Joint Modeling of Content and Discourse Relations in Dialogues

We present a joint modeling approach to identify salient discussion points in spoken meetings as well as to label the discourse relations between speaker turns. A variation of our model is also discussed when discourse relations are treated as latent variables. Experimental results on two popular meeting corpora show that our joint model can outperform state-of-the-art approaches for both phrase-based content selection and discourse relation prediction tasks. We also evaluate our model on predicting the consistency among team members' understanding of their group decisions. Classifiers trained with features constructed from our model achieve significant better predictive performance than the state-of-the-art.Comment: Accepted by ACL 2017. 11 page

Dialogue Act Recognition via CRF-Attentive Structured Network

Dialogue Act Recognition (DAR) is a challenging problem in dialogue interpretation, which aims to attach semantic labels to utterances and characterize the speaker's intention. Currently, many existing approaches formulate the DAR problem ranging from multi-classification to structured prediction, which suffer from handcrafted feature extensions and attentive contextual structural dependencies. In this paper, we consider the problem of DAR from the viewpoint of extending richer Conditional Random Field (CRF) structural dependencies without abandoning end-to-end training. We incorporate hierarchical semantic inference with memory mechanism on the utterance modeling. We then extend structured attention network to the linear-chain conditional random field layer which takes into account both contextual utterances and corresponding dialogue acts. The extensive experiments on two major benchmark datasets Switchboard Dialogue Act (SWDA) and Meeting Recorder Dialogue Act (MRDA) datasets show that our method achieves better performance than other state-of-the-art solutions to the problem. It is a remarkable fact that our method is nearly close to the human annotator's performance on SWDA within 2% gap.Comment: 10 pages, 4figure

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

Improving Topic Segmentation by Injecting Discourse Dependencies

Recent neural supervised topic segmentation models achieve distinguished superior effectiveness over unsupervised methods, with the availability of large-scale training corpora sampled from Wikipedia. These models may, however, suffer from limited robustness and transferability caused by exploiting simple linguistic cues for prediction, but overlooking more important inter-sentential topical consistency. To address this issue, we present a discourse-aware neural topic segmentation model with the injection of above-sentence discourse dependency structures to encourage the model make topic boundary prediction based more on the topical consistency between sentences. Our empirical study on English evaluation datasets shows that injecting above-sentence discourse structures to a neural topic segmenter with our proposed strategy can substantially improve its performances on intra-domain and out-of-domain data, with little increase of model's complexity.Comment: Accepted to the 3rd Workshop on Computational Approaches to Discourse (CODI-2022) at COLING 202

Dialogue Act Recognition Approaches

This paper deals with automatic dialogue act (DA) recognition. Dialogue acts are sentence-level units that represent states of a dialogue, such as questions, statements, hesitations, etc. The knowledge of dialogue act realizations in a discourse or dialogue is part of the speech understanding and dialogue analysis process. It is of great importance for many applications: dialogue systems, speech recognition, automatic machine translation, etc. The main goal of this paper is to study the existing works about DA recognition and to discuss their respective advantages and drawbacks. A major concern in the DA recognition domain is that, although a few DA annotation schemes seem now to emerge as standards, most of the time, these DA tag-sets have to be adapted to the specificities of a given application, which prevents the deployment of standardized DA databases and evaluation procedures. The focus of this review is put on the various kinds of information that can be used to recognize DAs, such as prosody, lexical, etc., and on the types of models proposed so far to capture this information. Combining these information sources tends to appear nowadays as a prerequisite to recognize DAs

Accessing spoken interaction through dialogue processing [online]

Zusammenfassung Unser Leben, unsere Leistungen und unsere Umgebung, alles wird derzeit durch Schriftsprache dokumentiert. Die rasante Fortentwicklung der technischen Möglichkeiten Audio, Bilder und Video aufzunehmen, abzuspeichern und wiederzugeben kann genutzt werden um die schriftliche Dokumentation von menschlicher Kommunikation, zum Beispiel Meetings, zu unterstützen, zu ergänzen oder gar zu ersetzen. Diese neuen Technologien können uns in die Lage versetzen Information aufzunehmen, die anderweitig verloren gehen, die Kosten der Dokumentation zu senken und hochwertige Dokumente mit audiovisuellem Material anzureichern. Die Indizierung solcher Aufnahmen stellt die Kerntechnologie dar um dieses Potential auszuschöpfen. Diese Arbeit stellt effektive Alternativen zu schlüsselwortbasierten Indizes vor, die Suchraumeinschränkungen bewirken und teilweise mit einfachen Mitteln zu berechnen sind. Die Indizierung von Sprachdokumenten kann auf verschiedenen Ebenen erfolgen: Ein Dokument gehört stilistisch einer bestimmten Datenbasis an, welche durch sehr einfache Merkmale bei hoher Genauigkeit automatisch bestimmt werden kann. Durch diese Art von Klassifikation kann eine Reduktion des Suchraumes um einen Faktor der Größenordnung 4­10 erfolgen. Die Anwendung von thematischen Merkmalen zur Textklassifikation bei einer Nachrichtendatenbank resultiert in einer Reduktion um einen Faktor 18. Da Sprachdokumente sehr lang sein können müssen sie in thematische Segmente unterteilt werden. Ein neuer probabilistischer Ansatz sowie neue Merkmale (Sprecherinitia­ tive und Stil) liefern vergleichbare oder bessere Resultate als traditionelle schlüsselwortbasierte Ansätze. Diese thematische Segmente können durch die vorherrschende Aktivität charakterisiert werden (erzählen, diskutieren, planen, ...), die durch ein neuronales Netz detektiert werden kann. Die Detektionsraten sind allerdings begrenzt da auch Menschen diese Aktivitäten nur ungenau bestimmen. Eine maximale Reduktion des Suchraumes um den Faktor 6 ist bei den verwendeten Daten theoretisch möglich. Eine thematische Klassifikation dieser Segmente wurde ebenfalls auf einer Datenbasis durchgeführt, die Detektionsraten für diesen Index sind jedoch gering. Auf der Ebene der einzelnen Äußerungen können Dialogakte wie Aussagen, Fragen, Rückmeldungen (aha, ach ja, echt?, ...) usw. mit einem diskriminativ trainierten Hidden Markov Model erkannt werden. Dieses Verfahren kann um die Erkennung von kurzen Folgen wie Frage/Antwort­Spielen erweitert werden (Dialogspiele). Dialogakte und ­spiele können eingesetzt werden um Klassifikatoren für globale Sprechstile zu bauen. Ebenso könnte ein Benutzer sich an eine bestimmte Dialogaktsequenz erinnern und versuchen, diese in einer grafischen Repräsentation wiederzufinden. In einer Studie mit sehr pessimistischen Annahmen konnten Benutzer eines aus vier ähnlichen und gleichwahrscheinlichen Gesprächen mit einer Genauigkeit von ~ 43% durch eine graphische Repräsentation von Aktivität bestimmt. Dialogakte könnte in diesem Szenario ebenso nützlich sein, die Benutzerstudie konnte aufgrund der geringen Datenmenge darüber keinen endgültigen Aufschluß geben. Die Studie konnte allerdings für detailierte Basismerkmale wie Formalität und Sprecheridentität keinen Effekt zeigen. Abstract Written language is one of our primary means for documenting our lives, achievements, and environment. Our capabilities to record, store and retrieve audio, still pictures, and video are undergoing a revolution and may support, supplement or even replace written documentation. This technology enables us to record information that would otherwise be lost, lower the cost of documentation and enhance high­quality documents with original audiovisual material. The indexing of the audio material is the key technology to realize those benefits. This work presents effective alternatives to keyword based indices which restrict the search space and may in part be calculated with very limited resources. Indexing speech documents can be done at a various levels: Stylistically a document belongs to a certain database which can be determined automatically with high accuracy using very simple features. The resulting factor in search space reduction is in the order of 4­10 while topic classification yielded a factor of 18 in a news domain. Since documents can be very long they need to be segmented into topical regions. A new probabilistic segmentation framework as well as new features (speaker initiative and style) prove to be very effective compared to traditional keyword based methods. At the topical segment level activities (storytelling, discussing, planning, ...) can be detected using a machine learning approach with limited accuracy; however even human annotators do not annotate them very reliably. A maximum search space reduction factor of 6 is theoretically possible on the databases used. A topical classification of these regions has been attempted on one database, the detection accuracy for that index, however, was very low. At the utterance level dialogue acts such as statements, questions, backchannels (aha, yeah, ...), etc. are being recognized using a novel discriminatively trained HMM procedure. The procedure can be extended to recognize short sequences such as question/answer pairs, so called dialogue games. Dialog acts and games are useful for building classifiers for speaking style. Similarily a user may remember a certain dialog act sequence and may search for it in a graphical representation. In a study with very pessimistic assumptions users are able to pick one out of four similar and equiprobable meetings correctly with an accuracy ~ 43% using graphical activity information. Dialogue acts may be useful in this situation as well but the sample size did not allow to draw final conclusions. However the user study fails to show any effect for detailed basic features such as formality or speaker identity