Span-level Aspect-based Sentiment Analysis via Table Filling (2023)

Abstract: In this paper, we propose a novel span-level model for Aspect-Based Sentiment Analysis (ABSA), which aims at identifying the sentiment polarity of the given aspect. In contrast to conventional ABSA models that focus on modeling the word-level dependencies between an aspect and its corresponding opinion expressions, in this paper, we propose Table Filling BERT (TF-BERT), which considers the consistency of multi-word opinion expressions at the span-level. Specially, we learn the span representations with a table filling method, by constructing an upper triangular table for each sentiment polarity, of which the elements represent the sentiment intensity of the specific sentiment polarity for all spans in the sentence. Two methods are then proposed, including table-decoding and table-aggregation, to filter out target spans or aggregate each table for sentiment polarity classification. In addition, we design a sentiment consistency regularizer to guarantee the sentiment consistency of each span for different sentiment polarities. Experimental results on three benchmarks demonstrate the effectiveness of our proposed model.

## introduction aspect-based sentiment analysis @xcite absa is a fine-grained branch of sentiment analysis, which aims at recognizing the sentiment polarity of the given aspect in the sentence. for example, given the sentence "boot time is super fast, around anywhere from 35 seconds to 1 minute" and the aspect "boot time", the opinion expression corresponding to the aspect is "super fast" so that the sentiment polarity of the aspect "boot time" is positive. recently, several methods @xcite @xcite @xcite @xcite @xcite have been proposed to exploit the connections between the given aspect and its corresponding opinion expressions in the task of absa. @xcite introduces recurrent neural networks (rnns) to retrieve the aspect-related information by fusing the aspect with its contextualized information in the sentence. furthermore, @xcite ; @xcite propose to model the distance dependency between the aspect and the distant opinion expressions with attention mechanisms @xcite . to better leverage the syntax information in the absa task, some recent studies @xcite @xcite adopt graph neural networks (gnns) over the dependency trees. moreover, @xcite ; @xcite generate dynamic aspect-specific trees for every sentence-aspect pair to learn the relationships between the aspect words and opinion words. despite the improvements achieved by the methods above in the task of absa, they take opinion expressions as single words and rely on attention mechanisms to learn the dependency between them, which gives rise to two issues: 1) word-level dependency ignores the semantics of the entire opinion expressions. 2) sentiment conflicts may exist in the multi-word opinion expressions since the sentiment polarities predicted over each word can be different @xcite . an example is shown in figure 1 , in which the opinion expression to the aspect "food" is "delicious but expensive". if the model only captures the dependency either between "food" and "delicious" or between "food" and "expensive", it would get the wrong sentiment polarity of positive/negative. even if all word-level dependencies have been built, the sentiment conflicts between "delicious" and "expensive" may still confuse the model. in principle, if the opinion words "delicious", "but" and "expensive" can be considered simultaneously, it is easier for the model to predict the correct sentiment polarity as neutral. to address the above issues, in this paper, we propose a span-level absa model and introduce the span-level dependencies, which consider all possible continuous subsequences of a sentence, namely spans, and build connections with the given aspect. while being more flexible, spans are of variable lengths, which inevitably pose significant challenges for standard mechanisms such as attention or gcn. in this paper, we take a different approach with a table filling method to learn span representations naturally and efficiently in the absa task, inspired by the success of table filling methods in the relational triple extraction (rte) task @xcite . based on the span representations, two methods for sentiment polarity classification are introduced, which consist of a table-decoding method and a table-aggregation method. specifically, we construct an upper triangular table for each sentiment polarity, of which each element represents the sentiment intensity of the specific sentiment polarity for the corresponding span. for the table-decoding method, inspired by @xcite , we first select all possible opinion expressions according to the sentiment intensity in the table for each sentiment polarity. next, we predict the sentiment polarities with the span representations, which are aggregated according to the sentiment intensities of the extracted target spans. for the table-aggregation method, we directly aggregate all sentiment polarity tables to get the probability of the specific sentiment polarity. additionally, in order to guarantee the sentiment consistency of each span with respect to different sentiment polarities, we design a sentiment consistency regularizer to prevent the same span from getting high sentiment intensities on different tables at the same time. in summary, the main contributions of the work are as follow: ## methodology in this section, we first show the problem definition in section 3.1, then describe the table filling strategy in section 3.2, followed by the model details in section 3.3. ## conclusion in this paper, we propose a novel table filling based model tf-bert for the absa task, which maintains an upper triangular table for each sentiment polarity and the elements in the table denotes the sentiment intensity of the specific sentiment polarity for all spans in the sentence. specifically, we first append the given aspect to the sentence and use the bert model to encode the augmented sentence to get the aspect-specific representations. then, we construct the span features and generate a table for each sentiment polarity. finally, we utilize two methods to obtain the sentiment probability distribution. additionally, to ensure the sentiment consistency of the same span across different tables, we adopt a sentiment consistency regularizer on the generated tables. extensive experiments on three benchmarks demonstrate the effectiveness of our tf-bert model.