Systèmes de questions-réponses

Résumé

Un système de questions-réponses (question answering system en anglais, ou QA system) est un système informatique permettant de répondre automatiquement à des questions posées par des humains, lors d'un échange fait en langue naturelle (comme le français). La discipline liée appartient aux domaines du traitement automatique de la langue et de la recherche d'information. Elle se démarque de l'interrogation de moteurs de recherche en cela qu'elle vise non seulement à récupérer les documents pertinents d'une collection de textes, mais également à formuler une réponse très ciblée à la question posée. Généralités Un outil de questions-réponses cherche à répondre à des requêtes, ou questions, telles « Quelle est la langue la plus parlée en Europe ? » ou « Quand est mort Louis XIV ? ». Le système utilise alors des techniques de traitement automatique des langues afin d'analyser la question et de rechercher une réponse adéquate à l'aide des documents auxquels il a accès. En pro

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https://fr.wikipedia.org/wiki/Syst%C3%A8mes_de_questions-r%C3%A9ponses

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Cognitive language engineering towards robust human-computer interaction

Vincenzo Pallotta

Intelligent information processing seems to be one of the most challenging task among those involved in human-computer interaction. A central issue is how to model the various types of interaction among artificial and natural entities at different levels of abstraction. On the one hand, models of interaction are required to better understand the communication phenomena. On the other, suitable languages and paradigms should provide powerful frameworks for developing computer-based applications. In this dissertation I focus on different aspects of the second problem, trying to develop a methodology for the design of interactive natural language applications (e.g. from question-answering to mixed-initiative dialogue). One of the main aspects I am concerned with in this work is the problem of their robustness. Several methods have been proposed for achieving robustness in natural language understanding, but these methods are sometimes hard to scale up or re-use in different applications. Moreover, they often concentrate on a single linguistic level of the processing rather than offering a global solution. I will set up a Language Engineering environment whose goal is to combine software engineering and cognitive aspects (e.g. aspects related to representation of a mental model of the speaker). Given its complexity, it is apparent that the problem can be solved only partially. I want to stress here that the main contribution of my work is a holistic perspective on the problem of natural language understanding. Rather than focusing on a particular aspect of natural language processing, I tried to benefit from the big amount of work that has been already done in Computational Linguistics and Computer Science merging different ideas and techniques. In the first part of the dissertation I explore the universe of Language Engineering in order to clarify how my contribution can be situated. After a survey on the state of the art on robust methods in analysis of natural language data, I focus on the role that Computational Logic plays in relating the syntactic and semantic analysis of natural language to its practical understanding within specific applications. Robustness is considered from two complementary perspectives, borrowing the terminology from modern software engineering: robustness "in the small" and robustness "in the large". The first perspective is discussed while presenting an application for the Interaction through Speech with Information Systems, where robust semantic parsing is used to extract queries from spoken natural language utterances.The second perspective is examplified by the re-engineering of an existing text analysis system using a new Language Engineering methodology: Agent-Oriented Language Engineering. In the second part of the thesis I discuss how cognitive aspects can be integrated into a Language Engineering environment leading to the notion of Cognitive Language Enginering. I tackle the difficult problem of robust dialogue management from both a cognitive and computational perspective. I propose two frameworks for the semantic representation and assimilation of information into the dialogue information state. The first framework allows us to represent and reason about the dynamic aspects of objects and events. The second framework is centered on the notion of mental space and it is used to build representations of the cognitive processing of information during communication.

EPFL2002

Chargement

We define a new task of question answering on meeting records and assess its difficulty in terms of types of information and retrieval techniques required. The importance of this task is revealed by the increasingly growing interest in the design of sophisticated interfaces for accessing meeting records such as meeting browsers. We ground our work on the empirical analysis of elicited user queries. We assess what is the type of information sought by the users and perform a user query classification along several semantic dimensions of the meeting content. We found that queries about argumentative processes and outcomes represent the majority among the elicited queries (about 60%). We also assess the difficulty in answering the queries and focus on the requirements of a prospective QA system to successfully deal with them. Our results suggest that standard Information Retrieval and Question Answering alone can only account for less than 20% of the queries and need to be completed with additional type of information and inference.

2006

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Question Answering in Conversations: Query Refinement Using Contextual and Semantic Information

Maryam Habibi, Parvaz Mahdabi, Andrei Popescu-Belis

This paper introduces a query refinement method applied to questions asked by users to a system during a meeting or a conversation that they have with other users. To answer the questions, the proposed method leverages the local context of the conversation along with semantic resources, either WordNet or word embeddings from word2vec. The method first represents the local context by extracting keywords from the transcript of the conversation, which is obtained from a real-time Automatic Speech Recognition (ASR) system and may contain noise. It then expands the queries with keywords that best represent the topic of the query, i.e.\ expansion keywords accompanied by weights indicating their topical similarity to the query. Finally, semantically related terms are added, using two options: either synonymous terms drawn from WordNet or similar words based on distributed representations in a low-dimensional word embedding space learned using word2vec. To evaluate the system, we introduce a dataset (named AREX for AMI Requests for Explanations) and an evaluation metric based on relevance judgments collected by crowdsourcing. We compare our query expansion approach with other methods, over queries from the AREX dataset, showing the superiority of our method when either manual or automatic transcripts of the AMI Meeting Corpus are used.

Idiap2016

Chargement

Cognitive language engineering towards robust human-computer interaction

Vincenzo Pallotta

EPFL2002