Multi-party Dialogue Research Articles

Recognizing Multi-Party Epistemic Dialogue Acts During Collaborative Game-Based Learning Using Large Language Models

AbstractUnderstanding students’ multi-party epistemic and topic based-dialogue contributions, or how students present knowledge in group-based chat interactions during collaborative game-based learning, offers valuable insights into group dynamics and learning processes. However, manually annotating these contributions is labor-intensive and challenging. To address this, we develop an automated method for recognizing dialogue acts from text chat data of small groups of middle school students interacting in a collaborative game-based learning environment. Our approach utilizes dual contrastive learning and label-aware data augmentation to fine-tune large language models’ underlying embedding representations within a supervised learning framework for epistemic and topic-based dialogue act classification. Results show that our method achieves a performance improvement of 4% to 8% over baseline methods in two key classification scenarios. These findings highlight the potential for automated dialogue act recognition to support understanding of how meaning-making occurs by focusing on the development and evolution of knowledge in group discourse, ultimately providing teachers with actionable insights to better support student learning.

Revisiting Conversation Discourse for Dialogue Disentanglement

Dialogue disentanglement aims to detach the chronologically ordered utterances into several independent sessions. Conversation utterances are essentially organized and described by the underlying discourse, and thus dialogue disentanglement requires the full understanding and harnessing of the intrinsic discourse attribute. In this paper, we propose enhancing dialogue disentanglement by taking full advantage of the dialogue discourse characteristics. First of all, in feature encoding stage , we construct the heterogeneous graph representations to model the various dialogue-specific discourse structural features, including the static speaker-role structures (i.e., speaker-utterance and speaker-mentioning structure) and the dynamic contextual structures (i.e., the utterance-distance and partial-replying structure). We then develop a structure-aware framework to integrate the rich structural features for better modeling the conversational semantic context. Second, in model learning stage , we perform optimization with a hierarchical ranking loss mechanism, which groups dialogue utterances into different discourse levels and carries training covering pair-wise and session-wise levels hierarchically. Third, in inference stage , we devise an easy-first decoding algorithm, which performs utterance pairing under the easy-to-hard manner with a global context, breaking the constraint of traditional sequential decoding order. On two benchmark datasets, our overall system achieves new state-of-the-art performances on all evaluations. In-depth analyses further demonstrate the efficacy of each proposed idea and also reveal how our methods help advance the task. Our work has great potential to facilitate broader multi-party multi-thread dialogue applications.

Enhancing Task-Oriented Dialogue Systems through Synchronous Multi-Party Interaction and Multi-Group Virtual Simulation

This paper presents two innovative approaches: a synchronous multi-party dialogue system that engages in simultaneous interactions with multiple users, and multi-group simulations involving virtual user groups to evaluate the resilience of this system. Unlike most other chatbots that communicate with each user independently, our system facilitates information gathering from multiple users and executes 17 administrative tasks for group requests adeptly by leveraging a state machine-based framework for complete control over dialogue flow and a large language model (LLM) for robust context understanding. Assessing such a unique dialogue system poses challenges, as it requires many groups of users to interact with the system concurrently for an extended duration. To address this, we simulate various virtual groups using an LLM, each comprising 10–30 users who may belong to multiple groups, in order to evaluate the efficacy of our system; each user is assigned a persona and allowed to interact freely without scripts. As a result, our system shows average success rates of 87% for task completion and 89% for natural language understanding. Comparatively, our virtual simulation, which has an average success rate of 80%, is juxtaposed with a group of 15 human users, depicting similar task diversity and error trends. To our knowledge, it is the first work to show the LLM’s potential in both task execution and the simulation of a synchronous dialogue system to fully automate administrative tasks.

Multi-particle-based multi-party controllable quantum secure dialogue

Based on the six-particle entangled state, this paper proposes a multi-party controllable quantum secure dialogue protocol, where the dialogue parties only need to perform single operation to efficiently encode secret information and embed the control information into a fixed particle sequence in order, ensuring the orderly transmission of information. Additionally, the protocol introduces a unique honesty detection mechanism, which relies on the classical XOR relationship of three qubits in a GHZ-like state, effectively guaranteeing the credibility of the controllers. The protocol allows two dialogue parties to synchronously obtain secret information with the permission of all controllers, without relying on additional classical channel, greatly enhancing communication efficiency and security. Through rigorous security analysis, it is proven that the protocol can effectively resist various potential attacks from both internal and external sources.

ChatMDG: A discourse parsing graph fusion based approach for multi-party dialogue generation

Comprehending multi-party dialogue generation poses a challenge due to intricate speaker interactions, where multiple participants engage in a dynamic exchange of questions and responses, assuming diverse roles such as speaker, receiver, and observer, with these roles evolving across conversational turns. Most existing research on multi-party dialogue generation only considers semantic information contained in each sentence and does not take into account the dialogue flow information implicit in multi-role interaction, leading to difficulties in accurately understanding the dialogue state in multi-party dialogue. To fill these gaps, we introduce an information fusion based approach for Multi-party Dialogue Generation named ChatMDG, which integrates role interaction into a semantic-enriched graph with context-based embeddings to cooperatively capture both global and local information in multi-party dialogue. Specifically, we proposes a graph-based network to represent the complex role-interaction dialogue structure for discourse parsing and then designs the dialogue flow encoding method to fuse role-interaction information with semantic states effectively. Furthermore, ChatMDG presents interaction strategies to correspondingly generate reactive and proactive utterances based on the fused embeddings, which lead to more dialogue coherence and user engagement. Experimental results show that ChatMDG significantly improves the accuracy of the multi-party response generation task, especially in complex scenarios with multiple interactions.

Hierarchical graph fusion network and a new argumentative dataset for multiparty dialogue discourse parsing

Discourse parsing in multi-party dialogue aims to extract the relationships between elementary discourse units (EDUs) such as arguments and utterances, and has numerous applications like chatbots or virtual assistants. Two significant challenges have been encountered in previous works: the obstacles in fusing various contexts in the modeling; and the lack of argumentative dialogue datasets in this field. To tackle context fusion challenges in the modeling, we introduce the Hierarchical Graph Fusion Network (HGFN). This method introduces sufficient contexts by hierarchically modeling the dialogue and minimizes context noise through a novel routing mechanism. It specifically: (1) Encodes multiple levels of contexts using hierarchical graph neural networks. During this stage, the router allows information exchange across different levels, expanding the model’s receptive field in dialogue. (2) Fuses matching signals from multiple levels of contexts with a fusion network. Here, the router restricts the information flows to the same context level, efficiently minimizing noise from irrelevant context. Furthermore, despite the significance of argumentative multi-party dialogue in real-world applications, this area remains largely unexplored due to dataset scarcity. To address this challenge, we develop two meticulously annotated datasets, MRDL and MRDR. Unlike the prevailing datasets that primarily focus on short and colloquial conversations, our datasets feature intricate argumentative dialogues and are publicly accessible at https://github.com/AI0Research/MRDL-and-MRDR. Our new datasets and the HGFN model could promote further advancements in this field. Extensive experiments are conducted and it is revealed that the HGFN model surpasses the state-of-the-art, particularly in complex, argumentative dialogues.

M2ER: Multimodal Emotion Recognition Based on Multi-Party Dialogue Scenarios

Researchers have recently focused on multimodal emotion recognition, but issues persist in recognizing emotions in multi-party dialogue scenarios. Most studies have only used text and audio modality, ignoring the video modality. To address this, we propose M2ER, a multimodal emotion recognition scheme based on multi-party dialogue scenarios. Addressing the issue of multiple faces appearing in the same frame of the video modality, M2ER introduces a method using multi-face localization for speaker recognition to eliminate the interference of non-speakers. The attention mechanism is used to fuse and classify different modalities. We conducted extensive experiments in unimodal and multimodal fusion using the multi-party dialogue dataset MELD. The results show that M2ER achieves superior emotion recognition in both text and audio modalities compared to the baseline model. The proposed method using speaker recognition in the video modality improves emotion recognition performance by 6.58% compared to the method without speaker recognition. In addition, the multimodal fusion based on the attention mechanism also outperforms the baseline fusion model.

Multilingual Coreference Resolution in Multiparty Dialogue

AbstractExisting multiparty dialogue datasets for entity coreference resolution are nascent, and many challenges are still unaddressed. We create a large-scale dataset, Multilingual Multiparty Coref (MMC), for this task based on TV transcripts. Due to the availability of gold-quality subtitles in multiple languages, we propose reusing the annotations to create silver coreference resolution data in other languages (Chinese and Farsi) via annotation projection. On the gold (English) data, off-the-shelf models perform relatively poorly on MMC, suggesting that MMC has broader coverage of multiparty coreference than prior datasets. On the silver data, we find success both using it for data augmentation and training from scratch, which effectively simulates the zero-shot cross-lingual setting.

MHaDiG: A Multilingual Humor-aided Multiparty Dialogue Generation in multimodal conversational setting

Building a multimodal humor-aided dialogue generation system in a multilingual setting is a very challenging task and it could even be more challenging in the Hindi language because of its morphological richness and free word order. Existing works only solved the problem of unimodal humorous dialogue generation, not humor-aided, in a monolingual setting. However, due to the tremendous growth in multimodal and multilingual content, there is a great demand to build multimodal humor-aided systems that support multilingual information access. To this end, in this paper, we propose a deep learning-based multimodal sentiment and emotion-aware humor-aided multiparty dialogue generation in multilingual (Hindi and English) setting. We evaluate our proposed approach (MHaDiG) on the recently released Sentiment, Humor, and Emotion-aware Multilingual Multimodal Multiparty Dataset (SHEMuD). Experimental results show the efficacy of MHaDiG and show the effect of multilingualism over monolingualism in the multimodal conversational setting.

Dialogue Relation Extraction with Document-Level Heterogeneous Graph Attention Networks

We propose a heterogeneous graph attention network to address the problem of dialogue relation extraction. Compared with several popular sequence-based and graph-based models, our method shows superior performance on the benchmark dataset DialogRE. The implementation of this work can be found at https://github.com/declare-lab/dialog-HGAT Dialogue relation extraction aims to detect the relation between pairs of entities mentioned in a multi-party dialogue. It plays an essential role in understanding the deep logic of dialogues and facilitating the development of intelligent dialogue systems. We introduce a heterogeneous graph attention network to model the cross-sentence relations in a conversation. This heterogeneous graph attention network has modeled multi-type features of the conversation, such as utterance, word, speaker, argument, and entity type information. We compare our method with several popular baselines such as convolutional neural networks and long short-term memory, experimental results show our model outperforms the state-of-the-art method by 9.4%/7.8% F1 scores, and 6.6%/3.9% $$F1_c$$ scores in both validation and test sets with only 4.0M parameters. In this work, we present an attention-based heterogeneous graph network to deal with the dialogue relation extraction task in an inductive manner. Experimental results on the dataset DialogRE confirm the effectiveness of our method.

A speaker-aware multiparty dialogue discourse parser with heterogeneous graph neural network

Discourse parsing for multiparty dialogue aims to detect the discourse structure and relations in a dialogue to obtain a discourse dependency graph. Existing models for this task have proven the effect of speaker information. However, no model explicitly learns representations of the speaker for parsing the discourse structure of dialogues. In this paper, to further exploit the effect of speaker information, we propose a novel model HG-MDP, which uses a heterogeneous graph neural network to encode dialogue graphs and we use an iterative update for the aggregation of speaker nodes and utterance nodes. Finally, we adopt updated utterance nodes to predict discourse dependency links and relations using the biaffine module. To validate our HG-MDP model, we perform experiments on the two existing benchmarks STAC and Molweni corpus. The results prove the effectiveness of the speaker modeling module on two datasets and we achieve the state-of-the-art on the Molweni dataset.

Enhanced Speaker-Aware Multi-Party Multi-Turn Dialogue Comprehension

Enhanced Speaker-Aware Multi-Party Multi-Turn Dialogue Comprehension

D$^{2}$PSG: Multi-Party Dialogue Discourse Parsing as Sequence Generation

D$^{2}$PSG: Multi-Party Dialogue Discourse Parsing as Sequence Generation

Dialogue Logic Aware and Key Utterance Decoupling Model for Multi-Party Dialogue Reading Comprehension

Multi-party dialogue machine reading comprehension (MRC) brings an unprecedented challenge due to the multiple speakers and the complex discourse linkages among speaker-aware utterances. The majority of current methods only consider the textual aspects of dialogue situations, and pay little attention to crucial speaker-aware cues. This prevents a model from capturing the speaker’s intention and important discourse information for questions in a complex discourse relationship, leading to the model giving wrong answers. In this paper, we construct a dialogue logic graph module by the relational graph convolutional network (R-GCN) to structure the dialogue information, and design a speaker prediction task to enhance the ability to capture discourse logic. Additionally, we construct a key utterance information decoupling module that focuses on the key discourse information flow involve questions, and filters out noise information. Extensive experiments FriendsQA and Molweni show that our approach outperforms competitive baselines and current state-of-the-art models, especially when dealing with more rounds of dialogue and questions involving people, events and time.

Territorial Conflict, Domestic Crisis, and the Covid-19 Pandemic in the South Caucasus. Explaining Variegated EU Responses.

Territorial Conflict, Domestic Crisis, and the Covid-19 Pandemic in the South Caucasus. Explaining Variegated EU Responses.

A survey of multi-party dialogue research based on deep learning

In recent years, with the extensive application of deep learning technology, breakthroughs have been made in the study of human-computer dialogue. However, most of the current human-machine dialogue systems are designed under the assumption that both parties are involved, and the research and application of more challenging multi-party human-machine dialogues are not yet mature. Based on the field of natural language processing, this paper will review the research progress of multi-party dialogue based on deep learning in recent years. First, from the perspective of human-machine dialogue, we sort out the key problems and existing solutions of the multi-party dialogue system; then, we introduce other natural language processing tasks based on multi-party dialogue; afterwards, we summarize the existing multi-party dialogue research dataset and make a comparative analysis of limitations on the existing dataset; Finally, we look forward to the future development trend of multi-party dialogue research.

More the Merrier: Towards Multi-Emotion and Intensity Controllable Response Generation

The focus on conversational systems has recently shifted towards creating engaging agents by inculcating emotions into them. Human emotions are highly complex as humans can express multiple emotions with varying intensity in a single utterance, whereas the conversational agents convey only one emotion in their responses. To infuse human-like behaviour in the agents, we introduce the task of multi-emotion controllable response generation with the ability to express different emotions with varying levels of intensity in an open-domain dialogue system. We introduce a Multiple Emotion Intensity aware Multi-party Dialogue (MEIMD) dataset having 34k conversations taken from 8 different TV Series. We finally propose a Multiple Emotion with Intensity-based Dialogue Generation (MEI-DG) framework. The system employs two novel mechanisms: viz. (i) determining the trade-off between the emotion and generic words, while focusing on the intensity of the desired emotions; and (ii) computing the amount of emotion left to be expressed, thereby regulating the generation accordingly. The detailed evaluation shows that our proposed approach attains superior performance compared to the baseline models.

Multi-party semi-quantum secure direct communication protocol with cluster states

A new semi-quantum secure direct communication (SQSDC) protocol based on four-particle cluster states is designed. Different from the existing SQSDC protocols, this semi-quantum secure direct communication protocol includes two classical senders and one quantum receiver. The designed SQSDC protocol promises higher qubit efficiency than the previous SQSDC protocols by adopting the property of the four-particle cluster state to implement encoding operations. Moreover, the security of the proposed protocol under some common eavesdropping attacks is validated. Furthermore, the presented SQSDC protocol can be transformed into an efficient multi-party semi-quantum dialogue (SQD) protocol.

Automatic Text-Based Personality Recognition on Monologues and Multiparty Dialogues Using Attentive Networks and Contextual Embeddings (Student Abstract)

Previous works related to automatic personality recognition focus on using traditional classification models with linguistic features. However, attentive neural networks with contextual embeddings, which have achieved huge success in text classification, are rarely explored for this task. In this project, we have two major contributions. First, we create the first dialogue-based personality dataset, FriendsPersona , by annotating 5 personality traits of speakers from Friends TV Show through crowdsourcing. Second, we present a novel approach to automatic personality recognition using pre-trained contextual embeddings (BERT and RoBERTa) and attentive neural networks. Our models largely improve the state-of-art results on the monologue Essays dataset by 2.49%, and establish a solid benchmark on our FriendsPersona. By comparing results in two datasets, we demonstrate the challenges of modeling personality in multi-party dialogue.

DREAM: A Challenge Data Set and Models for Dialogue-Based Reading Comprehension

We present DREAM, the first dialogue-based multiple-choice reading comprehension data set. Collected from English as a Foreign Language examinations designed by human experts to evaluate the comprehension level of Chinese learners of English, our data set contains 10,197 multiple-choice questions for 6,444 dialogues. In contrast to existing reading comprehension data sets, DREAM is the first to focus on in-depth multi-turn multi-party dialogue understanding. DREAM is likely to present significant challenges for existing reading comprehension systems: 84% of answers are non-extractive, 85% of questions require reasoning beyond a single sentence, and 34% of questions also involve commonsense knowledge. We apply several popular neural reading comprehension models that primarily exploit surface information within the text and find them to, at best, just barely outperform a rule-based approach. We next investigate the effects of incorporating dialogue structure and different kinds of general world knowledge into both rule-based and (neural and non-neural) machine learning-based reading comprehension models. Experimental results on the DREAM data set show the effectiveness of dialogue structure and general world knowledge. DREAM is available at https://dataset.org/dream/ .