Bilingual Lexicon Induction via Unsupervised Bitext Construction and Word Alignment

by   Haoyue Shi, et al.

Bilingual lexicons map words in one language to their translations in another, and are typically induced by learning linear projections to align monolingual word embedding spaces. In this paper, we show it is possible to produce much higher quality lexicons with methods that combine (1) unsupervised bitext mining and (2) unsupervised word alignment. Directly applying a pipeline that uses recent algorithms for both subproblems significantly improves induced lexicon quality and further gains are possible by learning to filter the resulting lexical entries, with both unsupervised and semi-supervised schemes. Our final model outperforms the state of the art on the BUCC 2020 shared task by 14 F_1 points averaged over 12 language pairs, while also providing a more interpretable approach that allows for rich reasoning of word meaning in context.


page 1

page 2

page 3

page 4


Combining Static Word Embeddings and Contextual Representations for Bilingual Lexicon Induction

Bilingual Lexicon Induction (BLI) aims to map words in one language to t...

Learning aligned embeddings for semi-supervised word translation using Maximum Mean Discrepancy

Word translation is an integral part of language translation. In machine...

Fake it Till You Make it: Self-Supervised Semantic Shifts for Monolingual Word Embedding Tasks

The use of language is subject to variation over time as well as across ...

Mask-Align: Self-Supervised Neural Word Alignment

Neural word alignment methods have received increasing attention recentl...

On the Limitations of Unsupervised Bilingual Dictionary Induction

Unsupervised machine translation---i.e., not assuming any cross-lingual ...

Updating Pre-trained Word Vectors and Text Classifiers using Monolingual Alignment

In this paper, we focus on the problem of adapting word vector-based mod...

Neural semi-Markov CRF for Monolingual Word Alignment

Monolingual word alignment is important for studying fine-grained editin...