Multi-level Emotion Cause Analysis by Multi-head Attention Based Multi-task Learning (2021)

Abstract: Emotion cause analysis (ECA) aims to identify the potential causes behind certain emotions intext. Lots of ECA models have been designed to extract the emotion cause at the clause level. However in many scenarios only extracting the cause clause is ambiguous. To ease the problemin this paper we introduce multi-level emotion cause analysis which focuses on identifying emotion cause clause (ECC) and emotion cause keywords (ECK) simultaneously. ECK is a more challenging task since it not only requires capturing the specific understanding of the role of eachword in the clause but also the relation between each word and emotion expression. We observethat ECK task can incorporate the contextual information from the ECC task while ECC taskcan be improved by learning the correlation between emotion cause keywords and emotion fromthe ECK task. To fulfill the goal of joint learning we propose a multi-head attention basedmulti-task learning method which utilizes a series of mechanisms including shared and privatefeature extractor multi-head attention emotion attention and label embedding to capture featuresand correlations between the two tasks. Experimental results show that the proposed method consistently outperforms the state-of-the-art methods on a benchmark emotion cause dataset.

## introduction emotion cause analysis (eca), a new field in emotion analysis, attempts to comprehend a given text, and then extracts potential causes that lead to emotion expressions in the text. there has been an increasing interest in the research community on eca more recently since it is widely used in many scenarios. for example, restaurants are eager to find out why people like or dislike their food or services from users' comments or reviews. similarly, instead of gauging public opinions towards policies or political issues just using frequency counts, governments would like to further know the triggering factors of certain attitudes expressed online. eca is a challenging emotion analysis task since it requires a comprehensive understanding of natural languages and the ability to do further inference. restricted by the lack of annotated corpora, early studies used rule-based methods @xcite and crowd-sourcing methods @xcite to tackle this task. until recently, gui et al. @xcite released a reasonable eca corpus, based on which they developed the first deep learning model for the task @xcite , and by following that, various other eca approaches were proposed and achieved superior results @xcite @xcite @xcite . example 1 [x 1 ] : entertainment reporter jucy interviewed that lam raymond and xinyue zhang to get married in 2020. [x 2 ] : also, [x 3 ] : she interviewed a piece of explosive news that the wedding ceremony of tina tang will be held. [x 4 ] : the tina tang's fans were very happy and congratulated her 1 . ( original text：[x 1 ] : 娱乐记者jucy采访到林峰和张馨月在2020年结婚. [x 2 ] : 同时, [x 3 ] : 她 采访到一条唐艺昕将要举办婚礼的爆炸性新闻. [x 4 ] : 唐艺昕的粉丝非常开心并且庆祝她.) most of the existing studies identify which clause contains the emotion cause. example 1 shows a piece of text from sina weibo, in which the emotion word is "happy" and the exact emotion cause of 1 each instance in the eca corpus contains presumably a unique emotion and at least one emotion cause clause. a clause is typically a text segment separated by punctuation marks (e.g., ',', '.', '?', '!', etc.) in the given document. ## conclusions in this paper, we study the multi-task learning approach to identify emotion cause at clause level and word level simultaneously. we propose an effective multi-head attention based multi-task learning network, which utilizes shared-private feature extractor, multi-head attention mechanism and label embedding mechanism to enable two tasks to interact with each other for better learning the task-oriented representations. results on benchmark dataset for eca task demonstrate that our model can effectively extract multi-level emotion causes, and outperform the strong qa-based system and other strong eca baselines by large margins. in the future, we plan to focus on extracting the specific cause(s) in a more accurate granularity for improving emotion cause analysis.