Learning a Generalizable Model of Team Conflict from Multiparty Dialogues

Good communication is indubitably the foundation of effective teamwork. Over time teams develop their own communication styles and often exhibit entrainment, a conversational phenomena in which humans synchronize their linguistic choices. Conversely, teams may experience conflict due to either personal incompatibility or differing viewpoints. We tackle the problem of predicting team conflict from embeddings learned from multiparty dialogues such that teams with similar post-task conflict scores lie close to one another in vector space. Embeddings were extracted from three types of features: (1) dialogue acts, (2) sentiment polarity, and (3) syntactic entrainment. Machine learning models often suffer domain shift; one advantage of encoding the semantic features is their adaptability across multiple domains. To provide intuition on the generalizability of different embeddings to other goal-oriented teamwork dialogues, we test the effectiveness of learned models trained on the Teams corpus on two other datasets. Unlike syntactic entrainment, both dialogue act and sentiment embeddings are effective for identifying team conflict. Our results show that dialogue act-based embeddings have the potential to generalize better than sentiment and entrainment-based embeddings. These findings have potential ramifications for the development of conversational agents that facilitate teaming.

Impact of Using Bidirectional Encoder Representations from Transformers (BERT) Models for Arabic Dialogue Acts Identification

In Human-Computer dialogue systems, the correct identification of the intent underlying a speaker's utterance is crucial to the success of a dialogue. Several researches have studied the Dialogue Act Classification (DAC) task to identify Dialogue Acts (DA) for different languages. Recently, the emergence of Bidirectional Encoder Representations from Transformers (BERT) models, enabled establishing state-of-the-art results for a variety of natural language processing tasks in different languages. Very few researches have been done in the Arabic Dialogue acts identification task. The BERT representation model has not been studied yet in Arabic Dialogue acts detection task. In this paper, we propose a model using BERT language representation to identify Arabic Dialogue Acts. We explore the impact of using different BERT models: AraBERT Original (v0.1, v1), AraBERT Base (v0.2, and v2) and AraBERT Large (v0.2, and v2), which are pretrained on different Arabic corpora (different in size, morphological segmentation, language model window, …). The comparison was performed on two available Arabic datasets. Using AraBERTv0.2-base model for dialogue representations outperformed all other pretrained models. Moreover, we compared the performance of AraBERTv0.2-base model to the state-of-the-art approaches applied on the two datasets. The comparison showed that this representation model outperformed the performance both state-of-the-art models.

Dialogue state tracking accuracy improvement by distinguishing slot-value pairs and dialogue behaviour

<div><p>Dialog state tracking (DST) plays a critical role in cycle life of a task-oriented dialogue system. DST represents the goals of the consumer at each step by dialogue and describes such objectives as a conceptual structure comprising slot-value pairs and dialogue actions that specifically improve the performance and effectiveness of dialogue systems. DST faces several challenges: diversity of linguistics, dynamic social context and the dissemination of the state of dialogue over candidate values both in slot values and in dialogue acts determined in ontology. In many turns during the dialogue, users indirectly refer to the previous utterances, and that produce a challenge to distinguishing and use of related dialogue history, Recent methods used and popular for that are ineffective. In this paper, we propose a dialogue historical context self-Attention framework for DST that recognizes relevant historical context by including previous user utterance beside current user utterances and previous system actions where specific slot-value piers variations and uses that together with weighted system utterance to outperform existing models by recognizing the related context and the relevance of a system utterance. For the evaluation of the proposed model the WoZ dataset was used. The implementation was attempted with the prior user utterance as a dialogue encoder and second by the additional score combined with all the candidate slot-value pairs in the context of previous user utterances and current utterances. The proposed model obtained 0.8 per cent better results than all state-of-the-art methods in the combined precision of the target, but this is not the turnaround challenge for the submission.</p></div>

Prosody in Automatic Speech Processing

Automatic speech processing (ASP) is understood as covering word recognition, the processing of higher linguistic components (syntax, semantics, and pragmatics), and the processing of computational paralinguistics (CP), which deals with speaker states and traits. This chapter attempts to track the role of prosody in ASP from the word level up to CP. A short history of the field from 1980 to 2020 distinguishes the early years (until 2000)—when the prosodic contribution to the modelling of linguistic phenomena, such as accents, boundaries, syntax, semantics, and dialogue acts, was the focus—from the later years, when the focus shifted to paralinguistics; prosody ceased to be visible. Different types of predictor variables are addressed, among them high-performance power features as well as leverage features, which can also be employed in teaching and therapy.

CrossWOZ: A Large-Scale Chinese Cross-Domain Task-Oriented Dialogue Dataset

To advance multi-domain (cross-domain) dialogue modeling as well as alleviate the shortage of Chinese task-oriented datasets, we propose CrossWOZ, the first large-scale Chinese Cross-Domain Wizard-of-Oz task-oriented dataset. It contains 6K dialogue sessions and 102K utterances for 5 domains, including hotel, restaurant, attraction, metro, and taxi. Moreover, the corpus contains rich annotation of dialogue states and dialogue acts on both user and system sides. About 60% of the dialogues have cross-domain user goals that favor inter-domain dependency and encourage natural transition across domains in conversation. We also provide a user simulator and several benchmark models for pipelined task-oriented dialogue systems, which will facilitate researchers to compare and evaluate their models on this corpus. The large size and rich annotation of CrossWOZ make it suitable to investigate a variety of tasks in cross-domain dialogue modeling, such as dialogue state tracking, policy learning, user simulation, etc.

World of Deacon Pobedov in Chekhov’s Novel «The Duel»: Laughter, Dialogue, Space

This article deals with the study of the phenomenon of laughter, which is an important component and feature of the character of deacon Pobedov in Chekhov’s novel «The Duel». The deacon’s laughter, as a reaction to the words and actions of other characters, implements in dialogues when Pobedov acts as an observer of the act of communication. In this context, laughter should be considered as an assessment of a communicative event. The study shows that the deacon, being in the space of nature, loses laughter as a positive reaction. In this space, the deacon, on the one hand, in the internal dialogue acts as a dreaming and reflecting person, and, on the other hand, a person experiencing feelings of fear, despair, and excitement.