End-to-End Emotion-Cause Pair Extraction based on Sliding Window Multi-Label Learning

Abstract

Emotion-cause pair extraction (ECPE) is a new task that aims to extract the potential pairs of emotions and their corresponding causes in a document. The existing methods first perform emotion extraction and cause extraction independently, and then perform emotion-cause pairing and filtering. However, the above methods ignore the fact that the cause and the emotion it triggers are inseparable, and the extraction of the cause without specifying the emotion is pathological, which greatly limits the performance of the above methods in the first step. To tackle these shortcomings, we propose two joint frameworks for ECPE: 1) multi-label learning for the extraction of the cause clauses corresponding to the specified emotion clause (CMLL) and 2) multi-label learning for the extraction of the emotion clauses corresponding to the specified cause clause (EMLL). The window of multilabel learning is centered on the specified emotion clause or cause clause and slides as their positions move. Finally, CMLL and EMLL are integrated to obtain the final result. We evaluate our model on a benchmark emotion cause corpus, the results show that our approach achieves the best performance among all compared systems on the ECPE task. * Corresponding author c1: Yesterday morning c2: a policeman visited the old man with the lost money, c3: and told him that the thief was caught. c4: The old man was very happy. c5: But he still feels worried, c6: as he doesn't know how to keep so much money {c4-c2, c4-c3, c5-c6} Output: emotion-cause pairs Input: a document Figure 1: An example showing the emotion-cause pair extraction (ECPE) task.

Recognizing Emotion Cause in Conversations

“…Finally, we introduce two challenging sub-tasks that demand complex reasoning, and setup strong baselines to solve the sub-tasks (Experiments). These baselines surpass the performance of several newly introduced complex neural approaches, e.g., ECPE-MLL [13], RankCP [36], and ECPE-2D [12].…”

“…ECPE-MLL [13] introduced a joint multi-label approach for emotion cause pair extraction. Specifically, the joint framework comprises two modules: (i) extraction of causal utterances for the target emotion utterance, (ii) extraction of emotion utterance for a causal utterance.…”

Multi-Granularity Semantic Aware Graph Model for Reducing Position Bias in Emotion-Cause Pair Extraction

“…Due to the problem of the tremendous search space, most existing methods have fully exploited relative position fea-tures to decrease the number of candidate pairs. For instance, ECPE-MLL (Ding et al, 2020b) and SLSN (Cheng et al, 2020) set a fixed size window around a certain clause, and the central clause and other clauses inside the window comprise candidate pairs. However, models heavily relying on the relative position features ignore the distant semantic cues, resulting in poor generalization ability towards position-insensitive data in which the cause clause is not in proximity to the emotion clause.…”

“…Most methods (Ding et al, 2020a;Cheng et al, 2020;Ding et al, 2020b) have set a fixed size window to reduce the number of candidate pairs according to the inherent position bias in the dataset, because of the sparsity of true emotion-cause pairs compared with candidate emotion-cause pairs. Besides, Chen et al (2020b) leveraged the relative position information explicitly in the process of pair representation learning.…”

“…Besides, Chen et al (2020b) leveraged the relative position information explicitly in the process of pair representation learning. The ECPE-MLL model proposed by Ding et al (2020b) is the state-of-the-art method in the ECPE task. An over-reliance on relative position information makes these methods have poor generalization ability towards position-insensitive data.…”