Emotion-cause Pair Research Articles

Multimodal Emotion-Cause Pair Extraction with Holistic Interaction and Label Constraint

Multimodal Emotion-Cause Pair Extraction with Holistic Interaction and Label Constraint

Overview and future prospects of emotion-cause pair extraction

Overview and future prospects of emotion-cause pair extraction

Unifying emotion-oriented and cause-oriented predictions for emotion-cause pair extraction

Emotion-cause pair extraction (ECPE) is an extraction task aiming to simultaneously identify the emotions and causes from the text without emotion annotations. Let ci and cj represent the emotion clause and the cause clause of a document, respectively, and we can predict one from the other and vice versa. Previous works fail to take advantage of this bidirectional opportunity. We refer to the prediction from ci to cj, i.e., ci→cj, as an emotion-oriented cause prediction (EoCP) task and the prediction from cj to ci, i.e., cj→ci, as a cause-oriented emotion prediction (CoEP) task. After redefining the ECPE task, we propose a novel unified architecture for ECPE, which incorporates EoCP and CoEP as cells and unifies them into a single-chain architecture. Additionally, we redefine emotion-cause pair extraction as a closed-loop structure detection problem to alleviate the mismatch between emotion and cause clauses. To enhance the training of the architecture, we provide a procedure for estimating the confidence of the extraction system for its emotion-cause pairs. We demonstrate the superiority of our proposed model through extensive experiments on two public datasets, achieving a new state-of-the-art performance. Furthermore, our method particularly achieves significant improvements in multiple emotion-cause pair extraction.

MV-SHIF: Multi-view symmetric hypothesis inference fusion network for emotion-cause pair extraction in documents

Emotion-cause pair extraction (ECPE) is a challenging task that aims to automatically identify pairs of emotions and their causes from documents. The difficulty of ECPE lies in distinguishing valid emotion-cause pairs from many irrelevant ones. Most previous methods have primarily focused on utilizing multi-task learning to extract semantic information solely from documents without explicitly encoding the relations between clauses. We propose a new approach that incorporates textual entailment paradigm aiming to infer the entailment relationship between the original document as the premise and the clauses or pairs described as the hypothesis. Our approach designs label-view hypothesis templates to improve ECPE by filtering out irrelevant emotion and cause clauses. Furthermore, we formulate candidate emotion-cause pairs as hypothesis statements, and define explicit multi-view symmetric templates to capture the emotion-cause relation semantics. The text entailment recognition for ECPE is finally implemented by fusing multi-view semantic information using a simplified capsule network. Our proposed model achieves state-of-the-art performance on ECPE compared to previous baselines. More importantly, this work demonstrates a novel effective way of applying the textual entailment paradigm to ECPE or clause-level causal discovery by designing multi-view hypothesis inference and information fusion.

Emotion-cause pair extraction via knowledge-driven multi-classification and graph-based position embedding

Emotion-cause pair extraction via knowledge-driven multi-classification and graph-based position embedding

A machine reading comprehension model with counterfactual contrastive learning for emotion-cause pair extraction

A machine reading comprehension model with counterfactual contrastive learning for emotion-cause pair extraction

A knowledge-guided graph attention network for emotion-cause pair extraction

Emotion-Cause Pair Extraction (ECPE) is a research objective focused on identifying and extracting all emotion-clause and cause-clause pairs from unannotated emotional text. Traditional methodologies have predominantly employed attention mechanisms or joint learning techniques for feature information interaction. However, these approaches often overlook the aggregation of features under the guidance of external knowledge. To enhance performance in addressing this ECPE challenge, we propose a novel knowledge-guided graph attention network, i.e., GAT-ECPE. This model chiefly relies on an interclause dependency graph as a guiding principle. By employing this knowledge-guided graph attention network, we can proficiently combine semantic and structural information between clauses. In addition, an interpair possibility graph, derived from the outcomes of subtasks, is integrated as an additional guiding principle. As such, we are able to aggregate features between clause pairs, thereby facilitating interaction between multiple tasks. Extensive experiments were conducted to validate our proposed model, and the obtained results demonstrate its superiority when compared to 12 considered baselines. In terms of performance metrics, our model achieves an F1 score F1 of 74.92% and a recall R of 77.52%. These values significantly outperform those achieved by state-of-the-art approaches, indicating the effectiveness and superiority of our GAT-ECPE.

Improving Representation With Hierarchical Contrastive Learning for Emotion-Cause Pair Extraction

Improving Representation With Hierarchical Contrastive Learning for Emotion-Cause Pair Extraction

From Extraction to Generation: Multimodal Emotion-Cause Pair Generation in Conversations

From Extraction to Generation: Multimodal Emotion-Cause Pair Generation in Conversations

Hierarchical Shared Encoder with Task-specific Transformer Layer Selection for Emotion-Cause Pair Extraction

Hierarchical Shared Encoder with Task-specific Transformer Layer Selection for Emotion-Cause Pair Extraction

Emotion-cause pair extraction with bidirectional multi-label sequence tagging

Emotion-cause pair extraction with bidirectional multi-label sequence tagging

SEEC and CHASE: An emotion-cause pair-oriented approach and conversational dataset with heterogeneous emotions for empathetic response generation

Empathetic Response Generation (ERG) in dialog agents has gained tremendous attention in the recent past. Among various methods used to impart empathy in the generated response, the contemporary one is using the emotion cause. Previous works of ERG have 2 major flaws: (1) usage of the emotion detected using the entire conversation to generate the empathetic response is heavily misleading as the speaker might be going through a completely different emotion in his last utterance. This erroneous emotion detection, in turn, leads to incorrect detection of the cause of that emotion. (2) Usage of entire utterance has proven to be inefficient in the cause extraction task. Consequently, existing works fail to capture multiple emotion clauses and their corresponding emotion causes for efficient ERG. In our work, we introduce a new dataset,11The dataset will be made available at https://drive.google.com/drive/folders/1ozYeuACo_He75w0XoNWZntMZ3FkiTHPB?usp=sharing.CHASE, which is a compilation of conversations extracted from various plays to highlight the above-mentioned change in emotion and how one can show empathy in such a case. In this dataset, we use Dr. Brene Brown’s 22https://brenebrown.com/. notion of empathy on how to administer this change in emotion in the response and still sound empathetic by generating golden responses for each conversation. To address the aforementioned flaws, we also propose a model, SEEC, that utilizes the Emotion-Cause Pair Extraction task on the conversation to find various {emotion clause, cause clause} pairs and use these to impart empathy appropriately to the responses. Our qualitative and quantitative results prove the efficiency in generating enhanced empathetic responses of both SEEC and CHASE.

Class imbalance mitigation: A select-then-extract learning framework for emotion-cause pair extraction

The class imbalance problem arises in the current end-to-end models for the emotion-cause pair extraction (ECPE) task, and a large number of negative clause pairs interfere model to depict the characteristics of emotion and cause. Most existing methods adopt a window-constrained strategy to limit the relative distance between candidate clauses to a smaller range to filter negative pairs. However, those methods ignore the semantic association between distant clauses, and using an artificial window size is imperfect due to the inherent ambiguity and delicacy of emotion. Inspired by the centrality principle of discourse, in this paper we propose a select-then-extract framework for the ECPE task, where the core clauses are first selected from the original document as candidate emotion and cause clauses, and then emotions, causes and emotion-cause pairs are jointly extracted from the candidate clauses. Specifically, our model includes two main components: core clause selector and emotion-cause pairs extractor. For the Core Clause Selector, we introduce the extractive document summarization (EDS) task and present a multi-granularity semantic awareness cooperative graph model (MGCOG) to extract core clauses from documents. Compared to the previous methods, the core clause selector is more effective for alleviating the category imbalance because the global causal cues and context can be captured by learning global keywords and inter-clause relationships, which determines more efficient candidate clauses. For Emotion-Cause Pairs Extractor, we put forward a multi-task learning model to jointly extract emotions, causes and emotion-cause pairs from the selected core clauses. Here, a multi-head attention is used to further model the relationship between candidate clauses, and a co-predictor is designed for assigning scores to all possible emotion-cause pairs. We further investigate the pipeline and the joint model under the select-then-extract framework, and show that the experimental results on benchmark datasets are consistently superior to the comparative baseline models. Extensive ablation experiments also verify the effectiveness of each component.

Multimodal Emotion-Cause Pair Extraction in Conversations

Conversation is an important form of human communication and contains a large number of emotions. It is interesting to discover emotions and their causes in conversations. Conversation in its natural form is multimodal. Many studies have been carried out on multimodal emotion recognition in conversations, yet there is still a lack of work on multimodal emotion cause analysis. In this work, we introduce a new task named Multimodal Emotion-Cause Pair Extraction in Conversations, aiming to jointly extract emotions and the corresponding causes from conversations reflected in multiple modalities (i.e., text, audio and video). We accordingly construct a multimodal conversational emotion cause dataset, Emotion-Cause-in-Friends, which contains 9,794 multimodal emotion-cause pairs among 13,619 utterances in the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Friends</i> sitcom. We benchmark the task by establishing two baseline systems including a heuristic approach considering inherent patterns in the location of causes and emotions and a deep learning approach that incorporates multimodal features for emotion-cause pair extraction, and conduct the human performance test for comparison. Furthermore, we investigate the effect of multimodal information, explore the potential of incorporating commonsense knowledge, and perform the task under both Static and Real-time settings.

A Reinforcement Learning Based Two-Stage Model for Emotion Cause Pair Extraction

Recently, many efforts have been devoted to promoting the Emotion-Cause Pair Extraction (ECPE) task, as jointly extracting emotions and their causes is considered more helpful than only identifying the emotions in many applications. Among the existing efforts, end-to-end approaches are getting popular as the main trend, while others like pipeline models have been overlooked due to their potential issues of cascading errors. Nevertheless, the advantages of the pipeline models, such as logically dividing a complicated task into multiple easier subtasks, are underestimated and not well exploited. Moreover, the existing end-to-end approaches fail to capture the implicit co-occurrence or exclusion patterns between multiple pairs of emotions and causes since they are extracted independently. In view of these limitations, we propose a novel two-stage model to address the ECPE task and incorporate reinforcement learning (RL) to tackle the cascading error issue. In particular, our two-stage model first detects emotion clauses and then recognizes cause clauses for each detected emotion clause sequentially. By representing the error of each decision as an explicit reward, our model clearly knows how the error at each stage affects the final performance, hence the model can adjust itself for better performance. Furthermore, the sequential prediction enables our model to use the results achieved in the previous stages as auxiliary information in the subsequent stages. Extensive experiments on the benchmark dataset demonstrate the effectiveness of our proposed two-stage model, and the ablation comparison shows the promising effect of reducing cascading errors by incorporating RL.

ECPEC: Emotion-Cause Pair Extraction in Conversations

Conversational sentiment analysis (CSA) and emotion-cause pair extraction (ECPE) tasks have attracted increasing attention in recent years. The former aims to predict the sentiment states of speakers in a conversation, and the latter is about extracting emotion-cause clauses in a document. However, one drawback of CSA is that it cannot model the causal reasoning among emotion and neutral utterances from different speakers. In this work, we propose a new task: emotion-cause pair extraction in conversations (ECPEC), which aims to extract pairs of emotional utterances and corresponding cause utterances in conversations. The utterance-level ECPEC task is more challenging since the distance between emotion and cause utterances is larger than that of the clause-level ECPE task. To this end, we build a novel dataset ConvECPE and propose a specifically designed two-step framework for the new ECPEC task. Experimental results on ConvECPE dataset demonstrate the feasibility of the ECPEC task as well as the effectiveness of our framework.

Semantic Decision Internal-Attention Graph Convolutional Network for End-to-End Emotion-Cause Pair Extraction

Emotion-cause pair extraction is an emergent natural language processing task; the target is to extract all pairs of emotion clauses and corresponding cause clauses from unannotated emotion text. Previous studies have employed two-step approaches. However, this research may lead to error propagation across stages. In addition, previous studies did not correctly handle the situation where emotion clauses and cause clauses are the same clauses. To overcome these issues, the authors first use a multitask learning model that is based on graph from the perspective of sorting, which can simultaneously extract emotion clauses, cause clauses and emotion-cause pairs via an end-to-end strategy. Then the authors propose to convert text into graph structured data, and process this scenario through a unique graph convolutional neural network. Finally, the authors design a semantic decision mechanism to address the scenario in which there are multiple emotion-cause pairs in a text.

A graph attention network utilizing multi-granular information for emotion-cause pair extraction

Emotion-cause pair extraction (ECPE) aims to extract emotion and cause clauses underlying a text and pair them. Most of the recent approaches to this problem adopt deep neural networks to model the inter-clause dependency, without making full use of information at word level and document level. In this paper, we propose a model that utilizes multi-granular information, including word-level, clause-level, and document-level information, to facilitate emotion-cause pair extraction. Our model consists of two fully-connected clause graphs, including emotion graph and cause graph, and graph attention is applied to learn emotion-specific and cause-specific representations which are then used to generate document-level representations. To exploit the mutual indication between emotion and cause, a cross-graph co-attention mechanism is proposed. Moreover, external knowledge of emotional and causal cues is incorporated to provide word-level indicative information for emotion-cause pair extraction. The proposed model is tested on both Chinese [1] and English [2] datasets, and the results show that our model achieves the state-of-the-art performance on both datasets.

A Consistent Dual-MRC Framework for Emotion-cause Pair Extraction

Emotion-cause pair extraction (ECPE) is a recently proposed task that aims to extract the potential clause pairs of emotions and its corresponding causes in a document. In this article, we propose a new paradigm for the ECPE task. We cast the task as a two-turn machine reading comprehension (MRC) task, i.e., the extraction of emotions and causes is transformed to the task of identifying answer clauses from the input document specific to a query. This two-turn MRC formalization brings several key advantages: First, the QA manner provides an explicit pairing way to identify causes specific to the target emotion; second, it provides a natural way of jointly modeling the emotion extraction, the cause extraction, and the pairing of emotion and cause; and third, it allows us to exploit the well-developed MRC models. Based on the two-turn MRC formalization, we propose a dual-MRC framework to extract emotion-cause pairs in a dual-direction way, which enables a more comprehensive coverage of all pairing cases. Furthermore, we propose a consistent training strategy for the second-turn query, so the model is able to filter the errors produced by the first turn at inference. Experiments on two benchmark datasets demonstrate that our method outperforms previous methods and achieves state-of-the-art performance. All the code and data of this work can be obtained at https://github.com/zifengcheng/CD-MRC .

Emotion Prediction Oriented Method With Multiple Supervisions for Emotion-Cause Pair Extraction

Emotion-cause pair extraction (ECPE) task aims to extract all the pairs of emotions and their causes from an unannotated emotion text. The previous works usually extract the emotion-cause pairs from two perspectives of emotion and cause. However, emotion extraction is more crucial to the ECPE task than cause extraction. Motivated by this analysis, we propose an end-to-end emotion-cause extraction approach oriented toward emotion prediction (EPO-ECPE), aiming to fully exploit the potential of emotion prediction to enhance emotion-cause pair extraction. Considering the strong dependence between emotion prediction and emotion-cause pair extraction, we propose a synchronization mechanism to share their improvement in the training process. That is, the improvement of emotion prediction can facilitate the emotion-cause pair extraction, and then the results of emotion-cause pair extraction can also be used to improve the accuracy of emotion prediction simultaneously. For the emotion-cause pair extraction, we divide it into genuine pair supervision and fake pair supervision, where the genuine pair supervision learns from the pairs with more possibility to be emotion-cause pairs. In contrast, fake pair supervision learns from other pairs. In this way, the emotion-cause pairs can be extracted directly from the genuine pair, thereby reducing the difficulty of extraction. Experimental results show that our approach outperforms the 13 compared systems and achieves new state-of-the-art performance.