57 research outputs found
Generating Coherent Messages in Real-time Decision Support: Exploiting Discourse Theory for Discourse Practice
This paper presents a message planner, TraumaGEN, that draws on rhetorical
structure and discourse theory to address the problem of producing integrated
messages from individual critiques, each of which is designed to achieve its
own communicative goal. TraumaGEN takes into account the purpose of the
messages, the situation in which the messages will be received, and the social
role of the system.Comment: 6 page
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
Knowledge Acquisition for Content Selection
An important part of building a natural-language generation (NLG) system is
knowledge acquisition, that is deciding on the specific schemas, plans, grammar
rules, and so forth that should be used in the NLG system. We discuss some
experiments we have performed with KA for content-selection rules, in the
context of building an NLG system which generates health-related material.
These experiments suggest that it is useful to supplement corpus analysis with
KA techniques developed for building expert systems, such as structured group
discussions and think-aloud protocols. They also raise the point that KA issues
may influence architectural design issues, in particular the decision on
whether a planning approach is used for content selection. We suspect that in
some cases, KA may be easier if other constructive expert-system techniques
(such as production rules, or case-based reasoning) are used to determine the
content of a generated text.Comment: To appear in the 1997 European NLG workshop. 10 pages, postscrip
Evaluating Centering for Information Ordering Using Corpora
In this article we discuss several metrics of coherence defined using centering theory and investigate the usefulness of such metrics for information ordering in automatic text generation. We estimate empirically which is the most promising metric and how useful this metric is using a general methodology applied on several corpora. Our main result is that the simplest metric (which relies exclusively on NOCB transitions) sets a robust baseline that cannot be outperformed by other metrics which make use of additional centering-based features. This baseline can be used for the development of both text-to-text and concept-to-text generation systems. </jats:p
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..
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
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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
Graph Mining under Linguistic Constraints to Explore Large Texts
https://www.cys.cic.ipn.mx/ojs/index.php/CyS/article/view/1529International audienceIn this paper, we propose an approach to explore large texts by highlighting coherent sub-parts. The exploration method relies on a graph representation of the text according to Hoey's linguistic model which allows the selection and the binding of adjacent and non-adjacent sentences. The main contribution of our work consists in proposing a method based on both Hoey's linguistic model and a special graph mining technique, called CoHoP mining, to extract coherent sub-parts of the graph representation of the text. We have conducted some experiments on several English texts showing the interest of the proposed approach
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