# Monolingually Derived Phrase Scores for Phrase Based SMT Using Neural Networks Vector Representations

arXiv: https://arxiv.org/abs/1506.00406 · HTML: https://mlsanity.com/paper/1506.00406/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Jun 2015.

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

- [A Preordered RNN Layer Boosts Neural Machine Translation in Low Resource Settings](https://mlsanity.com/paper/2112.13960/) (2021)

## Abstract (arXiv)

In this paper, we propose two new features for estimating phrase-based machine translation parameters from mainly monolingual data. Our method is based on two recently introduced neural network vector representation models for words and sentences. It is the first time that these models have been used in an end to end phrase-based machine translation system. Scores obtained from our method can recover more than 80% of BLEU loss caused by removing phrase table probabilities. We also show that our features combined with the phrase table probabilities improve the BLEU score by absolute 0.74 points.