S eq2 P ath: Generating Sentiment Tuples as Paths of a Tree (2022)

Abstract: Aspect-based sentiment analysis (ABSA) tasks aim to extract sentiment tuples from a sentence. Recent generative methods such as Seq2Seq models have achieved good performance by formulating the output as a sequence of sentiment tuples. However, the orders between the sentiment tuples do not naturally exist and the generation of the current tuple should not condition on the previous ones. In this paper, we propose Seq2Path to generate sentiment tuples as paths of a tree. A tree can represent “1-to-n” relations (e.g., an aspect term may correspond to multiple opinion terms) and the paths of a tree are independent and do not have orders. For training, we treat each path as an independent target, and we calculate the average loss of the ordinary Seq2Seq model over paths. For inference, we apply beam search with constrained decoding. By introducing an additional discriminative token and applying a data augmentation technique, valid paths can be automatically selected. We conduct experiments on five tasks including AOPE, ASTE, TASD, UABSA, ACOS. We evaluate our method on four common benchmark datasets including Laptop14, Rest14, Rest15, Rest16. Our proposed method achieves state-of-the-art results in almost all cases.

## conclusions in this paper, we propose seq2path, a novel parallel generative framework for absa. the previous seq2seq based method formulates the output as a sequence that has two main drawbacks: the order and the dependence. instead, our seq2path formulates the output as a tree and generates sentiment tuples as paths of the tree. seq2path can learn the precise conditional transition probability for token generation, by training with the loss of ordinary seq2seq averaged over paths. during inference, we apply beam search with constrained decoding. a discriminative token is also introduced to automatically select the valid paths. experiments show that our model achieves state-of-the-art on aope, aste, tasd, uabsa, acos across common datasets including laptop14, rest14, rest15, rest16 in almost all cases. in the future, we plan to extend our method to other structure prediction tasks in nlp such as information extraction tasks, event extraction and nested named entity recognition.