Make Compound Sentences Simple to Analyze: Learning to Split Sentences for Aspect-based Sentiment Analysis (2024)

Abstract: In the domain of Aspect-Based Sentiment Analysis (ABSA), generative methods have shown promising results and achieved substantial advancements. However, despite these advancements, the tasks of extracting sentiment quadruplets, which capture the nuanced sentiment expressions within a sentence, remain significant challenges. In particular, compound sentences can potentially contain multiple quadruplets, making the extraction task increasingly difficult as sentence complexity grows. To address this issue, we are focusing on simplifying sentence structures to facilitate the easier recognition of these elements and crafting a model that integrates seamlessly with various ABSA tasks. In this paper, we propose Aspect Term Oriented Sentence Splitter (ATOSS), which simplifies compound sentence into simpler and clearer forms, thereby clarifying their structure and intent. As a plug-and-play module, this approach retains the parameters of the ABSA model while making it easier to identify essential intent within input sentences. Extensive experimental results show that utilizing ATOSS outperforms existing methods in both ASQP and ACOS tasks, which are the primary tasks for extracting sentiment quadruplets

## introduction aspect-based sentiment analysis (absa) @xcite refers to the crucial task of understanding sentiments at the aspect-level. this technique identifies specific aspects of entities and evaluates their associated sentiments, providing richer contextual insights. in recent years, absa has progressed beyond simple sentiment classification to tackle more complex structures like sentiment triplets and quadruplets, which include aspect term, aspect category, opinion term and sen-absa input sentence : i swore never to return for a warm beer and mediocre meal. negative i swore never to return for a warm beer and mediocre meal. negative neutral mediocre? neutral? negative (a) beer (c) drinks quality (o) warm (s) negative (a) meal (c) food quality (o) mediocre (s) negative timent polarity for quadruplets. among these, the aspect sentiment quad prediction (asqp) and aspect-category-opinion-sentiment (acos) tasks, which involve predicting comprehensive sentiment quadruplets from a given sentence, are currently the most challenging tasks in absa and are being actively researched. recently, generative methods have been proposed as solutions for predicting quadruplets, gaining significant attention in research due to their simplicity in addressing this problem in an end-to-end manner. @xcite @xcite propose a framework that uses a sequence-to-sequence learning to transform an input sentence into predetermined output formats for predicting quadruplets. despite the state-of-the-art performances, absa models still suffer from the ambiguity of aspectlevel sentiments in complex sentence structure, often due to multiple subjects with different states or context-dependent changes in a single subject. for example, in figure 1 upper, the sentence "i swore never to return for a warm beer and mediocre meal" conveys a negative sentiment with "never to return". however, the opinion word "mediocre" describing the "meal" adds confusion to the overall sentiment of the sentence. as depicted in figure 1 lower, by splitting this sentence into "i swore never to return for a warm beer" and "i swore never to return for a mediocre meal", we can clearly identify the intended sentiment quadruplets. additionally, sentences that involve lists, conjunctions, causal relationships, or storytelling elements are difficult to interpret clearly, as they require complex reasoning and judgment, similar to human thought processes. to address the aforementioned challenges, our research aims to enable absa models to more easily identify intent within simple and clear sentence structures, avoiding confusion in complex and intertwined sentence constructions. motivated by these observations, we propose a model, named aspect term oriented sentence splitter (atoss), which helps to accurately identify quadruplets by simplifying an original, compound sentence into simpler and clearer forms for absa models. moreover, atoss, as a plug-andplay module, can be integrated into each absa model keeping their parameters. in other words, once atoss is pre-trained, it can be immediately applied to any absa model without the need for additional training. specifically, atoss is first optimized via llm distillation, and then aligned with the target absa model's sentence preference (figure 3 ). we first obtain split sentences by prompting with llm and train our model to generate the split sentence given an original sentence. moreover, we address any ambiguities or splitting biases by further tailoring our atoss for the target absa model that will perform quadruplet prediction. to this end, we adopt preference alignment @xcite with sentence pairs of preferred-dispreferred splitting results. as a result, atoss is fine-tuned to be further enhance the target absa model's quadruplet prediction accuracy. our extensive experiments on main aspect quadruplet prediction tasks, including asqp and acos demonstrate that atoss significantly enhances the prediction accuracy of state-of-the-art absa models, including fine-tuned models and prompt-based llms. specifically, atoss effectively reduces the error rates associated with incorrectly predicting aspect terms by facilitating the identification of aspect terms in input sentences for each sentiment quadruplet. furthermore, our atoss splitter can be seamlessly integrated into the inference stage for other absa tasks, highlighting its high level of generalizability. our contributions are summarized as follows: ## related work in this section, we review the existing literature on (1) state-of-the-art approaches to aspect-based sentiment analysis (absa), and (2) recent efforts to distillation of large language models' (llms) remarkable reasoning ability on various tasks into small language models (lms). ## preliminaries in this section, we formally define our target absa tasks and analyze absa models' behavior based on the structural complexity of input sentences. ## methodology in this section, we present a plug-and-play module that splits an input sentence into multiple simple sentences that facilitates absa tasks. our proposed model, named aspect term oriented sentence splitter (atoss), is a small lm trained via knowledge distillation from a teacher llm and further refined to align its output with helpfulness for enhancing the target absa model's accuracy. the overview of atoss is illustrated in figure 3 . ## experiments we conducted our experiments while addressing the following research questions: ## conclusion in this paper, we aim to simplify sentences through strategic splitting, allowing absa models to better understand the inherent structure within input sentences. this splitting strategy, termed aspect-oriented splitting, divides sentences more concisely and clearly while retaining the essential element of the aspect term. based on these points, we demonstrate that atoss, as a plug-and-play module, can be seamlessly integrated with various fine-tuned models as well as prompt-based llms, improving performance without necessitating any updates to the models themselves. our research highlights the significant impact of sentence structure on sentiment analysis, presenting substantial implications for the broader field of absa.