Solving Aspect Category Sentiment Analysis as a Text Generation Task (2021)

Abstract: Aspect category sentiment analysis has attracted increasing research attention. The dominant methods make use of pre-trained language models by learning effective aspect category-specific representations, and adding specific output layers to its pre-trained representation. We consider a more direct way of making use of pre-trained language models, by casting the ACSA tasks into natural language generation tasks, using natural language sentences to represent the output. Our method allows more direct use of pre-trained knowledge in seq2seq language models by directly following the task setting during pre-training. Experiments on several benchmarks show that our method gives the best reported results, having large advantages in few-shot and zero-shot settings.

## introduction aspect-based sentiment analysis (absa) is a finegrained sentiment analysis task that includes a number of subtasks, two of which are aspect category sentiment analysis (acsa) and aspect category @xcite . figure 1 shows an example, where the input is "the restaurant was expensive, but the menu was great". acd detects the aspect categories, such as price and food, and acsa predicts the sentiment polarities toward each aspect category. in this work, we focus on these two tasks as well as the joint task that combines both. previous studies have investigated various methods that treat acsa and acd as classification tasks, learning aspect-specific sentence representations @xcite . recently, pre-trained language models (plm) have shown their effectiveness to this end @xcite . the main idea is to make use of pre-trained models such as bert @xcite for representing an aspect-specific form of the input (e.g., by concatenating the aspect category to the end of the input sentence (figure 3(a) )), which provides useful semantic features for acsa and acd classifiers. such methods have given highly competitive results @xcite . the above classification models benefit from contextualized representations, which contain knowledge learned by pre-training over large data @xcite . however, their use of pre-trained knowledge can be viewed as indirect due to at least two reasons. first, the classification task is performed by using a neural network on top of pretrained representation, with separate network parameters. second, the integration of aspect category makes the aspect-specific input representation not exactly a natural language sentence, which differs from the pre-training setting. intuitively, more pre-trained knowledge could be leveraged by connecting pre-training and acsa at the task level, rather than only at the representation level. we investigate the above potentials by casting the sentiment classification tasks into language modelling tasks. in particular, as shown in figure 2 , both acsa and acd are transformed into sequence-to-sequence (seq2seq) tasks, where the encoder takes the input sentence and the decoder generates a natural language sentence. for acd, the output follows a template stating whether the specific aspect is discussed (e.g., "the category_type category is discussed"); for acsa, the sentiment polarity of a specific aspect is stated (e.g., "the sentiment polarity of given_category is polarity_type "). the setting corresponds closely to the denoising auto- ## methods formally for acd, the input is a sentence @xmath0, . . . , x n } = x 1:n , where x i denotes the i-th word. for acsa, a set of pre-identified aspect categories are also given. we introduce relevant pre-trained language models in 3.1, classification methods in section 3.2, mlm methods in section 3.3, and our generation method in section 3.4. ## conclusion we investigated a generation method for aspect category detection (acd) and aspect category sentiment analysis (acsa), which can make better use of bart's advantages in making semantic level summaries to the input by not introducing additional model parameters. experiments show that our proposed method obtains superior performance over the baseline models for both sentence-level and document-level aspect sentiment analysis. in contrast to the traditional sentiment classification methods, our method is also more powerful on zero-shot and few-shot tasks.