Aspect Sentiment Quad Prediction as Paraphrase Generation (2021)

Abstract: Aspect-based sentiment analysis (ABSA) has been extensively studied in recent years, which typically involves four fundamental sentiment elements, including the aspect category, aspect term, opinion term, and sentiment polarity. Existing studies usually consider the detection of partial sentiment elements, instead of predicting the four elements in one shot. In this work, we introduce the Aspect Sentiment Quad Prediction (ASQP) task, aiming to jointly detect all sentiment elements in quads for a given opinionated sentence, which can reveal a more comprehensive and complete aspect-level sentiment structure. We further propose a novel Paraphrase modeling paradigm to cast the ASQP task to a paraphrase generation process. On one hand, the generation formulation allows solving ASQP in an end-to-end manner, alleviating the potential error propagation in the pipeline solution. On the other hand, the semantics of the sentiment elements can be fully exploited by learning to generate them in the natural language form. Extensive experiments on benchmark datasets show the superiority of our proposed method and the capacity of cross-task transfer with the proposed unified Paraphrase modeling framework.

## introduction as a fine-grained opinion mining problem, aspectbased sentiment analysis (absa) aims to analyse sentiment information at the aspect level @xcite . typically, four fundamental sentiment elements are involved in absa, including 1) aspect category denoting the type of the concerned aspect; 2) aspect term which can be either explicitly or implicitly mentioned in the given text; 3) opinion term which describes the opinion towards the aspect; and 4) sentiment polarity denoting the sentiment class. given an example sentence "the pasta is over-cooked!", the sentiment elements are "food quality", "pasta", "overcooked", and "negative", respectively. due to its broad application scenarios, many research efforts have been made on absa to predict or extract those sentiment elements @xcite @xcite . early studies focus on the prediction of a single element such as aspect term extraction @xcite , aspect category detection @xcite , aspect sentiment classification based on either an aspect category @xcite or an aspect term @xcite . more recent works propose to extract multiple associated sentiment elements at the same time @xcite . for example, @xcite consider the aspect and opinion term pairwise extraction; @xcite propose the aspect sentiment triplet extraction (aste) task to detect the (aspect term, opinion term, sentiment polarity) triplets; @xcite handle the target aspect sentiment detection (tasd) task that jointly detects the aspect category, aspect term, and sentiment polarity. despite their popularity, these absa tasks only attempt to perform partial prediction instead of providing a complete aspect-level sentiment picture, i.e., identifying the four sentiment elements in one shot. to this end, we introduce the aspect sentiment quad prediction (asqp) task, aiming to predict all (aspect category, aspect term, opinion term, sentiment polarity) quads for a given opinionated sentence. this new task compensates for the drawbacks of previous tasks and helps us comprehensively understand user's aspect-level opinions. to tackle asqp, one straightforward idea is to decouple the quad prediction problem into several sub-tasks and solve them in a pipeline manner. however, such multi-stage approaches would suffer severely from error propagation because the overall prediction performance hinges on the accuracy of every step @xcite . besides, the involved sub-tasks, which are usually formulated as either token-level or sequence-level classification problems, underutilize the rich semantic information of the label (i.e., the meaning of sentiment elements to be predicted) since they treat the labels as number indices during training. intuitively, the aspect term "pasta" is unlikely to be coupled with the aspect category "service general" due to the large semantic gap between them. but such information cannot be suitably utilized in those classification-type methods. inspired by recent success in formulating various nlp tasks as text generation problems @xcite @xcite , we propose to tackle asqp in a sequenceto-sequence (s2s) manner in this paper. on one hand, the sentiment quads can be predicted in an end-to-end manner, alleviating the potential error propagation in the pipeline solutions. on the other hand, the rich label semantic information could be fully exploited by learning to generate the sentiment elements in the natural language form. exploiting generation modeling for the asqp task mainly faces two challenges: (i) how to linearize the desired sentiment information so as to facilitate the s2s learning? (ii) how can we utilize the pretrained models for tackling the task, which is a common practice now for solving various absa tasks @xcite )? to handle these two challenges, we propose a novel para-phrase modeling paradigm, which transforms the asqp task as a paraphrase generation problem @xcite . specifically, our approach linearizes the sentiment quad into a natural language sentence as if we were paraphrasing the input sentence and highlighting its major sentiment elements. for example, we can transform the aforementioned sentiment quad (food quality, pasta, over-cooked, negative) to a sentence "food quality is bad because pasta is over-cooked". such a linearized target sequence, paired with the input sentence "the pasta is over-cooked!" can then be used to learn the mapping function of a generation model. we can seamlessly utilize the large pretrained generative models such as t5 @xcite by fine-tuning with such input-target pairs. therefore, the rich label semantics of the sentiment elements is naturally fused with the rich knowledge of the pretrained models in the form of natural sentences, rather than directly treating the desired sentiment quad text sequence as the generation target @xcite . we summarize our contributions as follows: 1) we study a new task, namely aspect sentiment quad prediction (asqp) in this work and introduce two datasets with sentiment quad annotations for each sample, aiming to analyze more comprehensive aspect-level sentiment information. 2) we propose to tackle asqp as a paraphrase generation problem, which can predict the sentiment quads in one shot and fully utilize the semantics information of natural language labels. 3) extensive experiments show that the proposed paraphrase modeling is effective to tackle asqp as well as other absa tasks, outperforming the previous state-of-the-art models in all cases. 4) the experiment also suggests that our paraphrase method naturally facilitates the knowledge transfer across related tasks with the unified framework, which can be especially beneficial in the low-resource setting. foot_1 2 related work absa has been extensively studied in recent years where the main research line is the extraction of the sentiment elements. early studies focus on the prediction of a single element such as extracting the aspect term @xcite @xcite , detecting the mentioned aspect category @xcite , and predicting the sentiment polarity, given either an aspect term @xcite @xcite or an aspect category @xcite . some works further consider the joint detection of two sentiment elements, including the pairwise extraction of aspect and opinion term @xcite @xcite ; the prediction of aspect term and its corresponding sentiment polarity @xcite @xcite @xcite ; and the co-extraction of aspect category and sentiment polarity @xcite . more recently, triplet prediction tasks are proposed in absa, aiming to predict the sentiment elements in triplet format. @xcite propose the aspect sentiment triplet extraction (aste) task, which has received lots of attention @xcite @xcite . @xcite introduce the target aspect sentiment detection (tasd) task, aiming to predict the aspect category, aspect term, and sentiment polarity simultaneously, which can handle the case where the aspect term is implicit expressed in the given text (treated as "null") @xcite . built on top of those tasks, we introduce the aspect sentiment quad prediction problem, aiming to predict the four sentiment elements in one shot, which can provide a more detailed and comprehensive sentiment structure for a given text. adopting pretrained transformer-based models such as bert @xcite has become a common practice for tackling the absa problem. especially, many absa tasks benefit from appropriately utilizing the pretrained models. @xcite transform the aspect sentiment classification task as a language inference problem by constructing an auxiliary sentence. @xcite and @xcite formulate multiple absa tasks as a reading comprehension task to fully utilize the knowledge of the pre-trained model. very recently, there are some attempts on tackling absa problem in a s2s manner, either treating the class index @xcite or the desired sentiment element sequence @xcite as the target of the generation model. in this work, we propose a paraphrase modeling that can better utilize the knowledge of the pre-trained model via casting the original task to a paraphrase generation process. ## experimental setup dataset we build the asqp datasets based on semeval shared challenges @xcite . the annotations of the opinion term and aspect category are derived from @xcite and @xcite respectively. we align the samples from these two sources and merge the annotations with the same aspect term in each sentence as the anchor. we further conduct some additional annotations: every sample is annotated by two human annotators and the conflict cases would be checked. finally, we obtain two datasets, namely rest15 and rest16, where each data instance contains a review sentence with one or multiple sentiment quads. we further split 20% of the data from the training set as the validation set. the statistics is summarized in evaluation metrics we employ f1 scores as the main evaluation metrics. a sentiment quad prediction is counted as correct if and only if all the predicted elements are exactly the same as the gold labels. we also report the precision (pre) and recall (rec) scores for the asqp task. ## conclusions we introduce a new absa task, namely aspect sentiment quad prediction (asqp) in this paper, aiming to provide a more comprehensive aspectlevel sentiment picture. we propose a novel para-phrase modeling paradigm that tackles the original quad prediction as a paraphrase generation problem. experiments on two datasets show its superiority compared with previous state-of-the-art models. we also demonstrate that the proposed method provides a unified framework that can be easily adapted to handle other absa tasks as well. extensive analysis are conducted to understand the characteristics of the proposed method. we can notice that asqp remains a challenging problem and worth further exploring. we look forward future work could propose better methods to tackle such a difficult absa task for fully revealing the aspect-level opinion information.