A Survey of Available Corpora For Building Data-Driven Dialogue Systems: The Journal Version

During the past decade, several areas of speech and language understanding have witnessed substantial breakthroughs from the use of data-driven models. In the area of dialogue systems, the trend is less obvious, and most practical systems are still built through significant engineering and expert knowledge. Nevertheless, several recent results suggest that data-driven approaches are feasible and quite promising. To facilitate research in this area, we have carried out a wide survey of publicly available datasets suitable for data-driven learning of dialogue systems. We discuss important characteristics of these datasets, how they can be used to learn diverse dialogue strategies, and their other potential uses. We also examine methods for transfer learning between datasets and the use of external knowledge. Finally, we discuss appropriate choice of evaluation metrics for the learning objective

Deriving and Exploiting Situational Information in Speech: Investigations in a Simulated Search and Rescue Scenario

The need for automatic recognition and understanding of speech is emerging in tasks involving the processing of large volumes of natural conversations. In application domains such as Search and Rescue, exploiting automated systems for extracting mission-critical information from speech communications has the potential to make a real difference. Spoken language understanding has commonly been approached by identifying units of meaning (such as sentences, named entities, and dialogue acts) for providing a basis for further discourse analysis. However, this fine-grained identification of fundamental units of meaning is sensitive to high error rates in the automatic transcription of noisy speech. This thesis demonstrates that topic segmentation and identification techniques can be employed for information extraction from spoken conversations by being robust to such errors. Two novel topic-based approaches are presented for extracting situational information within the search and rescue context. The first approach shows that identifying the changes in the context and content of first responders' report over time can provide an estimation of their location. The second approach presents a speech-based topological map estimation technique that is inspired, in part, by automatic mapping algorithms commonly used in robotics. The proposed approaches are evaluated on a goal-oriented conversational speech corpus, which has been designed and collected based on an abstract communication model between a first responder and a task leader during a search process. Results have confirmed that a highly imperfect transcription of noisy speech has limited impact on the information extraction performance compared with that obtained on the transcription of clean speech data. This thesis also shows that speech recognition accuracy can benefit from rescoring its initial transcription hypotheses based on the derived high-level location information. A new two-pass speech decoding architecture is presented. In this architecture, the location estimation from a first decoding pass is used to dynamically adapt a general language model which is used for rescoring the initial recognition hypotheses. This decoding strategy has resulted in a statistically significant gain in the recognition accuracy of the spoken conversations in high background noise. It is concluded that the techniques developed in this thesis can be extended to more application domains that deal with large volumes of natural spoken conversations

The Global Language of Human Rights: A Computational Linguistic Analysis

Human rights discourse has been likened to a global lingua franca, and in more ways than one, the analogy seems apt. Human rights discourse is a language that is used by all yet belongs uniquely to no particular place. It crosses not only the borders between nation-states, but also the divide between national law and international law: it appears in national constitutions and international treaties alike. But is it possible to conceive of human rights as a global language or lingua franca not just in a figurative or metaphorical sense, but in a literal or linguistic sense as a legal dialect defined by distinctive patterns of word choice and usage? Does there exist a global language of human rights that transcends not only national borders, but also the divide between domestic and international law? Empirical analysis suggests that the answer is yes, but this global language comes in at least two variants or dialects. New techniques for performing automated content analysis enable us to analyze the bulk of all national constitutions over the last two centuries, together with the world’s leading regional and international human rights instruments, for patterns of linguistic similarity and to evaluate how much language, if any, they share in common. Specifically, we employ a technique known as topic modeling that disassembles texts into recurring verbal patterns. The results highlight the existence of two species or dialects of rights talk—the universalist dialect and the positive-rights dialect—both of which are global in reach and rising in popularity. The universalist dialect is generic in content and draws heavily on the type of language found in international and regional human rights instruments. It appears in particularly large doses in the constitutions of transitional states, developing states, and states that have been heavily exposed to the influence of the international community. The positive-rights dialect, by contrast, is characterized by its substantive emphasis on positive rights of a social or economic variety, and by its prevalence in lengthier constitutions and constitutions from outside the common law world, especially those of the Spanish-speaking world. Both dialects of rights talk are truly transnational, in the sense that they appear simultaneously in national, regional, and international legal instruments and transcend the distinction between domestic and international law. Their existence attests to the blurring of the boundary between constitutional law and international law

Automatic Summarization

It has now been 50 years since the publication of Luhn’s seminal paper on automatic summarization. During these years the practical need for automatic summarization has become increasingly urgent and numerous papers have been published on the topic. As a result, it has become harder to find a single reference that gives an overview of past efforts or a complete view of summarization tasks and necessary system components. This article attempts to fill this void by providing a comprehensive overview of research in summarization, including the more traditional efforts in sentence extraction as well as the most novel recent approaches for determining important content, for domain and genre specific summarization and for evaluation of summarization. We also discuss the challenges that remain open, in particular the need for language generation and deeper semantic understanding of language that would be necessary for future advances in the field

COMPUTATIONAL ANALYSIS OF THE CONVERSATIONAL DYNAMICS OF THE UNITED STATES SUPREME COURT

The decisions of the United States Supreme Court have far-reaching implications in American life. Using transcripts of Supreme Court oral arguments this work looks at the conversational dynamics of Supreme Court justices and links their conversational interaction with the decisions of the Court and individual justices. While several studies have looked at the relationship between oral arguments and case variables, to our knowledge, none have looked at the relationship between conversational dynamics and case outcomes. Working from this view, we show that the conversation of Supreme Court justices is both predictable and predictive. We aim to show that conversation during Supreme Court cases is patterned, this patterned conversation is associated with case outcomes, and that this association can be used to make predictions about case outcomes. We present three sets of experiments to accomplish this. The first examines the order of speakers during oral arguments as a patterned sequence, showing that cohesive elements in the discourse, along with references to individuals, provide significant improvements over our "bag-of-words" baseline in identifying speakers in sequence within a transcript. The second graphically examines the association between speaker turn-taking and case outcomes. The results presented with this experiment point to interesting and complex relationships between conversational interaction and case variables, such as justices' votes. The third experiment shows that this relationship can be used in the prediction of case outcomes with accuracy ranging from 62.5% to 76.8% for varying conditions. Finally, we offer recommendations for improved tools for legal researchers interested in the relationship between conversation during oral arguments and case outcomes, and suggestions for how these tools may be applied to more general problems

Lexical ambiguity detection in professional discourse

Publisher Copyright: © 2022 The Author(s)Professional discourse is the language used by specialists, such as lawyers, doctors and academics, to communicate the knowledge and assumptions associated with their respective fields. Professional discourse can be especially difficult for non-specialists to understand due to the lexical ambiguity of commonplace words that have a different or more specific meaning within a specialist domain. This phenomena also makes it harder for specialists to communicate with the general public because they are similarly unaware of the potential for misunderstandings. In this article, we present an approach for detecting domain terms with lexical ambiguity versus everyday English. We demonstrate the efficacy of our approach with three case studies in statistics, law and biomedicine. In all case studies, we identify domain terms with a precision@100 greater than 0.9, outperforming the best performing baseline by 18.1–91.7%. Most importantly, we show this ranking is broadly consistent with semantic differences. Our results highlight the difficulties that existing semantic difference methods have in the cross-domain setting, which rank non-domain terms highly due to noise or biases in the data. We additionally show that our approach generalizes to short phrases and investigate its data efficiency by varying the number of labeled examples.Peer reviewe

Survey of the State of the Art in Natural Language Generation: Core tasks, applications and evaluation

This paper surveys the current state of the art in Natural Language Generation (NLG), defined as the task of generating text or speech from non-linguistic input. A survey of NLG is timely in view of the changes that the field has undergone over the past decade or so, especially in relation to new (usually data-driven) methods, as well as new applications of NLG technology. This survey therefore aims to (a) give an up-to-date synthesis of research on the core tasks in NLG and the architectures adopted in which such tasks are organised; (b) highlight a number of relatively recent research topics that have arisen partly as a result of growing synergies between NLG and other areas of artificial intelligence; (c) draw attention to the challenges in NLG evaluation, relating them to similar challenges faced in other areas of Natural Language Processing, with an emphasis on different evaluation methods and the relationships between them.Comment: Published in Journal of AI Research (JAIR), volume 61, pp 75-170. 118 pages, 8 figures, 1 tabl

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

Neural Unsupervised Domain Adaptation in NLP—A Survey

Deep neural networks excel at learning from labeled data and achieve state-of-the-art results on a wide array of Natural Language Processing tasks. In contrast, learning from unlabeled data, especially under domain shift, remains a challenge. Motivated by the latest advances, in this survey we review neural unsupervised domain adaptation techniques which do not require labeled target domain data. This is a more challenging yet a more widely applicable setup. We outline methods, from early approaches in traditional non-neural methods to pre-trained model transfer. We also revisit the notion of domain, and we uncover a bias in the type of Natural Language Processing tasks which received most attention. Lastly, we outline future directions, particularly the broader need for out-of-distribution generalization of future intelligent NLP