ZHIXIAOBAO at S em E val-2022 Task 10: Apporoaching Structured Sentiment with Graph Parsing (2022)

Abstract: This paper presents our submission to task 10, Structured Sentiment Analysis of the SemEval 2022 competition. The task aims to extract all elements of the fine-grained sentiment in a text. We cast structured sentiment analysis to the prediction of the sentiment graphs following (Barnes et al., 2021), where nodes are spans of sentiment holders, targets and expressions, and directed edges denote the relation types between them. Our approach closely follows that of semantic dependency parsing (Dozat and Manning, 2018). The difference is that we use pre-trained language models (e.g., BERT and RoBERTa) as text encoder to solve the problem of limited annotated data. Additionally, we make improvements on the computation of cross attention and present the suffix masking technique to make further performance improvement. Substantially, our model achieved the Top-1 average Sentiment Graph F1 score on seven datasets in five different languages in the monolingual subtask.

## introduction semeval 2022 task 10 is a structured sentiment analysis task, aiming to predict all of the opinion tuples in a text. each opinion o is a tuple (t, h, e, p), where h is a holder who expresses a polarity p towards a target t through a sentiment expression e. in practical, the task of structured sentiment analysis can help machines understand how people perceive ideas, policy etc. this paper describes the system developed by the team zhixiaobao for semeval-2022 task 10. we follow the work of @xcite to cast the task as dependency graph parsing problem. the predicted opinion tuples are denoted by a directed graph, @xmath0, e), for each sentence. as shown in figure 1 , all tokens in a sentence are presented as nodes and there are directed edges between the nodes to represent their relations. each node in v can point to multiple nodes, and can have multiple incoming edges too. sentiment expressions are regarded as roots in the structured sentiment graph (e.g., the token "5" and "don't" in figure 1 ). notice that not all nodes connect to the other nodes (e.g., the token "give" in figure 1 ). the isolated tokens are the none-sentiment elements in the sentence, thus we should be able to predict the edge type of "null" in our model. original edge types are defined as holder, target, and expression. following the work of @xcite , we also tried the "+inlabel" style of definition, where none-"null" edge types consist of holder in , holder out , target in , target out , expression in , expression out . foot markers in, out denotes the in-span and out-span edges respectively. for example, the edge from "5" to "some" belongs to holder out , while the edge from "some" to "others" belongs to holder in . as demonstrated in previous work @xcite , formulating the task as a graph structure prediction problem is superior to that of solving it by the span extraction and relation prediction approaches. the former can better extract overlapping spans than the latter. thus, our model mainly follows the solution of dependency parsing to directly predict between-word relations. the model consists of a text encoder to extract contextual features of tokens, and a classifier to predict edges between each pair of tokens. a bilinear or biaffine cross attention is applied in the classifier layer to make multiplicative interactions between the features of a pair of tokens. in our model, pre-trained language models (e.g., bert and roberta) are used as the text encoder. we discover that finetuning the pre-trained language model brings huge enhancements in our experiments. in addition, as the meaning of in-span and out-span edge types are totally different, we leverage two cross attention for in-span edge types and out-span edge types prediction respectively. we also present a suffix mask-some others give the new umuc 5 stars, don't believe them. ing technique to reduce noise in the data. these techniques greatly improve the performance of our model compared with the original dependency parsing model. our model ranked 1st out of 32 participating teams on monolingual subtask and got the highest average f1 score on 4 datasets. ## task description semeval task 10 focuses on predicting all elements of the structured sentiment in a text, represented by opinion tuples (t, h, e, p), where h is a holder who expresses a polarity p towards a target t through a sentiment expression e. the evaluation is on seven datasets in five languages, the statistics of which are shown in norec fine @xcite is the largest structured sentiment multi-domain dataset with professional reviews in norwegian. multib eu and multib ca @xcite are hotel reviews datasets in basque and catalan, respectively. opener en and opener es @xcite are polarity-enhanced datasets with customer reviews in spanish and english respectively. mpqa @xcite annotates news articles in english from the world press. finally, ds unis @xcite is an annotated english reviews dataset of online universities and e-commerce. previous shared tasks on aspect-based sentiment analysis (absa) focus on extracting sentiment targets and classifying the polarity directly. most previous methods follow the information ex-traction pipeline, which firstly extract the span of holders, targets, expressions and subsequently predict the relations. however, splitting structured sentiment analysis into subtasks may cause the error propagation problem. we follow the work that solving the problem by dependency graph parsing @xcite to achieve better performance in our model. ## system overview the overview of our system is shown in figure 1 . we use a pre-trained language model to extract text information as the node features in the graph. then, a cross attention layer is used to compute the predicted score of each edge type. after we get the edge score for each token pair, a graph parsing algorithm is presented to transform the predicted score to opinion tuples. ## experiments the experiments details and main results are shown in this section. ## conclusion in this paper, we have presented the implementation of the zhixiaobao system submitted to the semeval-2022 task 10. we propose an enhanced dependency parsing model for sentiment graph analysis. we leverage the fine-tuning technique of pre-trained language models, bert large and roberta large to increase the ability of model generalization. furthermore, we present the discrete cross attention and suffix masking technique to achieve a significant performance improvement. our model ranked 1st out of 32 participating teams on the monolingual subtask with the highest sf 1 score on 4 datasets.