Towards Finding and Fixing Fragments–-Using ML to Identify Non-Sentential Utterances and their Antecedents in Multi-Party Dialogue

Schlangen D. Towards Finding and Fixing Fragments–-Using ML to Identify Non-Sentential Utterances and their Antecedents in Multi-Party Dialogue. In: Proceedings of the 43rd Annual Meeting of the Association for Computational Linguistics (ACL05). Ann Arbor, Michigan: Association for Computational Linguistics; 2005: 247-254

Classification and Resolution of Non-Sentential Utterances in Dialogue

This article addresses the problems of classification and resolution of non-sentential utterances (NSUs) in dialogue. NSUs are utterances that do not have a complete sentential form but convey a full clausal meaning given the conversational context, such as “To the contrary!” or “How much?”. The presented approach builds upon the work of Fernández, Ginzburg, and Lappin (2007), who provide a taxonomy of NSUs divided in 15 classes along with a small annotated corpus extracted from dialogue transcripts. The main part of this article focuses on the automatic classification of NSUs according to these classes. We show that a combination of novel linguistic features and active learning techniques yields a significant improvement in the classification accuracy over the state-of-the-art, and is able to mitigate the scarcity of labelled data. Based on this classifier, the article also presents a novel approach for the semantic resolution of NSUs in context using probabilistic rules

Supervision distante pour l'apprentissage de structures discursives dans les conversations multi-locuteurs

L'objectif principal de cette thèse est d'améliorer l'inférence automatique pour la modélisation et la compréhension des communications humaines. En particulier, le but est de faciliter considérablement l'analyse du discours afin d'implémenter, au niveau industriel, des outils d'aide à l'exploration des conversations. Il s'agit notamment de la production de résumés automatiques, de recommandations, de la détection des actes de dialogue, de l'identification des décisions, de la planification et des relations sémantiques entre les actes de dialogue afin de comprendre les dialogues. Dans les conversations à plusieurs locuteurs, il est important de comprendre non seulement le sens de l'énoncé d'un locuteur et à qui il s'adresse, mais aussi les relations sémantiques qui le lient aux autres énoncés de la conversation et qui donnent lieu à différents fils de discussion. Une réponse doit être reconnue comme une réponse à une question particulière ; un argument, comme un argument pour ou contre une proposition en cours de discussion ; un désaccord, comme l'expression d'un point de vue contrasté par rapport à une autre idée déjà exprimée. Malheureusement, les données de discours annotées à la main et de qualités sont coûteuses et prennent du temps, et nous sommes loin d'en avoir assez pour entraîner des modèles d'apprentissage automatique traditionnels, et encore moins des modèles d'apprentissage profond. Il est donc nécessaire de trouver un moyen plus efficace d'annoter en structures discursives de grands corpus de conversations multi-locuteurs, tels que les transcriptions de réunions ou les chats. Un autre problème est qu'aucune quantité de données ne sera suffisante pour permettre aux modèles d'apprentissage automatique d'apprendre les caractéristiques sémantiques des relations discursives sans l'aide d'un expert ; les données sont tout simplement trop rares. Les relations de longue distance, dans lesquelles un énoncé est sémantiquement connecté non pas à l'énoncé qui le précède immédiatement, mais à un autre énoncé plus antérieur/tôt dans la conversation, sont particulièrement difficiles et rares, bien que souvent centrales pour la compréhension. Notre objectif dans cette thèse a donc été non seulement de concevoir un modèle qui prédit la structure du discours pour une conversation multipartite sans nécessiter de grandes quantités de données annotées manuellement, mais aussi de développer une approche qui soit transparente et explicable afin qu'elle puisse être modifiée et améliorée par des experts.The main objective of this thesis is to improve the automatic capture of semantic information with the goal of modeling and understanding human communication. We have advanced the state of the art in discourse parsing, in particular in the retrieval of discourse structure from chat, in order to implement, at the industrial level, tools to help explore conversations. These include the production of automatic summaries, recommendations, dialogue acts detection, identification of decisions, planning and semantic relations between dialogue acts in order to understand dialogues. In multi-party conversations it is important to not only understand the meaning of a participant's utterance and to whom it is addressed, but also the semantic relations that tie it to other utterances in the conversation and give rise to different conversation threads. An answer must be recognized as an answer to a particular question; an argument, as an argument for or against a proposal under discussion; a disagreement, as the expression of a point of view contrasted with another idea already expressed. Unfortunately, capturing such information using traditional supervised machine learning methods from quality hand-annotated discourse data is costly and time-consuming, and we do not have nearly enough data to train these machine learning models, much less deep learning models. Another problem is that arguably, no amount of data will be sufficient for machine learning models to learn the semantic characteristics of discourse relations without some expert guidance; the data are simply too sparse. Long distance relations, in which an utterance is semantically connected not to the immediately preceding utterance, but to another utterance from further back in the conversation, are particularly difficult and rare, though often central to comprehension. It is therefore necessary to find a more efficient way to retrieve discourse structures from large corpora of multi-party conversations, such as meeting transcripts or chats. This is one goal this thesis achieves. In addition, we not only wanted to design a model that predicts discourse structure for multi-party conversation without requiring large amounts of hand-annotated data, but also to develop an approach that is transparent and explainable so that it can be modified and improved by experts. The method detailed in this thesis achieves this goal as well

Context-based multimodal interpretation : an integrated approach to multimodal fusion and discourse processing

This thesis is concerned with the context-based interpretation of verbal and nonverbal contributions to interactions in multimodal multiparty dialogue systems. On the basis of a detailed analysis of context-dependent multimodal discourse phenomena, a comprehensive context model is developed. This context model supports the resolution of a variety of referring and elliptical expressions as well as the processing and reactive generation of turn-taking signals and the identification of the intended addressee(s) of a contribution. A major goal of this thesis is the development of a generic component for multimodal fusion and discourse processing. Based on the integration of this component into three distinct multimodal dialogue systems, the generic applicability of the approach is shown.Diese Dissertation befasst sich mit der kontextbasierten Interpretation von verbalen und nonverbalen Gesprächsbeiträgen im Rahmen von multimodalen Dialogsystemen. Im Rahmen dieser Arbeit wird, basierend auf einer detaillierten Analyse multimodaler Diskursphänomene, ein umfassendes Modell des Gesprächskontextes erarbeitet. Dieses Modell soll sowohl die Verarbeitung einer Vielzahl von referentiellen und elliptischen Ausdrücken, als auch die Erzeugung reaktiver Aktionen wie sie für den Sprecherwechsel benötigt werden unterstützen. Ein zentrales Ziel dieser Arbeit ist die Entwicklung einer generischen Komponente zur multimodalen Fusion und Diskursverarbeitung. Anhand der Integration dieser Komponente in drei unterschiedliche Dialogsysteme soll der generische Charakter dieser Komponente gezeigt werden

Towards Finding and Fixing Fragments: Using ML to Identify Non-Sentential Utterances and their Antecedents in Multi-Party Dialogue

Non-sentential utterances (e.g., shortanswers as in “Who came to the party?”— “Peter.”) are pervasive in dialogue. As with other forms of ellipsis, the elided material is typically present in the context (e.g., the question that a short answer answers). We present a machine learning approach to the novel task of identifying fragments and their antecedents in multiparty dialogue. We compare the performance of several learning algorithms, using a mixture of structural and lexical features, and show that the task of identifying antecedents given a fragment can be learnt successfully (f(0.5) =.76); we discuss why the task of identifying fragments is harder (f(0.5) =.41) and finally report on a combined task (f(0.5) =.38).

Head-Driven Phrase Structure Grammar

Head-Driven Phrase Structure Grammar (HPSG) is a constraint-based or declarative approach to linguistic knowledge, which analyses all descriptive levels (phonology, morphology, syntax, semantics, pragmatics) with feature value pairs, structure sharing, and relational constraints. In syntax it assumes that expressions have a single relatively simple constituent structure. This volume provides a state-of-the-art introduction to the framework. Various chapters discuss basic assumptions and formal foundations, describe the evolution of the framework, and go into the details of the main syntactic phenomena. Further chapters are devoted to non-syntactic levels of description. The book also considers related fields and research areas (gesture, sign languages, computational linguistics) and includes chapters comparing HPSG with other frameworks (Lexical Functional Grammar, Categorial Grammar, Construction Grammar, Dependency Grammar, and Minimalism)

Head-Driven Phrase Structure Grammar

Head-Driven Phrase Structure Grammar (HPSG) is a constraint-based or declarative approach to linguistic knowledge, which analyses all descriptive levels (phonology, morphology, syntax, semantics, pragmatics) with feature value pairs, structure sharing, and relational constraints. In syntax it assumes that expressions have a single relatively simple constituent structure. This volume provides a state-of-the-art introduction to the framework. Various chapters discuss basic assumptions and formal foundations, describe the evolution of the framework, and go into the details of the main syntactic phenomena. Further chapters are devoted to non-syntactic levels of description. The book also considers related fields and research areas (gesture, sign languages, computational linguistics) and includes chapters comparing HPSG with other frameworks (Lexical Functional Grammar, Categorial Grammar, Construction Grammar, Dependency Grammar, and Minimalism)