Language-agnostic BERT Sentence Embedding

TL;DR

This paper introduces a multilingual BERT-based sentence embedding model that significantly reduces the need for parallel data, achieving high accuracy across 112 languages and enabling effective cross-lingual tasks.

Contribution

It presents a novel approach combining multiple methods to create a language-agnostic BERT sentence embedding model with state-of-the-art performance.

Findings

Achieves 83.7% bi-text retrieval accuracy over 112 languages

Reduces parallel data requirements by 80% using pre-trained multilingual models

Enables training competitive NMT models using mined parallel data

Abstract

While BERT is an effective method for learning monolingual sentence embeddings for semantic similarity and embedding based transfer learning (Reimers and Gurevych, 2019), BERT based cross-lingual sentence embeddings have yet to be explored. We systematically investigate methods for learning multilingual sentence embeddings by combining the best methods for learning monolingual and cross-lingual representations including: masked language modeling (MLM), translation language modeling (TLM) (Conneau and Lample, 2019), dual encoder translation ranking (Guo et al., 2018), and additive margin softmax (Yang et al., 2019a). We show that introducing a pre-trained multilingual language model dramatically reduces the amount of parallel training data required to achieve good performance by 80%. Composing the best of these methods produces a model that achieves 83.7% bi-text retrieval accuracy over 112 languages on Tatoeba, well above the 65.5% achieved by Artetxe and Schwenk (2019b), while still performing competitively on monolingual transfer learning benchmarks (Conneau and Kiela, 2018). Parallel data mined from CommonCrawl using our best model is shown to train competitive NMT models for en-zh and en-de. We publicly release our best multilingual sentence embedding model for 109+ languages at https://tfhub.dev/google/LaBSE.