Cross-Domain Data Augmentation with Domain-Adaptive Language Modeling for Aspect-Based Sentiment Analysis (2023)

Abstract: Cross-domain Aspect-Based Sentiment Analysis (ABSA) aims to leverage the useful knowledge from a source domain to identify aspect-sentiment pairs in sentences from a target domain. To tackle the task, several recent works explore a new unsupervised domain adaptation framework, i.e., Cross-Domain Data Augmentation (CDDA), aiming to directly generate much labeled target-domain data based on the labeled source-domain data. However, these CDDA methods still suffer from several issues: 1) preserving many source-specific attributes such as syntactic structures; 2) lack of fluency and coherence; 3) limiting the diversity of generated data. To address these issues, we propose a new cross-domain Data Augmentation approach based on Domain-Adaptive Language Modeling named DA ^2 LM, which contains three stages: 1) assigning pseudo labels to unlabeled target-domain data; 2) unifying the process of token generation and labeling with a Domain-Adaptive Language Model (DALM) to learn the shared context and annotation across domains; 3) using the trained DALM to generate labeled target-domain data. Experiments show that DA ^2 LM consistently outperforms previous feature adaptation and CDDA methods on both ABSA and Aspect Extraction tasks. The source code is publicly released at https://github.com/NUSTM/DALM .

## introduction as an important task in sentiment analysis, aspect-based sentiment analysis (absa) aims to extract aspect terms from sentences and predict the sentiment polarity towards each aspect term @xcite . for example, given a sentence "the screen is broken", the aspect term is screen and its sentiment polarity is negative. with the advancements of deep learning techniques, a myriad of neural approaches have been proposed for absa ## conclusion in this paper, we proposed a cross-domain data augmentation framework based on domain-adaptive language modeling (da 2 lm), which contains three key stages to automatically generate sufficient target-domain labeled data, including 1) domain-adaptive pseudo labeling, 2) domain-adaptive language modeling, and 3) target-domain data generation. experiments on four benchmark datasets show that our da 2 lm framework consistently outperforms the state-ofthe-art method for the cross-domain absa task. moreover, further evaluation results demonstrate the superiority of the generated data in terms of diversity, fluency, and data distribution.