the second step, we built up a Chinese natural language generation system which is able to generate descriptive texts. The system is divided into a strategic and a tactical component. The strategic component arranges message contents in response to the input goal into a well-organised hierarchical discourse structure by using a text planner. The tactical component takes the hierarchical discourse structure as input and produces surface sentences with punctuation marks inserted appropriately. Within the tactical component, the first task consists of linearising in depth-first order the message units in the discourse structure and mapping them into syntactic-oriented representations. Referring expressions, the main concern in this thesis, are generated within the mapping process. A linguistic realisation program is then invoked to convert the syntactic representation into surface strings in Chinese.After the implementation, we sent some generated texts to a number of native speakers of Chinese and compared human-created results and computer-generated text to investigate the quality of the generated anaphors. The results of the comparison show that the rules we obtained are effective in dealing with the generation of anaphors in Chinese

Explainable expert systems: A research program in information processing

Our work in Explainable Expert Systems (EES) had two goals: to extend and enhance the range of explanations that expert systems can offer, and to ease their maintenance and evolution. As suggested in our proposal, these goals are complementary because they place similar demands on the underlying architecture of the expert system: they both require the knowledge contained in a system to be explicitly represented, in a high-level declarative language and in a modular fashion. With these two goals in mind, the Explainable Expert Systems (EES) framework was designed to remedy limitations to explainability and evolvability that stem from related fundamental flaws in the underlying architecture of current expert systems

Generating Natural Language Definitions from Classification Hierarchies

In interactions with users, knowledge based systems are often called upon to define their terms or concepts [Maybury, 1989]. These terms and concepts usually comprise classes within some classification scheme (e.g., a generalization hierarchy). Beyond simply retrieving the superclass of the to-be-defined class (e.g., "a mammal is a vertebrate") a more sophisticated definition also requires selection of distinguishing features or characteristics of this class (e.g., "a mammal is a vertebrate that gives live birth to and nurses its offspring"). To do this, we have refined and extended set theoretic, feature-based models of object similarity and proWtypica1ity, and developed an algorithm that selects the most distinguishing set of attributes and attribute-value pairs of a class in the context of a taxonomy of classes and their properties based on notions of prototypicality and discriminatory power. In this paper, we illustrate a classificatory representation using objects and attribute-value pairs in a test domain of vertebrates; describe our algorithm for computing prototypicality, discriminatory power, and distinctive power, based on this sample representation; and show how this algorithm is implemented to generate definitions of object classes in this representation

A classification scheme for annotating speech acts in a business email corpus

This paper reports on the process of manual annotation of speech acts in a corpus of business emails, in the context of the PROBE project (PRagmatics of Business English). The project aims to bring together corpus, computational, and theoretical linguistics by drawing on the insights made available by the annotated corpus. The corpus data sheds light on the linguistic and discourse structures of speech act use in business email communication. This enhanced linguistic description can be compared to theoretical linguistic representations of speech act categories to assess how well traditional distinctions relate to real-world, naturally occurring data. From a computational perspective, the annotated data is required for the development of an automated speech act tagging tool. Central to this research is the creation of a high quality, manually annotated speech act corpus, using an easily interpretable classification scheme. We discuss the scheme chosen for the project and the training guidelines given to the annotators, and describe the main challenges identified by the annotators

Natural Language Generation as an Intelligent Activity (Proposal for Dissertation Research)

In this proposal, I outline a generator conceived of as part of a general intelligent agent. The generator\u27s task is to provide the overall system with the ability to use communication in language to serve its purposes, rather than to simply encode information in language. This requires that generation be viewed as a kind of goal-directed action that is planned and executed in a dynamically changing environment. In addition, the generator must not be dependent on domain or problem-specific information but rather on a general knowledge base .that it shares with the overall system. These requirements have specific consequences for the design of the generator and the representation it uses. In particular, the text planner and the low-level linguistic component must be able to interact and negotiate over decisions that involve both high-level and low-level constraints. Also, the knowledge representation must allow for the varying perspective that an intelligent agent will have on the things it talks about; the generator must be able to appropriately vary how it describes things as the system\u27s perspective on them changes. The generator described here will demonstrate how these ideas work in practice and develop them further

A classification scheme for annotating speech acts in a business email corpus

This paper reports on the process of manual annotation of speech acts in a corpus of business emails, in the context of the PROBE project (PRagmatics of Business English). The project aims to bring together corpus, computational, and theoretical linguistics by drawing on the insights made available by the annotated corpus. The corpus data sheds light on the linguistic and discourse structures of speech act use in business email communication. This enhanced linguistic description can be compared to theoretical linguistic representations of speech act categories to assess how well traditional distinctions relate to real-world, naturally occurring data. From a computational perspective, the annotated data is required for the development of an automated speech act tagging tool. Central to this research is the creation of a high quality, manually annotated speech act corpus, using an easily interpretable classification scheme. We discuss the scheme chosen for the project and the training guidelines given to the annotators, and describe the main challenges identified by the annotators