S en P oi at S em E val-2022 Task 10: Point me to your Opinion, S en P oi (2022)

Abstract: Structured Sentiment Analysis is the task of extracting sentiment tuples in a graph structure commonly from review texts. We adapt the Aspect-Based Sentiment Analysis pointer network BARTABSA to model this tuple extraction as a sequence prediction task and extend their output grammar to account for the increased complexity of Structured Sentiment Analysis. To predict structured sentiment tuples in languages other than English we swap BART for a multilingual mT5 and introduce a novel Output Length Regularization to mitigate overfitting to common target sequence lengths, thereby improving the performance of the model by up to 70%. We evaluate our approach on seven datasets in five languages including a zero shot crosslingual setting.

## introduction the goal of sentiment analysis is to understand a writer's opinions expressed in a text. in recent years, this topic became of particular research interest since with the advent of social media platforms, users were encouraged to share their opinions about a wide range of subjects with the world. for example, websites like yelp collect and share opinions about restaurant visits and various online retailers allow customers to publish their reviews about items in their assortment. consequently, various text corpora emerged that made it feasible to apply machine learning based approaches to this task @xcite . sentiment analysis can be approached with various degrees of granularity. it is possible to only classify the overall polarity of a sentence as positive or negative, or a more precise intermediate value @xcite . furthermore, the sentiment can be predicted in a more fine-grained manner, like with regard to the various targets addressed in the sentence. this can be of particular interest if the writer is ambiguous in their review and no overall sentiment label can be assigned to a sentence. for example, a customer might describe the service or the interior of a restaurant as unpleasant but the food itself as good. this can be extended to more complex sentence structures, taking into account not only the aspects or the sentiment, but also its holder (i.e. who is expressing the sentiment) or the opinion term itself, e.g. @xcite . in this paper, we work on the challenge described by @xcite . here, the goal is to extract an arbitrary number of opinion tuples from a text. each tuple can consist of a holder, a target, a sentiment expression, and a polarity. however, it is also possible that some of these mentioned entities are not present in a certain tuple. moreover, a sentence may include no sentiment and hence no tuples should be extracted. the challenge spans data from five different languages, namely english, spanish, catalan, basque, and norwegian and two different subtasks. first, there is the monolingual setting in which you are allowed to train on all sentiment data, including those in the language that is subsequently used to test the model's capability of extracting sentiment. moreover, in the crosslingual setting the models are tested on spanish, catalan and basque sentiment data while being trained on any of the other languages. it was also allowed to train on spanish, catalan and basque data as long as it does not contain any annotated sentiment information. this makes the crosslingual structured sentiment analysis task a zero shot setting. our approach is based on bartabsa @xcite , who leverage a pointer network to predict sentiment tuples (aspect, opinion, sentiment) by representing them using a custom output grammar. they extend a bart model with a pointer generator network which is thus able to predict token indices from the input sequence as output. our contributions are as follows: (i) we introduce a new, flexible grammar to model structured sentiment graphs as a pointer sequence, (ii) we explore mt5 as base model allowing us to make cross-lingual predictions, (iii) we introduce a new length regularizer to prevent overfitting to common sequence lengths. ## methodology our approach to predicting sentiment graphs on the given datasets is grounded on bartabsa (2.2). we adopt and extend their framework to not only be able to predict aspect-based sentiment but also structured sentiment. for this we introduce a new output grammar which is able to model and represent the sentiment tuples as required for this task. we adapt their sequential pointer representation for triplet extraction (aspect, opinion, sentiment) to the task at hand. ## related works the paste framework @xcite also uses pointer networks to solve the task of aspect-based sentiment analysis. instead of transformers, they use an lstm @xcite as decoder and two additional bi-lstms as pointer networks -one for pointing to aspect and opinion term each. @xcite propose an approach to extract opinion triplets from text in a generative manner without using pointers. however, their output grammar is also less flexible meaning that all of the entities (target, aspect, and sentiment) have to be present in each triple. they also introduced one of the data sets used to train bartabsa and paste. apart from structured sentiment analysis, pointer networks were also successfully applied to various further nlp tasks where it is beneficial to directly transfer parts of the input sequence to the output. this includes automatic summarization @xcite and entity extraction @xcite . ## conclusion in this work, we adapted bartabsa, a pointer network based on bart, for the task of structured sentiment analysis. in particular, we introduced a new output grammar which is able to model the increased complexity of this task by taking into account new entity types, split entities and missing entities in sentiment tuples. we also experimented replacing bart with an mt5 network to allow for input sequences in languages other than english. we found that using the approach of bartabsa it is possible to swap out bart for another base model but in the case of mt5 this comes with a significant performance hit which we suspect is mainly driven by training instabilities we encountered. moreover, we introduced a output length regularizer to reduce overfitting to common sequence output lengths from the trainset. we found this to be very beneficial consistently on all data sets and to increase relative performance by up to 70%.