Thread Reconstruction in Conversational Data using Neural Coherence Models

Discussion forums are an important source of information. They are often used to answer specific questions a user might have and to discover more about a topic of interest. Discussions in these forums may evolve in intricate ways, making it difficult for users to follow the flow of ideas. We propose a novel approach for automatically identifying the underlying thread structure of a forum discussion. Our approach is based on a neural model that computes coherence scores of possible reconstructions and then selects the highest scoring, i.e., the most coherent one. Preliminary experiments demonstrate promising results outperforming a number of strong baseline methods.Comment: Neu-IR: Workshop on Neural Information Retrieval 201

Sélection de Réponses pour les Systèmes de Dialogue Basés sur la Recherche de Réponse de Bout en Bout

The increasing need of human assistancepushed researchers to develop automatic,smart and tireless dialogue systems that can conversewith humans in natural language to be eithertheir virtual assistant or their chat companion. Theindustry of dialogue systems has been very popularin the last decade and many systems from industryand academia have been developed. In this thesis,we study retrieval-based dialogue systems whichaim to find the most appropriate response to theconversation among a set of predefined responses.The main challenge of these systems is to understandthe conversation and identify the elementsthat describe the problem and the solution whichare usually implicit. Most of the recent approachesare based on deep learning techniques which canautomatically capture implicit information. Howeverthese approaches are either complex or domaindependent. We propose a simple, end-to-endand efficient retrieval-based dialogue system thatfirst matches the response with the history of theconversation on the sequence-level and then we extendthe system to multiple levels while keeping thearchitecture simple and domain independent. Weperform several analyzes to determine possible improvements.e besoin croissant en assistance humainea poussé les chercheurs à développer dessystèmes de dialogue automatiques, intelligents etinfatigables qui conversent avec les humains dansun langage naturel pour devenir soit leurs assistantsvirtuels ou leurs compagnons. L’industriedes systèmes de dialogue est devenue populairecette dernière décennie, ainsi, plusieurs systèmesont été développés par des industriels comme desacadémiques. Dans le cadre de cette thèse, nousétudions les systèmes de dialogue basés sur larecherche de réponse qui cherchant la réponse laplus appropriée à la conversation parmi un ensemblede réponses prédéfini. Le défi majeur de cessystèmes est la compréhension de la conversation etl’identification des éléments qui décrivent le problèmeet la solution qui sont souvent implicites. Laplupart des approches récentes sont basées sur destechniques d’apprentissage profond qui permettentde capturer des informations implicites. Souvent,ces approches sont complexes ou dépendent fortementdu domaine. Nous proposons une approchede recherche de réponse de bout en bout, simple,efficace et indépendante du domaine et qui permetde capturer ces informations implicites. Nouseffectuons également plusieurs analyses afin dedéterminer des pistes d’amélioration

MappSent: a Textual Mapping Approach for Question-to-Question Similarity

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A Multi-Domain Framework for Textual Similarity. A Case Study on Question-to-Question and Question-Answering Similarity Tasks

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Ordonnancement de réponses dans les systèmes de dialogue basé sur une similarité contexte/réponse

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Towards Simple but Efficient Next Utterance Ranking

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Multi-level Context Response Matching in Retrieval-Based Dialog Systems

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Next utterance ranking based on context response similarity

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Towards Simple but Efficient Next Utterance Ranking

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Thread Reconstruction in Conversational Data using Neural Coherence

International audienceDiscussion forums are an important source of information. They are often used to answer specific questions a user might have and to discover more about a topic of interest. Discussions in these forums may evolve in intricate ways, making it difficult for users to follow the flow of ideas. We propose a novel approach for automatically identifying the underlying thread structure of a forum discussion. Our approach is based on a neural model that computes coherence scores of possible reconstructions and then selects the highest scoring, i.e., the most coherent one. Preliminary experiments demonstrate promising results outperforming a number of strong baseline methods