Enhancing Cross-target Stance Detection with Transferable Semantic-Emotion Knowledge (2020)

Abstract: Stance detection is an important task, which aims to classify the attitude of an opinionated text towards a given target. Remarkable success has been achieved when sufficient labeled training data is available. However, annotating sufficient data is labor-intensive, which establishes significant barriers for generalizing the stance classifier to the data with new targets. In this paper, we proposed a Semantic-Emotion Knowledge Transferring (SEKT) model for cross-target stance detection, which uses the external knowledge (semantic and emotion lexicons) as a bridge to enable knowledge transfer across different targets. Specifically, a semantic-emotion heterogeneous graph is constructed from external semantic and emotion lexicons, which is then fed into a graph convolutional network to learn multi-hop semantic connections between words and emotion tags. Then, the learned semantic-emotion graph representation, which serves as prior knowledge bridging the gap between the source and target domains, is fully integrated into the bidirectional long short-term memory (BiLSTM) stance classifier by adding a novel knowledge-aware memory unit to the BiLSTM cell. Extensive experiments on a large real-world dataset demonstrate the superiority of SEKT against the state-of-the-art baseline methods.

## introduction the goal of stance detection is to automatically predict the attitude (i.e., favor, against, or none) of an opinionated text towards a given target @xcite . recently, deep learning methods, such as convolutional neural network (cnn) and long short-term memory (lstm) @xcite , have dominated the study of stance detection. impressive stance detection performances have been achieved when a large * corresponding authors: {lixutao, yym}@hit.edu.cn number of labeled samples are available. however, obtaining rich annotated data is a time-consuming and labor-intensive process. conventional stance detection methods are struggling to cope well with the data across targets. this motivates the studies of cross-target stance detection @xcite , which infers the attitude of the destination target by leveraging a large amount of annotated data from the source target. so far, several previous studies have been conducted for cross-target stance detection @xcite @xcite . these methods leverage either common words or concept-level knowledge shared by different targets to bridge the knowledge gap across the different targets. such models suffer from two issues when they are applied to cross-target stance detection in practice. first, stance detection often involves analyzing the texts from social media that are short and informal, making it difficult to extract domain-independent common words shared by different targets from the training data. second, users may express their stance towards a given target in an implicit way. thus, the existing conceptlevel based methods may fail to distinguish implicit stance-carrying terms and context information. to alleviate the aforementioned issues, we propose a semantic-emotion knowledge transferring (sekt) model for cross-domain stance detection, which leverages external knowledge as a bridge between source and destination targets. the proposed model is motivated by the observation that the data with different targets usually shares certain common external knowledge that can be transferred from the source to destination targets. first, we build a semantic-emotion graph (se-graph) from semantic-related and emotion-related lexicons, which incorporates external knowledge from both word-level and concept-level. in se-graph, each node is either a word or an emotion tag, and the edge between each node pair indicates the cooccurrences of the two nodes in the lexicons. second, a graph convolutional network (gcn) @xcite is employed to learn the graph representation that captures the multi-hop semantic connections between words or emotion tags rather than one-hop connection. third, we extend the standard bidirectional lstm (bilstm) classifier to fully integrate the external knowledge (segraph) by adding an additional knowledge-aware memory unit (kamu) to the lstm cell. kamu is capable of controlling the influence of the external knowledge in learning the hidden state of each word. the main contributions of this paper can be summarized as follows: ## error analysis to better understand the limitations of sekt, we additionally carry out an analysis of the errors made by sekt. specifically, we randomly select 100 instances that are incorrectly predicted by sekt from the expanded semeval-2016 dataset. we revealed several reasons for the classification errors, which can be divided into the following categories. first, sekt fails to classify some sentences that contain latent opinions or require deep comprehension. for example, for the sentence "i guess nbc does not like to hear the truth. [f avor] " with a target "donald trump", sekt tends to predict an incorrect against stance. this is because the sekt model cannot learn the implicit relationship between nbc * and trump, which is not acquirable from the semantic-emotion lexicons. the * national broadcasting company second error category is caused by special hashtags with implicit meanings. for example, sekt cannot correctly predict the stance for the sentence "the gift that keeps on giving. #makeitstop #semst" @xcite . this may be because the information in the sentence is not sufficient enough such that sekt cannot capture the sequential patterns of the stance-related words. it suggests that certain data augmentation strategy needs to be devised in the future so as to capture the sequential patterns between stance-related words from short texts. ## conclusion in this paper, we proposed a semantic-emotion knowledge transferring (sekt) model for crosstarget stance classification, which used the external knowledge from semantic and emotion lexicons as commonsense knowledge to bridge the gap across different targets. specifically, we first built a se-graph from semantic and emotion lexicons, which leveraged external knowledge from both word-level and concept-level. second, the gcn was employed to learn the graph representation that captured multi-hop semantic connections between words or emotion tags. third, we extend the standard bilstm classifier to fully integrate the external knowledge by adding a novel knowledge-aware memory unit to the bilstm cell. the experimental results demonstrated that the sekt model significantly outperformed the state-of-the-art methods for cross-target stance detection.