BACKGROUND: Transcription factors and their target genes form regulatory modules known as regulons, which exhibit significant specificity across various cell types. The integration of single-cell transcriptome data, transcription factor motif data, and ChIP-seq data presents a challenging task in identifying cell-type-specific regulons and examining their activities. RESULTS: In response, this study presents a Deep Structured Semantic Model for inferring and prioritizing cell-type-specific Regulons (DSSMReg). This approach utilizes single-cell transcriptome and transcription factor motif data to map transcription factors and target genes into a low-dimensional semantic space, resulting in the generation of feature vectors. The model then computes the cosine similarity between transcription factors and target genes to evaluate their regulatory strength and subsequently infers cell-type-specific regulons based on this assessment. Moreover, DSSMReg employs the AUCell algorithm to rank the importance of regulons for each cell type. CONCLUSIONS: We compared DSSMReg against five representative gene regulatory inference algorithms using scRNA-seq data from five cell lines, with DSSMReg achieving the highest evaluation metrics for both AUROC and AUPRC. Furthermore, we applied DSSMReg to infer cell-type-specific regulons from scRNA-seq data of triple-negative breast cancer and human bone marrow hematopoietic stem cells. Our results indicated that regulons with high AUCell scores possess significant biological relevance. The source code of DSSMReg is freely available at https://github.com/YaxinF/DSSMReg .

Dialogue discourse parsing aims to identify and analyze discourse relations between the utterances within dialogues. However, linguistic features in dialogues, such as omission and idiom, frequently introduce ambiguities that obscure the intended discourse relations, posing significant challenges for parsers. To address this issue, we propose a Discourse-aware Clarification Module (DCM) to enhance the performance of the dialogue discourse parser. DCM employs two distinct reasoning processes: clarification type reasoning and discourse goal reasoning. The former analyzes linguistic features, while the latter distinguishes the intended relation from the ambiguous one. Furthermore, we introduce Contribution-aware Preference Optimization (CPO) to mitigate the risk of erroneous clarifications, thereby reducing cascading errors. CPO enables the parser to assess the contributions of the clarifications from DCM and provide feedback to optimize the DCM, enhancing its adaptability and alignment with the parser’s requirements. Extensive experiments on the STAC and Molweni datasets demonstrate that our approach effectively resolves ambiguities and significantly outperforms the state-of-the-art (SOTA) baselines.

Goal-oriented proactive dialogue systems are designed to guide user conversations seamlessly towards specific objectives by planning a goal-oriented path. However, previous research has focused predominantly on optimizing these paths while neglecting the inconsistencies that may arise between generated responses and dialogue contexts, including user profiles, dialogue history, domain knowledge, and subgoals. To address this issue, we introduce a model-agnostic two-stage Consistency Reflection and Correction (CRC) framework. Specifically, in the consistency reflection stage, the model is prompted to reflect on the discrepancies between generated responses and dialogue contexts, identifying inconsistencies and suggesting possible corrections. In the consistency correction stage, the model generates responses that are more consistent with the dialogue context based on these reflection results. We conducted experiments on various model architectures with different parameter sizes, including encoder-decoder models (BART, T5) and decoder-only models (GPT-2, DialoGPT, Phi3, Mistral and LLaMA3), and the experimental results on three datasets demonstrate that our CRC framework significantly improves the consistency between generated responses and dialogue contexts.

Previous work on Incomplete Utterance Rewriting (IUR) has primarily focused on generating rewritten utterances based solely on dialogue context, ignoring the widespread phenomenon of coreference and ellipsis in dialogues. To address this issue, we propose a novel framework called TEO (Two-stage approach on Editing Operation) for IUR, in which the first stage generates editing operations and the second stage rewrites incomplete utterances utilizing the generated editing operations and the dialogue context. Furthermore, an adversarial perturbation strategy is proposed to mitigate cascading errors and exposure bias caused by the inconsistency between training and inference in the second stage. Experimental results on three IUR datasets show that our TEO outperforms the SOTA models significantly.

Previous work on empathetic response generation mainly focused on utilizing the speaker’s emotions to generate responses. However, the performance of identifying fine-grained emotions is limited, introducing cascading errors to empathetic response generation. Moreover, due to the conflict between the information in the dialogue history and the recognized emotions, previous work often generated general and uninformative responses. To address the above issues, we propose a novel framework NEC (Non-Emotion-Centric empathetic dialogue generation) based on contrastive learning and context-sensitive entity and social commonsense, in which the frequent replies and sentences with incorrect emotions are punished through contrastive learning, thereby improving the empathy, diversity and information of the responses. The experimental results demonstrate that our NEC enhances the quality of empathetic generation and generates more diverse responses in comparison with the state-of-the-art baselines.The code will be available at https://github.com/huangfu170/NEC-empchat

Multi-party dialogue discourse parsing is an important and challenging task in natural language processing (NLP). Previous studies struggled to fully understand the deep semantics of dialogues, especially when dealing with complex topic interleaving and ellipsis. To address the above issues, we propose a novel model DDPE (Dialogue Discourse Parsing with Explanations) to integrate external knowledge from Large Language Models (LLMs), which consists of three components, i.e., explanation generation, structural parsing, and contrastive learning. DDPE employs LLMs to generate explanatory and contrastive information about discourse structure, thereby providing additional reasoning cues that enhance the understanding of dialogue semantics. The experimental results on the two public datasets STAC and Molweni show that our DDPE significantly outperforms the state-of-the-art (SOTA) baselines.

Previous work on dialogue topic shift detection has primarily focused on shallow local reasoning, overlooking the importance of considering the global historical structure and local details to elucidate the underlying causes of topic shift. To address the above two issues, we introduce the dual-process theory to this task and design a novel Dual-Module Framework DMF (i.e., intuition and reasoning module) for dialogue topic shift detection to emulate this cognitive process. Specifically, the intuition module employs Large Language Models (LLMs) to extract and store the global topic structure of historical dialogue, while the reasoning module introduces a LLM to generate reasoning samples between the response and the most recent topic of historical dialogue, thereby providing local detail explanations for topic shift. Moreover, we distill the dual-module framework into a small generative model to facilitate more precise reasoning. The experimental results on three public datasets show that our DMF outperforms the state-of-the-art baselines.

Multiparty dialog discourse parsing (MDDP) aims to identify the links between pairs of utterances and recognize their discourse relations. Previous research has attempted to address data sparsity in discourse parsing through multitask learning, but these efforts often relied on manually annotated fine-grained information, limiting their practical applicability. In this study, we propose dynamic task-adaptive graph transformer with difficulty-aware task scheduling (DTGT-DTS), an innovative multitask approach that enhances discourse parsing by leveraging neighboring tasks like addressee recognition and speaker identification, without requiring additional annotations. These tasks share common discourse links with discourse parsing but also possess distinct private links. To tackle this, we design a dynamic task-adaptive graph transformer (DTGT) that captures shared links between discourse parsing and its neighboring tasks while distinguishing the private links of neighboring tasks. In addition, we develop a difficulty-aware task scheduling (DTS) strategy that promotes multitask learning by dynamically adjusting training priorities based on the relative difficulty of different tasks. Experimental results on two widely used discourse datasets-Molweni (78 245 links and relations) and STAC (12 691 links and relations)-show that our DTGT-DTS model achieves a 6.07% and 5.31% performance improvement in link identification, respectively, and a 7.27% and 6.02% improvement in relation recognition.

The unparalleled performance of closedsourced ChatGPT has sparked efforts towards its democratization, with notable strides made by leveraging real user and ChatGPT dialogues, as evidenced by Vicuna.However, due to challenges in gathering dialogues involving human participation, current endeavors like Baize and UltraChat rely on ChatGPT conducting roleplay to simulate humans based on instructions, resulting in overdependence on seeds, diminished human-likeness, limited topic diversity, and an absence of genuine multi-round conversational dynamics.To address the above issues, we propose a paradigm to simulate human behavior better and explore the benefits of incorporating more human-like questions in multiturn conversations.Specifically, we directly target human questions extracted from genuine human-machine conversations as a learning goal and provide a novel user simulator called 'Socratic'.The experimental results show our response model, 'PlatoLM', achieves SoTA performance among LLaMA-based 7B models in MT-Bench.Our findings further demonstrate that our method introduces highly human-like questioning patterns and rich topic structures, which can teach the response model better than previous works in multi-round conversations.

Large language models, like ChatGPT, have shown remarkable capability in many downstream tasks, yet their ability to understand discourse structures of dialogues remains less explored, where it requires higher level capabilities of understanding and reasoning. In this paper, we aim to systematically inspect ChatGPT’s performance in two discourse analysis tasks: topic segmentation and discourse parsing, focusing on its deep semantic understanding of linear and hierarchical discourse structures underlying dialogue. To instruct ChatGPT to complete these tasks, we initially craft a prompt template consisting of the task description, output format, and structured input. Then, we conduct experiments on four popular topic segmentation datasets and two discourse parsing datasets. The experimental results showcase that ChatGPT demonstrates proficiency in identifying topic structures in general-domain conversations yet struggles considerably in specific-domain conversations. We also found that ChatGPT hardly understands rhetorical structures that are more complex than topic structures. Our deeper investigation indicates that ChatGPT can give more reasonable topic structures than human annotations but only linearly parses the hierarchical rhetorical structures. In addition, we delve into the impact of in-context learning (e.g., chain-of-thought) on ChatGPT and conduct the ablation study on various prompt components, which can provide a research foundation for future work. The code is available at https://github.com/yxfanSuda/GPTforDDA.

Although existing fashionable generation methods on Incomplete Utterance Rewriting (IUR) can generate coherent utterances, they often result in the inclusion of irrelevant and redundant tokens in rewritten utterances due to their inability to focus on critical tokens in dialogue context.Furthermore, the limited size of the training datasets also contributes to the insufficient training of the IUR model.To address the first issue, we propose a multi-task learning framework EO-IUR (Editing Operation-guided Incomplete Utterance Rewriting) that introduces the editing operation labels generated by sequence labeling module to guide generation model to focus on critical tokens.Furthermore, we introduce a token-level heterogeneous graph to represent dialogues.To address the second issue, we propose a two-dimensional utterance augmentation strategy, namely editing operation-based incomplete utterance augmentation and LLM-based historical utterance augmentation.The experimental results on three datasets demonstrate that our EO-IUR outperforms previous state-of-the-art (SOTA) baselines in both open-domain and task-oriented dialogue.

Previous studies on multi-party dialogue generation predominantly concentrated on modeling the reply-to structure of dialogue histories, always overlooking the coherence between generated responses and target utterances.To address this issue, we propose a Reinforcement Learning approach emphasizing both Topic and Rhetorical Coherence (RL-TRC).In particular, the topic-and rhetorical-coherence tasks are designed to enhance the model's perception of coherence with the target utterance.Subsequently, an agent is employed to learn a coherence policy, which guides the generation of responses that are topically and rhetorically aligned with the target utterance.Furthermore, three discourse-aware rewards are developed to assess the coherence between the generated response and the target utterance, with the objective of optimizing the policy.The experimental results and in-depth analyses on two popular datasets demonstrate that our RL-TRC significantly outperforms the state-of-the-art baselines, particularly in generating responses that are more coherent with the target utterances.

Dialogue discourse parsing aims to reflect the relation-based structure of dialogue by establishing discourse links according to discourse relations.To alleviate data sparsity, previous studies have adopted multitasking approaches to jointly learn dialogue discourse parsing with related tasks (e.g., reading comprehension) that require additional human annotation, thus limiting their generality.In this paper, we propose a multitasking framework that integrates dialogue discourse parsing with its neighboring task addressee recognition.Addressee recognition reveals the reply-to structure that partially overlaps with the relation-based structure, which can be exploited to facilitate relationbased structure learning.To this end, we first proposed a reinforcement learning agent to identify training examples from addressee recognition that are most helpful for dialog discourse parsing.Then, a task-aware structure transformer is designed to capture the shared and private dialogue structure of different tasks, thereby further promoting dialogue discourse parsing.Experimental results on both the Molweni and STAC datasets show that our proposed method can outperform the SOTA baselines.The code will be available at https://github.com/yxfanSuda/RLTST.

Discourse parsing aims to construct a discourse structure tree to reflect the internal structure of a document. Most existing work only considers parsing documents from the paragraph-level or sentence-level granularity, ignoring the inter-action of different levels of granularity. Therefore, we propose a Multi-Granularity Interaction Method (MGIM) that facilitates discourse parsing through bidirectional information interaction at multi-granularity. We first introduce the structural information at the sentence level to boost paragraph-level parsing, and then use the functional pragmatics information at the paragraph level to guide sentence-level parsing. Moreover, we introduce an auxiliary task, discourse functional pragmatics recognition, to improve sentence-level parsing, which can guide sentence-level discourse tree construction from a macro perspective. Meanwhile, since the research field still lacks data for studying unified Chi-nese discourse parsing, we construct a Unified Chinese Discourse TreeBank UCDTB. Experimental results on both the Chinese UCDTB and the English RST-DT demonstrate the effectiveness of our proposed method.

Conversational discourse parsing aims to construct an implicit utterance dependency tree to reflect the turn-taking in a multi-party conversation. Existing works are generally divided into two lines: graph-based and transition-based paradigms, which perform well for short-distance and long-distance dependency links, respectively. However, there is no study to consider the advantages of both paradigms to facilitate conversational discourse parsing. As a result, we propose a distance-aware multi-task framework DAMT that incorporates the strengths of transition-based paradigm to facilitate the graph-based paradigm from the encoding and decoding process. To promote multi-task learning on two paradigms, we first introduce an Encoding Interactive Module (EIM) to enhance the flow of semantic information between both two paradigms during the encoding step. And then we apply a Distance-Aware Graph Convolutional Network (DAGCN) in the decoding process, which can incorporate the different-distance dependency links predicted by the transition-based paradigm to facilitate the decoding of the graph-based paradigm. The experimental results on the datasets STAC and Molweni show that our method can significantly improve the performance of the SOTA graph-based paradigm on long-distance dependency links.

Hierarchically constructing micro (i.e., intra-sentence or inter-sentence) discourse structure trees using explicit boundaries (e.g., sentence and paragraph boundaries) has been proved to be an effective strategy. However, it is difficult to apply this strategy to document-level macro (i.e., inter-paragraph) discourse parsing, the more challenging task, due to the lack of explicit boundaries at the higher level. To alleviate this issue, we introduce a topic segmentation mechanism to detect implicit topic boundaries and then help the document-level macro discourse parser to construct better discourse trees hierarchically. In particular, our parser first splits a document into several sections using the topic boundaries that the topic segmentation detects. Then it builds a smaller and more accurate discourse sub-tree in each section and sequentially forms a whole tree for a document. The experimental results on both Chinese MCDTB and English RST-DT show that our proposed method outperforms the state-of-the-art baselines significantly.

Implicit discourse relation recognition (IDRR)is a critical task in discourse analysis.Previous studies only regard it as a classification task and lack an in-depth understanding of the semantics of different relations.Therefore, we first view IDRR as a generation task and further propose a method joint modeling of the classification and generation.Specifically, we propose a joint model, CG-T5, to recognize the relation label and generate the target sentence containing the meaning of relations simultaneously.Furthermore, we design three target sentence forms, including the question form, for the generation model to incorporate prior knowledge.To address the issue that large discourse units are hardly embedded into the target sentence, we also propose a target sentence construction mechanism that automatically extracts core sentences from those large discourse units.Experimental results both on Chinese MCDTB and English PDTB datasets show that our model CG-T5 achieves the best performance against several state-of-the-art systems.

Macro discourse relation recognition is an important task of macro discourse analysis. The existing models ignore the micro discourse structure within paragraphs and could not accurately grasp the paragraph semantics. In addition, the traditional pre-trained models only used the representation of the [CLS] token for macro relation classification, lacking more detailed semantic interaction between discourse units. To solve the above issues, we proposed a macro discourse relation recognition model based on Micro Discourse Structure and Self-Interactive Network (MDSSIN) that mines the semantic representation of important parts within a paragraph and enhances semantic interaction between paragraphs. Specially, we first automatically build the micro-structure discourse tree for each paragraph and get the core clause in each paragraph according to nuclearity. Then, we use the self-interactive attention mechanism to capture more detailed semantic interaction between discourse units and between the core clauses of discourse units. Experimental results on Chinese MCDTB show that our method achieves the SOTA performance.