Multilingual Sentence Transformer as A Multilingual Word Aligner
This work addresses the need for more effective multilingual word alignment tools for NLP researchers and practitioners, offering a novel application of an existing model with incremental improvements through fine-tuning.
The paper tackled the problem of multilingual word alignment by investigating whether the LaBSE sentence Transformer could serve as a strong aligner, despite its sentence-level training, and demonstrated that it outperforms previous state-of-the-art models across seven language pairs, including achieving new SOTA on zero-shot pairs.
Multilingual pretrained language models (mPLMs) have shown their effectiveness in multilingual word alignment induction. However, these methods usually start from mBERT or XLM-R. In this paper, we investigate whether multilingual sentence Transformer LaBSE is a strong multilingual word aligner. This idea is non-trivial as LaBSE is trained to learn language-agnostic sentence-level embeddings, while the alignment extraction task requires the more fine-grained word-level embeddings to be language-agnostic. We demonstrate that the vanilla LaBSE outperforms other mPLMs currently used in the alignment task, and then propose to finetune LaBSE on parallel corpus for further improvement. Experiment results on seven language pairs show that our best aligner outperforms previous state-of-the-art models of all varieties. In addition, our aligner supports different language pairs in a single model, and even achieves new state-of-the-art on zero-shot language pairs that does not appear in the finetuning process.