Refine, Align, and Aggregate: Multi-view Linguistic Features Enhancement for Aspect Sentiment Triplet Extraction (2024)

Abstract: Aspect Sentiment Triplet Extraction (ASTE) aims to extract the triplets of aspect terms, their associated sentiment and opinion terms. Previous works based on different modeling paradigms have achieved promising results. However, these methods struggle to comprehensively explore the various specific relations between sentiment elements in multi-view linguistic features, which is the prior indication effect for facilitating sentiment triplets extraction, requiring to align and aggregate them to capture the complementary higher-order interactions. In this paper, we propose Multi-view Linguistic Features Enhancement (MvLFE) to explore the aforementioned prior indication effect in the “Refine, Align, and Aggregate” learning process. Specifically, we first introduce the relational graph attention network to encode the word-pair relations represented by each linguistic feature and refine them to pay more attention to the aspect-opinion pairs. Next, we employ the multi-view contrastive learning to align them at a fine-grained level in the contextual semantic space to maintain semantic consistency. Finally, we utilize the multi-semantic cross attention to capture and aggregate the complementary higher-order interactions between diverse linguistic features to enhance the aspect-opinion relations. Experimental results on several benchmark datasets show the effectiveness and robustness of our model, which achieves state-of-the-art performance.

## introduction aspect sentiment triplet extraction (aste) is a new variant of aspect-based sentiment analysis (absa) @xcite , which is an information extraction style task to identify all sentiment triplets from a review to explain what the targeted aspects are, how their sentiment polarities are and why they have such polarities (i.e., opin- the cheese pizza is crispy and tasty , but the ambience is indeed dull . syntactic dependency type: the cheese pizza is crispy and tasty , but the ambience is indeed dull . relative position distance: the cheese pizza is crispy and tasty , but the ambience is indeed dull . part-of-speech relation: triplets: {(cheese pizza, crispy, positive), (cheese pizza, tasty, positive), (ambience, indeed dull, negative)} sentence: the cheese pizza is crispy and tasty , but the ambience is indeed dull . positive positive negative figure 1 : the prior indication effect of multi-view linguistic features for aste. it reveals various specific relations between matched aspect and opinion terms, also within the terms themselves in different views of linguistic features, which facilitate the extraction of sentiment triplets from input sentences. in the above triplet set, aspect and opinion terms are marked in red and blue, with positive sentiment in green and negative in brown. "sdd/rpd:n" denotes distance between two words is n. early work adopts the straightforward solution to decompose the aste task into several absa subtasks to separately extract sentiment elements of triplets with a two-stage pipeline approach @xcite . to exploit the association among multiple subtasks, @xcite ; @xcite ; @xcite transform the aste task into the machine reading comprehension (mrc) paradigm with diverse task-specific queries, which jointly train multiple subtasks but pipeline inference. obviously, these methods potentially lead to the well-known error propagation problem. to alleviate this problem, most of recent studies focus on jointly extracting the sentiment triplets in an end-to-end fashion @xcite @xcite . some efforts formulate the aste task as a text generation problem to generate the sentiment triplets in one shot @xcite @xcite . however, these approaches are constrained in capturing reciprocity among the sentiment elements. therefore, @xcite ; @xcite ; @xcite construct the word-pair relations by designing a novel grid tagging scheme @xcite to capture such association. @xcite ; @xcite further leverage the syntax dependency or additional linguistic features to enrich the word-pair representations. nevertheless, these methods primarily fuse the linguistic features and the contextual features at a shallow level, which neglect to align and aggregate them to capture the complementary higher-order interactions. consequently, they fail to exploit the prior indication effect of multi-view linguistic features for aste. naturally, two questions arise regarding the prior indication effect by our observations. q1: what is the prior indication effect for aste? we interpret it as there exist various specific relations between matched aspect-opinion pairs, also within aspect or opinion terms themselves in different views of linguistic features, which facilitate the sentiment triplets extraction. take figure 1 as an example. first, in the view of syntactic dependency type, we observe that the aspect term is the nominal subject of the opinion term due to the "nsubj" type. besides, "cheese" and "pizza" comprise an aspect term by the "compound" type, while "indeed" is the adverbial modifier of "dull" so they combine as an opinion term. thus, these dependency types facilitate not only the extraction of aspect and opinion terms but also their pairing. second, in the view of part-of-speech relation, we find that aspect terms are nouns while opinion terms are adjectives. hence, the word pair with the "nn-jj" part-of-speech combination tends to form an aspect-opinion pair. in addition, we notice that the matched aspect and opinion terms are closer in terms of the distance of syntactic dependency and relative position. q2: how to effectively exploit the prior indication effect to help aste? we argue that exploring it requires the "refine, align, and aggregate" learning process. first, there exist numerous irrelevant word-pair relations represented by multi-view linguistic features to aste (e.g., relations between commas and other words). therefore, we need to refine the multi-view linguistic features to focus on the relations of the matched aspect-opinion pairs. second, different views of linguistic features represent various relation types for the same aspect-opinion pairs, so they require to be aligned to maintain semantic consistency. finally, since the prior indication effect of a single linguistic feature is one-sided for aste, it is crucial to capture and aggregate the complementary higher-order interactions between diverse linguistic features. motivated by the above observations, we propose multi-view linguistic features enhancement (mvlfe) to explore the prior indication effect to enhance the word-pair relations. firstly, we introduce the relational graph attention network to encode the word-pair relations represented by each linguistic feature as multi-channel edge features, which are normalized as doubly stochastic matrices to facilitate the aggregation and updating of word nodes, preventing over-smoothing across multi-layer learning. moreover, edge features are updated by the edge refining strategy which considers the implicit results of aspect and opinion terms extraction to support their matching. secondly, inspired by image-text matching for cross-modal alignment, we employ the multi-view contrastive learning to align diverse aspect-opinion relations represented by different linguistic features in the semantic space which obtained by the biaffine transformation of contexts to maintain semantic consistency. finally, we utilize the multi-semantic cross attention to aggregate the complementary higherorder interactions between diverse linguistic features to enhance aspect-opinion relations. in summary, the key contributions are as follows: aspect-based sentiment analysis (absa) (pontiki et al., 2014) has received wide attention in recent years. early works focus on identifying the query -----parser ℎ5 ℎ3 tasty food but poor service bert ℎ1 ℎ2 ℎ4 mlp biaffine mechanism graph construction and initialization relative position distance syntactic dependency type part-of-speech relation relational graph attention network refine syntactic dependency distance multi-view contrastive learning multi-semantic cross attention n n -j j nn-j j n n -j j n n -j j sd d :1 sdd:1 sd d: 2 sd d: 2 rp d :1 rpd: 1 rp d: 4 rp d: 2 a m o d amod prediction and triplets decoding transform node update (eq.1) edge refinement (eq.5) node aggregate (eq.1) dsn (eq.3~4) coefficients (eq.2) -th relational graph attention network layer (eq.6) ground truth (eq.12) (eq.17) ∧ others tasty-food pair alignment amod nn-jj sdd:1 rpd:1 poor-service pair alignment amod nn-jj sdd:1 rpd:1 multi-view contrastive learning ... multi-head cross attention (eq.13) / / / key value add&ln (eq.14) → / → / → / → add&fc (eq.15) multi-semantic cross attention single sentiment element, i.e., aspect term extraction (ate) @xcite @xcite , opinion term extraction (ote) @xcite @xcite and aspect sentiment classification (asc) @xcite @xcite . to further explore the interactions among sentiment elements, some efforts are devoted to coupling the individual subtasks, i.e., aspect-opinion pair extraction (aope) @xcite @xcite and aspect extraction and sentiment classification (aesc) @xcite . based on their works, @xcite introduces the more challenging aste task to present a near-complete solution for absa. subsequent works address aste task by different modeling paradigms: sequence tagging @xcite , grid tagging (also known as table-filling) @xcite @xcite @xcite @xcite , mrc-based methods with diverse task-specific queries @xcite @xcite and generative methods @xcite @xcite @xcite @xcite . however, these approaches generally ignore the prior indication effect of multi-view linguistic features for aste, which facilitates the triplet extraction. ## methodology in this section, we first introduce the task definition and the adopted tagging scheme. then we elaborate on the details of our mvlfe model and the training objective. the overall architecture is illustrated in figure 2 . ## conclusion in this paper, we propose a mvlfe architecture to explore the prior indication effect of multi-view linguistic features for aste task in the "refine, align, and aggregate" learning process. we first devise relational graph attention network to encode and refine diverse linguistic features to attend the aspect-opinion pairs. then we employ multiview contrastive learning for semantic alignment. moreover, we utilize multi-semantic cross attention to aggregate them to capture the complementary higher-order interactions. extensive experiments on benchmark datasets show the effectiveness and robustness of our mvlfe model, which consistently outperforms all baselines on aste task as well as several subtasks. in the future, we plan to extend our model for information extraction.