The growing demand for psychological support underscores the lack of high-quality counseling dialogue datasets, particularly in non-English contexts. We propose PGSim, a Path-Guided Simulation framework that mirrors real counseling processes—symptom description, problem identification, cause analysis, strategy planning, and iterative adjustment. PGSim models each user scenario as a fine-grained quadruple {Group, Psychological Problem, Problem Cause, Support Focus} and guides dialogue generation through expert-annotated strategy paths. Real counseling dialogues and expert-edited samples are used to fine-tune two language models: a Dialog Generator for strategy-aligned dialogue creation and a Dialog Modifier for expert-level refinement. After automated and human verification, we construct the Chinese Psychological support Dialogue Dataset (CPsDD), containing 68K dialogues across 13 groups, 16 problems, 13 causes, and 12 support focuses. We further present the Comprehensive Agent Dialogue Support System (CADSS), which integrates profiling, summarization, strategy planning, and empathetic response. Experiments on CPsDD and ESConv demonstrate that CADSS achieves state-of-the-art results on Strategy Prediction and Emotional Support Conversation tasks.

Classroom discourse analysis is critical for tracing cognitive restructuring, yet existing research predominantly focuses on Dialogue Acts (DA), overlooking the deeper dimension of Opinion Evolution (OE).In this paper, we formally define the task of Classroom Opinion Evolution Recognition and introduce the Classroom Opinion Evolution Dataset (COED).Addressing the "Accuracy-Cost-Data" trilemma in real-world educational scenarios and the "overconfidence" failure mode of traditional confidence-based cascading systems on longtail samples, we propose the Multi-task Enhanced Cascade Hybrid (MECH) framework.Grounded in the CODA (Continuous Opinions and Discrete Actions) theory, MECH conceptually translates the "Action-Opinion" dualism into a risk-aware routing mechanism.Instead of relying solely on prediction confidence, this mechanism utilizes high-risk argumentative DA signals derived from multi-task learning to construct a "semantic safety net" effectively routing implicit or ambiguous samples to a Large Language Model for reasoning.Experimental results demonstrate that MECH achieves a state-of-the-art accuracy of 78.55% while reducing API costs by 44.4%.Furthermore, the framework exhibits robustness in few-shot scenarios (using only 20% of data), offering a cost-effective and interpretable solution for large-scale educational dialogue analysis.Our code and data are publicly available at https: //github.com/ywh24284-code/MECH.

In social media, stickers are commonly used alongside text to convey sentiment and intent, yet their abstract nature and embedded text create challenges for multimodal analysis. Current research is limited by these characteristics and the lack of datasets. To address this gap, we introduce MSA-ITEI: Multimodal Sticker Analysis through Image, Text, and underlying Emotions and Intentions. This method effectively generates accurate text descriptions of stickers and handles multimodal tasks in a textual format. Experiments on two multimodal sticker datasets demonstrate that MSA-ITEI outperforms other leading multimodal models and large language models, in tasks such as sticker sentiment analysis, multimodal sentiment analysis, and intent recognition. Our code will be made publicly available.

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.

Thanks to the development of pre-trained sequence-to-sequence (seq2seq) models (e.g., BART), recent studies on AMR parsing often regard this task as a seq2seq translation problem by linearizing AMR graphs into AMR token sequences in pre-processing and recovering AMR graphs from sequences in post-processing. Seq2seq AMR parsing is a relatively simple paradigm but it unavoidably loses structural information among AMR tokens. To compensate for the loss of structural information, in this paper we explicitly leverage AMR structure in the decoding phase. Given an AMR graph, we first project the structure in the graph into an AMR token graph, i.e., structure among AMR tokens in the linearized sequence. The structures for an AMR token could be divided into two parts: structure in prediction history and structure in future. Then we propose to model structure in prediction history via a graph attention network (GAT) and learn structure in future via a multi-task scheme, respectively. Experimental results show that our approach significantly outperforms a strong baseline and achieves performance with 85.5 ±0.1 and 84.2 ±0.1 Smatch scores on AMR 2.0 and AMR 3.0, respectively

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.

Event extraction (EE) is a fundamental task of natural language processing, which aims to recognize the occurrence of events and their arguments in the texts. Most previous works on EE employ the pipelineframework to firstlyextract event triggers, then to extract arguments for the given event triggers. This framework is very simple, but error cascades are inevitable. Besides, in some cases, the extraction of arguments is easier than event triggers. Furthermore, arguments can help in event trigger extraction. In order to reduce error cascades and take advantage of the complementarity between event triggers and arguments, we propose a bidirectional stacked question answering (BSQA) framework for event extraction. Specifically, we first unify event trigger extraction and argument extraction into an independent span detection and classification component. Secondly, for each component, we devise a non-restrictive extraction query, a restrictive extraction query and a set of restrictive classification queries to accomplish the corresponding span detection and classification task. Finally, we stack the two components for event trigger and argument extraction bidirectionally. One direction sequentially extract event triggers and arguments, while the other direction recognizes arguments first, then event triggers. Experimental results on the FewFC corpus show the effectiveness ofourproposed approach.

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.

Current document-level neural machine translation (NMT) systems have achieved remarkable progress with document context. Nevertheless, discourse information that has been proven effective in many NLP tasks is ignored in most previous work. In this work, we aim at incorporating the coherence information hidden within the RST-style discourse structure into machine translation. To achieve it, we propose a document-level NMT system enhanced with the discourse-aware document context, which is named Disco2NMT. Specifically, Disco2NMT models document context based on the discourse dependency structures through a hierarchical architecture. We first convert the RST tree of an article into a dependency structure and then build the graph convolutional network (GCN) upon the segmented EDUs under the guidance of RST dependencies to capture the discourse-aware context for NMT incorporation. We conduct experiments on the document-level English-German and English-Chinese translation tasks with three domains (TED, News, and Europarl). Experimental results show that our Disco2NMT model significantly surpasses both context-agnostic and context-aware baseline systems on multiple evaluation indicators.

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.

Longyin Zhang, Fang Kong, Guodong Zhou. Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). 2021.

Due to its great importance in deep natural language understanding and various down-stream applications, text-level parsing of discourse rhetorical structure (DRS) has been drawing more and more attention in recent years.However, all the previous studies on text-level discourse parsing adopt bottom-up approaches, which much limit the DRS determination on local information and fail to well benefit from global information of the overall discourse.In this paper, we justify from both computational and perceptive points-of-view that the top-down architecture is more suitable for textlevel DRS parsing.On the basis, we propose a top-down neural architecture toward text-level DRS parsing.In particular, we cast discourse parsing as a recursive split point ranking task, where a split point is classified to different levels according to its rank and the elementary discourse units (EDUs) associated with it are arranged accordingly.In this way, we can determine the complete DRS as a hierarchical tree structure via an encoder-decoder with an internal stack.Experimentation on both the English RST-DT corpus and the Chinese CDTB corpus shows the great effectiveness of our proposed top-down approach towards textlevel DRS parsing.

Discourse structure tree construction is the fundamental task of discourse parsing and most previous work focused on English.Due to the cultural and linguistic differences, existing successful methods on English discourse parsing cannot be transformed into Chinese directly, especially in paragraph level suffering from longer discourse units and fewer explicit connectives.To alleviate the above issues, we propose two reading modes, i.e., the global backward reading and the local reverse reading, to construct Chinese paragraph level discourse trees.The former processes discourse units from the end to the beginning in a document to utilize the left-branching bias of discourse structure in Chinese, while the latter reverses the position of paragraphs in a discourse unit to enhance the differentiation of coherence between adjacent discourse units.The experimental results on Chinese MCDTB demonstrate that our model outperforms all strong baselines.

Sentiment classification aims to detect polarity from a piece of text. The polarity is usually positive or negative, and the text genre is usually product review. The challenges of sentiment classification are that it is hard to capture semantic of reviews, and the labeled data is hard to annotate. Therefore, we propose neural co-training to learn the semantic representation of each review using the neural network model, and learn the information from unlabeled data using a co-training framework. In particular, we use the attention-based bi-directional Gated Recurrent Unit (Att-BiGRU) to model the semantic content of each review and regard different categories of the target product as different views. We then use a co-training framework to learn and predict the unlabeled reviews with different views. Experiment results with the Yelp dataset demonstrate the effectiveness of our approach.

Chinese zero pronoun (ZP) resolution plays a critical role in discourse analysis. Different from traditional mention-to-mention approaches, this article proposes a chain-to-chain approach to improve the performance of ZP resolution in three aspects. First, consecutive ZPs are clustered into coreferential chains, each working as one independent anaphor as a whole. In this way, those ZPs far away from their overt antecedents can be bridged via other consecutive ZPs in the same coreferential chains and thus better resolved. Second, common noun phrases (NPs) are automatically grouped into coreferential chains using traditional approaches, each working as one independent antecedent candidate as a whole. That is, those NPs occurring in the same coreferential chain are viewed as one antecedent candidate as a whole, and ZP resolution is made between ZP coreferential chains and common NP coreferential chains. In this way, the performance can be much improved due to the effective reduction of the search space by pruning singletons and negative instances. Third and finally, additional features from ZP and common NP coreferential chains are employed to better represent anaphors and their antecedent candidates, respectively. Comprehensive experiments on the OntoNotes V5.0 corpus show that our chain-to-chain approach significantly outperforms the state-of-the-art mention-to-mention approaches. To our knowledge, this is the first work to resolve zero pronouns in a chain-to-chain way.

In the literature, most of the previous studies on English implicit discourse relation recognition only use sentence-level representations, which cannot provide enough semantic information in Chinese due to its unique paratactic characteristics.In this paper, we propose a topic tensor network to recognize Chinese implicit discourse relations with both sentencelevel and topic-level representations.In particular, besides encoding arguments (discourse units) using a gated convolutional network to obtain sentence-level representations, we train a simplified topic model to infer the latent topic-level representations.Moreover, we feed the two pairs of representations to two factored tensor networks, respectively, to capture both the sentence-level interactions and topiclevel relevance using multi-slice tensors.Experimentation on CDTB, a Chinese discourse corpus, shows that our proposed model significantly outperforms several state-of-the-art baselines in both micro and macro F1-scores.

Coreference resolution plays an important role in text understanding. In the literature, various neural approaches have been proposed and achieved considerable success. However, structural information, which has been proven useful in coreference resolution, has been largely ignored in previous neural approaches. In this paper, we focus on effectively incorporating structural information to neural coreference resolution from three aspects. Firstly, nodes in the parse trees are employed as a constraint to filter out impossible text spans (i.e., mention candidates) in reducing the computational complexity. Secondly, contextual information is encoded in the traversal node sequence instead of the word sequence to better capture hierarchical information for text span representation. Lastly, additional structural features (e.g., the path, siblings, degrees, category of the current node) are encoded to enhance the mention representation. Experimentation on the data-set of the CoNLL 2012 Shared Task shows the effectiveness of our proposed approach in incorporating structural information into neural coreference resolution.

Discourse parsing is a challenging task and plays a critical role in discourse analysis. Since the release of the Rhetorical Structure Theory Discourse Treebank and the Penn Discourse Treebank, the research on English discourse parsing has attracted increasing attention and achieved considerable success in recent years. At the same time, some preliminary research on certain subtasks about discourse parsing for other languages, such as Chinese, has been conducted. In this article, we present an end-to-end Chinese discourse parser with the Connective-Driven Dependency Tree scheme, which consists of multiple components in a pipeline architecture, such as the elementary discourse unit (EDU) detector, discourse relation recognizer, discourse parse tree generator, and attribution labeler. In particular, the attribution labeler determines two attributions (i.e., sense and centering) for every nonterminal node (i.e., discourse relation) in the discourse parse trees. Systematically, our parser detects all EDUs in a free text, generates the discourse parse tree in a bottom-up way, and determines the sense and centering attributions for all nonterminal nodes by traversing the discourse parse tree. Comprehensive evaluation on the Connective-Driven Dependency Treebank corpus from both component-wise and error-cascading perspectives is conducted to illustrate how each component performs in isolation, and how the pipeline performs with error propagation. Finally, it shows that our end-to-end Chinese discourse parser achieves an overall F1 score of 20% with full automation.

Discourse parsing is a challenging task and plays a critical role in discourse analysis.In this paper, we focus on labeling full argument spans of discourse connectives in the Penn Discourse Treebank (PDTB).Previous studies cast this task as a linear tagging or subtree extraction problem.In this paper, we propose a novel constituent-based approach to argument labeling, which integrates the advantages of both linear tagging and subtree extraction.In particular, the proposed approach unifies intra-and intersentence cases by treating the immediately preceding sentence as a special constituent.Besides, a joint inference mechanism is introduced to incorporate global information across arguments into our constituent-based approach via integer linear programming.Evaluation on PDT-B shows significant performance improvements of our constituent-based approach over the best state-of-the-art system.It also shows the effectiveness of our joint inference mechanism in modeling global information across arguments.

In this paper, we propose a Connectivedriven Dependency Tree (CDT) scheme to represent the discourse rhetorical structure in Chinese language, with elementary discourse units as leaf nodes and connectives as non-leaf nodes, largely motivated by the Penn Discourse Treebank and the Rhetorical Structure Theory.In particular, connectives are employed to directly represent the hierarchy of the tree structure and the rhetorical relation of a discourse, while the nuclei of discourse units are globally determined with reference to the dependency theory.Guided by the CDT scheme, we manually annotate a Chinese Discourse Treebank (CDTB) of 500 documents.Preliminary evaluation justifies the appropriateness of the CDT scheme to Chinese discourse analysis and the usefulness of our manually annotated CDTB corpus.

Coreference resolution plays a critical role in discourse analysis.This paper focuses on exploiting zero pronouns to improve Chinese coreference resolution.In particular, a simplified semantic role labeling framework is proposed to identify clauses and to detect zero pronouns effectively, and two effective methods (refining syntactic parser and refining learning example generation) are employed to exploit zero pronouns for Chinese coreference resolution.Evaluation on the CoNLL-2012 shared task data set shows that zero pronouns can significantly improve Chinese coreference resolution.

Personal profile information on social media like LinkedIn.comand Facebook.com is at the core of many interesting applications, such as talent recommendation and contextual advertising.However, personal profiles usually lack organization confronted with the large amount of available information.Therefore, it is always a challenge for people to find desired information from them.In this paper, we address the task of personal profile summarization by leveraging both personal profile textual information and social networks.Here, using social networks is motivated by the intuition that, people with similar academic, business or social connections (e.g.co-major, co-university, and cocorporation) tend to have similar experience and summaries.To achieve the learning process, we propose a collective factor graph (CoFG) model to incorporate all these resources of knowledge to summarize personal profiles with local textual attribute functions and social connection factors.Extensive evaluation on a large-scale dataset from LinkedIn.comdemonstrates the effectiveness of the proposed approach.

Event anaphora resolution plays a critical role in discourse analysis. This paper proposes a tree kernel-based framework for event pronoun resolution. In particular, a new tree expansion scheme is introduced to automatically determine a proper parse tree structure for event pronoun resolution by considering various kinds of competitive information related with the anaphor and the antecedent candidate. Evaluation on the OntoNotes English corpus shows the appropriateness of the tree kernel-based framework and the effectiveness of competitive information for event pronoun resolution.