2 research outputs found
CL-XABSA: Contrastive Learning for Cross-lingual Aspect-based Sentiment Analysis
As an extensive research in the field of Natural language processing (NLP),
aspect-based sentiment analysis (ABSA) is the task of predicting the sentiment
expressed in a text relative to the corresponding aspect. Unfortunately, most
languages lack of sufficient annotation resources, thus more and more recent
researchers focus on cross-lingual aspect-based sentiment analysis (XABSA).
However, most recent researches only concentrate on cross-lingual data
alignment instead of model alignment. To this end, we propose a novel
framework, CL-XABSA: Contrastive Learning for Cross-lingual Aspect-Based
Sentiment Analysis. Specifically, we design two contrastive strategies, token
level contrastive learning of token embeddings (TL-CTE) and sentiment level
contrastive learning of token embeddings (SL-CTE), to regularize the semantic
space of source and target language to be more uniform. Since our framework can
receive datasets in multiple languages during training, our framework can be
adapted not only for XABSA task, but also for multilingual aspect-based
sentiment analysis (MABSA). To further improve the performance of our model, we
perform knowledge distillation technology leveraging data from unlabeled target
language. In the distillation XABSA task, we further explore the comparative
effectiveness of different data (source dataset, translated dataset, and
code-switched dataset). The results demonstrate that the proposed method has a
certain improvement in the three tasks of XABSA, distillation XABSA and MABSA.
For reproducibility, our code for this paper is available at
https://github.com/GKLMIP/CL-XABSA