Smart “Chef”: Verifying the Effect of Role-based Paraphrasing for Aspect Term Extraction (2023)

Abstract: We tackle Aspect Term Extraction (ATE), a task of automatically extracting aspect terms from sentences. The current Pretrained Language Model (PLM) based extractors have achieved significant improvements. They primarily benefit from context-aware encoding. However, a considerable number of sentences in ATE corpora contain uninformative or low-quality contexts. Such sentences frequently act as “troublemakers” during test. In this study, we explore the context-oriented quality improvement method. Specifically, we propose to automatically rewrite the sentences from the perspectives of virtual experts with different roles, such as a “chef” in the restaurant domain. On this basis, we perform ATE over the paraphrased sentences during test, using the well-trained extractors without any change. In the experiments, we leverage ChatGPT to determine virtual experts in the considered domains, and induce ChatGPT to generate paraphrases conditioned on the roles of virtual experts. We experiment on the benchmark SemEval datasets, including Laptop-domain L14 and Restaurant-domain R14-16. The experimental results show that our approach effectively recalls the inconspicuous aspect terms like “al di la”, although it reduces the precision. In addition, it is proven that our approach can be substantially improved by redundancy elimination and multi-role voting. More importantly, our approach can be used to expand the predictions obtained on the original sentences. This yields state-of-the-art performance (i.e., F1-scores of 86.2%, 89.3%, 77.7%, 82.7% on L14 and R14-16) without retraining or fine-tuning the baseline extractors.

## introduction ate is a natural language processing task, which aims to extract aspect terms from sentences @xcite . the aspect term refers to a word, phrase or named entity depicting a certain domain-specific attribute. for example, the text span "al di la" in ( 1 ) is specified as a restaurantdomain aspect term because it appears as the sign of an italian trattoria. the current studies leverage plms as backbones to construct ate models (extractors), including bert @xcite , bart @xcite and t5 @xcite as mentioned in section 2. they yield significant improvements, compared to conventional neural networks like @xcite . the advantage is primarily attributed to the strong perception ability on noteworthy contexts, as well as context-aware representation learning ability. however, such extractors frequently suffer from uninformative or low-quality contexts. for example, the context "love" in @xcite is uninformative for recognizing "al di la". by contrast, the substitution containing a knowledge-rich context makes it easier to recognize aspect terms, such as the case in (2). accordingly, we propose a chatgpt-based edition fictionalization (chef) method to assist the current plm-based extractors. chef acts as a domain-specific virtual expert with different roles to rewrite sentences, with the aim to refine contexts of potential aspect terms. chatgpt is utilized for both expert generation and sentence rewriting. a series of post-processing methods are coupled with chef, including redundancy elimination, synonym replacement and multi-role voting. we experiment on the benchmark datasets l14 and r14-16 @xcite @xcite . the well-trained bert base and pst @xcite (sota) are adopted in the experiments. during test, they perform over the rewritten sentences by chef, without retraining and fine-tuning. the test results demonstrate the effectiveness of chef in recalling aspects and expanding the predictions. ## related work context-aware encoding contributes to ate. it brings domain-specific contextual information into token-level representations. therefore, cnn @xcite and bert @xcite @xcite are widely used for ate due to the abilities of convolving local contexts or absorbing attentive contextual information. their expanded versions decnn @xcite @xcite and bert-pt @xcite @xcite are generally adopted as backbones in the subsequent studies. in addition, bert is utilized as the pedestal for context-aware encoding in a series of more complex tasks, including aspect-sentiment triplet extraction (aste) @xcite @xcite @xcite and mrc-based aste @xcite . recently, the generative framework is introduced into the studies of aste, and accordingly bart @xcite and t5 @xcite are used. they are constructed with the transformer encoder-decoder architecture in the seq2seq paradigm, where attentive contextual information absorption is conducted on both sides. ## approach we aim to provide knowledge-rich and high-quality sentences for ate. specifically, we use chef to rewrite sentences, and feed the rewritten sentences into an extractor to predict aspect terms. finally, we combine the aspect terms which are respectively extracted from the original and rewritten sentences. ## conclusion we utilize chatgpt to rewrite sentences with different roles of domain-specific experts, so as to provide informative and high-quality contexts for plm-based ate models. experiments show that the proposed method contributes to the salvage of the neglected aspect terms, and applies more to the short and long sentences. in the future, we will use the rewritten sentences for contrastive learning. to reduce the reliance on chatgpt, we will develop an offline context rewriting method by knowledge distillation and domain-specific pretraining.