# Agreement-based Learning of Parallel Lexicons and Phrases from Non-Parallel Corpora

arXiv: https://arxiv.org/abs/1606.04597 · HTML: https://mlsanity.com/paper/1606.04597/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Jun 2016.

## Built on by (most cited first, 1 of 1)

- [Improving Neural Machine Translation by Multi-Knowledge Integration with Prompting](https://mlsanity.com/paper/2312.04807/) (2023)

## Abstract (arXiv)

We introduce an agreement-based approach to learning parallel lexicons and phrases from non-parallel corpora. The basic idea is to encourage two asymmetric latent-variable translation models (i.e., source-to-target and target-to-source) to agree on identifying latent phrase and word alignments. The agreement is defined at both word and phrase levels. We develop a Viterbi EM algorithm for jointly training the two unidirectional models efficiently. Experiments on the Chinese-English dataset show that agreement-based learning significantly improves both alignment and translation performance.