Margin-based Parallel Corpus Mining with Multilingual Sentence Embeddings

Abstract

Machine translation is highly sensitive to the size and quality of the training data, which has led to an increasing interest in collecting and filtering large parallel corpora. In this paper, we propose a new method for this task based on multilingual sentence embeddings. In contrast to previous approaches, which rely on nearest neighbor retrieval with a hard threshold over cosine similarity, our proposed method accounts for the scale inconsistencies of this measure, considering the margin between a given sentence pair and its closest candidates instead. Our experiments show large improvements over existing methods. We outperform the best published results on the BUCC mining task and the UN reconstruction task by more than 10 F1 and 30 precision points, respectively. Filtering the English-German ParaCrawl corpus with our approach, we obtain 31.2 BLEU points on newstest2014, an improvement of more than one point over the best official filtered version.

ACL 2019

References in corpus (5)

• Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation
• Convolutional Sequence to Sequence Learning
• Achieving Human Parity on Automatic Chinese to English News Translation
• Weighted Transformer Network for Machine Translation
• An Empirical Analysis of NMT-Derived Interlingual Embeddings and their Use in Parallel Sentence Identification

Cited by in corpus (5)

• Improving Multilingual Sentence Embedding using Bi-directional Dual Encoder with Additive Margin Softmax
• Contextual Lensing of Universal Sentence Representations
• LAWDR: Language-Agnostic Weighted Document Representations from Pre-trained Models
• Exploiting Parallel Corpora to Improve Multilingual Embedding based Document and Sentence Alignment
• NMT-based Cross-lingual Document Embeddings