Enhanced Multi-Channel Graph Convolutional Network for Aspect Sentiment Triplet Extraction (2022)

Abstract: Aspect Sentiment Triplet Extraction (ASTE) is an emerging sentiment analysis task. Most of the existing studies focus on devising a new tagging scheme that enables the model to extract the sentiment triplets in an end-to-end fashion. However, these methods ignore the relations between words for ASTE task. In this paper, we propose an Enhanced Multi-Channel Graph Convolutional Network model (EMC-GCN) to fully utilize the relations between words. Specifically, we first define ten types of relations for ASTE task, and then adopt a biaffine attention module to embed these relations as an adjacent tensor between words in a sentence. After that, our EMC-GCN transforms the sentence into a multi-channel graph by treating words and the relation adjacent tensor as nodes and edges, respectively. Thus, relation-aware node representations can be learnt. Furthermore, we consider diverse linguistic features to enhance our EMC-GCN model. Finally, we design an effective refining strategy on EMC-GCN for word-pair representation refinement, which considers the implicit results of aspect and opinion extraction when determining whether word pairs match or not. Extensive experimental results on the benchmark datasets demonstrate that the effectiveness and robustness of our proposed model, which outperforms state-of-the-art methods significantly.

## introduction aspect sentiment triplet extraction (aste) is a new variant of aspect-based sentiment analysis (absa). the aste task aims to extract aspect sentiment triplets from a sentence, and each triplet contains three elements, namely aspect term, opinion term and their associated sentiment. in figure 1 , an example illustrates the definition of aste. figure 1 : a sentence with its dependency tree is given to illustrate aste task. in the triplet set, aspect terms, opinion terms are highlighted in blue and yellow, respectively. the positive sentiment polarity is highlighted in red, while the negative in green. to extract the triplets, previous studies have developed three types of approaches. pipeline approaches @xcite independently extract elements of the triplet. however, such techniques ignore the interaction between them, and potentially lead to error propagation and extra costs. to utilize the associations among the multiple subtasks, @xcite and @xcite formulate the aste task as a multi-turn machine reading comprehension (mrc) problem and design a model based on bert to jointly train multiple subtasks. meanwhile, some efforts devote to extracting the triplets in an end-to-end framework @xcite @xcite @xcite , which is constructed mainly by designing new tagging scheme. although previous works have achieved significant fruits, there exists still several challenges. here, two questions arise naturally for aste task by our observations. 1) how to utilize various relations between words to help aste task? take figure 1 as an example; for word pair ("gourmet", "food"), "gourmet" and "food" belong to the same aspect term "gourmet food". likewise, for word pair ("food", "delicious"), "food" is an opinion target of "delicious" and is endowed with a positive sentiment polarity. therefore, to effectively extract the aspect term "gourmet food", we expect that "gourmet" can obtain the information of "food" and vice versa. to judge the sentiment polarity of the aspect term, information of the opinion term "delicious" should be delivered to "gourmet food". in short, we need to learn task-dependent word representations based on the relations between words. 2) how to utilize the linguistic features to help aste task? first, we observe that aspect terms "gourmet food" and "service" are nouns, while opinion terms "delicious" and "poor" are adjectives. thus, the word pair composed of a noun and an adjective tend to form aspect-opinion pair. second, from the syntactic dependency tree in figure 1 , different dependency types exist in word pairs. for instance, "gourmet" and "food" comprise a compound noun because the dependency type between them is "compound", while "food" is the nominal subject of "delicious" due to the type "nsubj". thus, these dependency types can help not only the extraction of aspect and opinion terms but also their matching foot_1 . in addition, we consider the tree-based and relative position distances which describe the relevance of two words. in this paper, we propose a novel architecture, enhanced multi-channel graph convolutional network model (emc-gcn), to answer the aforementioned questions. firstly, we utilize a biaffine attention module to model the relation probability distribution between words in a sentence and use a vector to represent it. each dimension in the vector corresponds to a certain relation type. to this end, we can derive a relation adjacency tensor from a sentence. furthermore, our emc-gcn transforms the sentence to a multi-channel graph by treating words and the relation adjacency tensor as nodes and edges, respectively. in order to learn precise relation between words, we impose relation constraint on the relation adjacency tensor. secondly, to exploit linguistic features, including lexical and syntactic information, we obtain the part-of-speech combination, syntactic dependency type, tree-based distance and relative position distance of each word pair in the sentence. similarly, we respectively transform these features into the edges for the multi-channel graphs to further enhance our model. although part of linguistic fea-tures has been applied in other tasks @xcite @xcite , to the best of our knowledge, they are rarely used in aste task. it is non-trivial to explore various linguistic features, adapt and apply them to aste in a novel way. thirdly, inspired by the classifier chains method @xcite in multi-label classification task, we devise an effective refining strategy. our strategy considers the implicit results of aspect and opinion extraction for word-pair representation refinement when judging whether word pairs match. our contributions are highlighted as follows: ## related work traditional sentiment analysis tasks are sentencelevel @xcite or document-level @xcite oriented. in contrast, aspect-based sentiment analysis (absa) is an aspect or entity oriented fine-grained sentiment analysis task. the most three basic subtasks are aspect term extraction (ate) @xcite @xcite @xcite , aspect sentiment classification (asc) @xcite @xcite @xcite and opinion term extraction (ote) @xcite @xcite . the studies solve these tasks separately and ignore the dependency between these subtasks. therefore, some efforts devoted to couple the two subtasks and proposed effective models to jointly extract aspect-based pairs. this kind of work mainly has two tasks: aspect and opinion term co-extraction (aote) @xcite @xcite @xcite and aspect-sentiment pair extraction (aspe) @xcite @xcite . most recently, peng et al. (2020) first proposed the aste task and developed a two-stage pipeline framework to couple together aspect extraction, aspect sentiment classification and opinion extraction. to further explore this task, @xcite transformed aste to a machine reading comprehension problem and utilized the shared bert encoder to obatin the triplets after multiple stages decoding. another line of research focuses on designing a new tagging scheme that makes the model can extract the triplets in an endto-end fashion @xcite @xcite @xcite . for instance, @xcite proposed a positionaware tagging scheme, which solves the limitations related to existing works by enriching the expressiveness of labels. @xcite proposed a grid tagging scheme, similar to table filling @xcite , to solve this task in an end-to-end manner. @xcite converted aste task into a generative formulation. however, these approaches generally ignore the relations between words and linguistic features which effectively promote the triplet extraction. ## proposed framework in this section, we elaborate on the details of emc-gcn. the overview of the emc-gcn framework pos w i and w j of the word pair (w i , w j ) respectively belong to an aspect term and an opinion term, and they form aspectopinion pair with positive/neutral/negative sentiment. 8 neu 9 neg 10 ⊥ no above relations between word pair (w i , w j ). ## conclusions and future work in this paper, we propose an emc-gcn architecture for aste task. to exploit relations between words, we first devise a multi-channel graph structure for modeling different relation type of each word pair. then, we utilize graph convolution operation over all channels to learn relation-aware node representations. furthermore, we consider linguistic features to enhance the gcn-based model. finally, we design an effective refining strategy on emc-gcn for better extracting triplets. extensive experiments on benchmark datasets show that our emc-gcn model consistently outperforms all baselines. in the future, we will analyse roles of linguistic features and effects of their combinations.