Transition-based Directed Graph Construction for Emotion-Cause Pair Extraction (2020)

Abstract: Emotion-cause pair extraction aims to extract all potential pairs of emotions and corresponding causes from unannotated emotion text. Most existing methods are pipelined framework, which identifies emotions and extracts causes separately, leading to a drawback of error propagation. Towards this issue, we propose a transition-based model to transform the task into a procedure of parsing-like directed graph construction. The proposed model incrementally generates the directed graph with labeled edges based on a sequence of actions, from which we can recognize emotions with the corresponding causes simultaneously, thereby optimizing separate subtasks jointly and maximizing mutual benefits of tasks interdependently. Experimental results show that our approach achieves the best performance, outperforming the state-of-the-art methods by 6.71% (p<0.01) in F1 measure.

## introduction emotion-cause pair extraction (ecpe) is a new task to identify emotions and the corresponding causes from unannotated emotion text @xcite . this involves several subtasks, including 1) extracting pair components from input text, e.g., emotion detection and cause detection; 2) combining all the elements of the two sets into emotion-cause pairs and eliminating the pairs that do not exist a causal relationship. for the former subtask, a clause can be categorized into "emotion", which usually contains an emotion keyword to express specific sentiment polarity, or "cause", which contains the reason or stimuli of an observed emotion. then, the set of all possible emotioncause pairs will be fed into the second subtask to determine the relationship. in general, it is an essential issue in emotion analysis since it provides * * co-corresponding authors a new perspective to investigate how emotions are provoked, expressed, and perceived. figure 1 shows an example of ecpe, and the text is segmented into three clauses. in this instance, only the second clause and the third clause hold an emotion causality, where "i lost my phone while shopping" is the cause of emotion " i feel sad now". thus, the extracted results of this sample should be {i lost my phone while shopping, i feel sad now}. the goal of ecpe is to identify all the pairs that have emotion causality in an emotion text. however, from both theoretical and computational perspectives, due to the inherent ambiguity and subtlety of emotions, it is hard for machines to build a mechanism for understanding emotion causality like human beings. previous approaches mostly focused on detecting the causes towards the given annotation of emotions, which was followed by most of the recent studies in this field @xcite @xcite @xcite @xcite . nevertheless, it suffers that emotions must be annotated before extracting the causes, which limits the applications in real-world scenarios. towards this issue, @xcite presented a new task to extract emotion-cause pairs from the unannotated text. however, they followed a pipelined framework, which models emotions and causes separately, rather than joint decoding. hence, to overcome the drawback of error propagation may occur in existing methods. ideally, the emotion-cause structure should be considered as an integral framework, including representation learning, emotion-cause extraction, and reasoning. to this end, we transform the ecpe problem into a procedure of directed graph construction, from which emotions and the corresponding causes can be extracted simultaneously based on the labeled edges. the directed graph is constructed by designing a novel transition-based parsing model, which incrementally creates the labeled edges according to the causal relationship between the connected nodes, through a sequence of defined actions. in this process, the emotion detection, cause detection, and their causality association can be jointly learned through joint decoding, without differentiating subtask structures, so that the maximum potential of information interaction between emotions and causes can be exploited. besides, the proposed model processes the input sequence in a psycholinguistically motivated left to right order, consequently, reducing the number of potential pairs needed to be parsed and leading to speed up (if all clauses are connected by cartesian products, the time complexity will be o(n 2 )). regarding feature representation, bert @xcite is used to produce the deep and contextualized representation for each clause, and lstms @xcite are performed to capture long-term dependencies among input sequences. in addition, action history and relative distance information between the emotion-cause pairs are also encoded to benefit the task. to summarize, our contribution includes: ## task definition the formal definition of emotion-cause pair extraction is given in @xcite . briefly, 1 the code and dataset are available at: https:// github.com/hlt-hitsz/transecpe given a piece of emotion text d n 1 = (c 1 , c 2 , . . . , c n ), which consists of several manually segmented clauses. the goal of ecpe is to output all potential pairs where exist emotion causality: ## our approach we present a new framework aimed at integrating the emotion-cause pair extraction into a procedure of parsing-like directed graph construction. the proposed framework incrementally constructs and labels the graph from input sequences, scoring partially segmented results using rich non-local features. figure 2 shows the overall architecture of the proposed framework. in the following, we first introduce how to construct the directed graph based on a novel transition-based system, then the details of feature representation will be described. ## related work different from the traditional emotion analysis, which aims to identify emotion categories in text. emotion cause extraction (ece) reveals the essential information about what causes a certain emotion and why there is an emotional change. it is a more challenging task due to the inherent ambiguity and subtlety of emotion expressions. lee et al. ( 2010 ) first defined the emotion cause extraction as a word-level extraction task. they manually constructed a dataset from the academia sinica balanced chinese corpus and generalized a series of linguistics rules based on the dataset. based on this setting, there are some studies have been exploited for this task such as rule-based methods @xcite @xcite and machine learning methods @xcite . @xcite converted the task from word-level to clause-level due to a clause may be the most appropriate unit to detect causes, and extracted causes using six groups of manually constructed linguistic cues. by following this task setting, @xcite extended the rule-based features to 25 linguistics cues, then trained classifiers on svm and crfs to detect causes. @xcite released a new chinese emotion cause dataset collected from sina city news foot_2 and proposed a multi-kernel based method to identify emotion causes. following this corpus, @xcite proposed a learning to re-rank method based on a series of emotion-dependent and emotion-independent features. recently, inspired by the success of deep learning architecture, some studies focused on identifying emotion causes with well designed neural network and attention mechanism @xcite @xcite @xcite . all of the above studies extracted emotion causes rely on the given emotion annotations, which limits the application in real-world scenarios due to the expensive annotations. targeting this problem, @xcite proposed a novel task based on ece, namely emotion-cause pair extraction (ecpe), which aims at extracting emotions and the corresponding causes from unannotated emotion text. however, they followed a pipelined framework which first detects emotions and causes with individual learning frameworks, then performed emotion-cause pairing to eliminate the unmatched pairs, leading to a drawback of error propagation. in this work, we design a novel transition-based model to extract emotions and causes simultaneously to maximize the mutual benefits of subtasks, thus alleviating the drawback of error propagation. transition-based system is usually designed to model the chunk-level relation in a sentence for dependency parsing @xcite @xcite . apart from its application in dependency parsing, transition-based method has also achieved great success in other natural language processing tasks, such as word segmentation @xcite , information extraction @xcite , disfluency detection @xcite and nested mention recognition @xcite . to the best of our knowledge, this is the first work which extracts the emotion-cause pairs in an end-to-end manner. ## conclusion in this paper, we present a novel transition-based framework to extract emotion-cause pairs as a procedure of directed graph construction. instead of previous pipelined approaches, the proposed framework incrementally outputs the emotion-cause pairs as a single task, thereby the interdependence between emotions and causes can be exploited more effectively. experimental results on a standard benchmark demonstrate the superiority and robustness of the proposed model compared to a number of competitive methods. in the future, one possible direction is creating complete graphs with their nodes being input clauses to achieve full coverage. besides, graph neural network-based (kipf and welling, 2016) methods are also worth investigating to model the relations among nodes for this task.