Tagging-Assisted Generation Model with Encoder and Decoder Supervision for Aspect Sentiment Triplet Extraction (2023)

Abstract: ASTE (Aspect Sentiment Triplet Extraction) has gained increasing attention. Recent advancements in the ASTE task have been primarily driven by Natural Language Generation-based (NLG) approaches. However, most NLG methods overlook the supervision of the encoder-decoder hidden representations and fail to fully utilize the semantic information provided by the labels to enhance supervision. These limitations can hinder the extraction of implicit aspects and opinions. To address these challenges, we propose a tagging-assisted generation model with encoder and decoder supervision (TAGS), which enhances the supervision of the encoder and decoder through multiple-perspective tagging assistance and label semantic representations. Specifically, TAGS enhances the generation task by integrating an additional sequence tagging task, which improves the encoder’s capability to distinguish the words of triplets. Moreover, it utilizes sequence tagging probabilities to guide the decoder, improving the generated content’s quality. Furthermore, TAGS employs a self-decoding process for labels to acquire the semantic representations of the labels and aligns the decoder’s hidden states with these semantic representations, thereby achieving enhanced semantic supervision for the decoder’s hidden states. Extensive experiments on various public benchmarks demonstrate that TAGS achieves state-of-the-art performance.

## introduction aspect sentiment triplet extraction (aste) aims to extract sentiment triplets from a sentence, i.e., aspect: the aspect term represents an explicit mention of a discussed target, opinion: the mentioned comment terms/phrases, sentiment: sentiment polarity of the aspect, holding significant potential in downstream research and applications. unlike sentence sentiment classification, aste emphasizes the explanation for sentiments, explicitly highlighting the causes of sentiments and the entities to which they are attached. this task involves addressing challenges such as the diversity of emotional expressions and the complexity of linguistic contexts. for instance, in the sentence "food wise, it's ok but a bit pricey for what you get considering the restaurant isn't a fancy place," three sentiment triplets can be extracted: (food, ok, neutral), (food, pricey, neutral), and (restaurant, isn't a fancy place, neutral). ## problem statement the input of the aste task is a sentence @xmath0, x 2 , ..., x n }, where each x i represents a word and n is the maximum length of the sentence. the goal of the aste task is to generate a set of sentiment triplets @xmath1, o, s) k } ## methodology fig. 1 shows our proposed tags method. tags comprises two modules, an empowering generation through sequential tagging module (egst) and a label-driven semantic alignment (ldsa) module. egst leverages sequence tagging task to enhance the generation model in three aspects: multitask learning, guided generation, and result optimization. ldsa utilizes a label self-decoding process to obtain the semantic representation of labels and aligns the decoder's hidden states to the semantic representation during training, thereby achieving enhanced semantic supervision for the decoder's hidden states. ## conclusion in this work, we introduce a generation model called tags, which enhances the supervision of both the encoder and decoder through multipleperspective tagging assistance and label semantic representations. specifically, tags utilizes sequence tagging to enhance the generation model in multiple aspects: multitask learning, guided generation, and result optimization. additionally, tags employs a label self-decoding process to obtain semantic representations of labels and aligns the decoder's hidden states with these representations, thereby providing enhanced semantic supervision for the decoder's hidden states. these two components enhance the supervision of the encoder and decoder's hidden states, resulting in improved generation quality. extensive experiments demonstrate that our method significantly advances the state-of-the-art on benchmark datasets.