# Improving Machine Translation with Phrase Pair Injection and Corpus Filtering

arXiv: https://arxiv.org/abs/2301.08008 · HTML: https://mlsanity.com/paper/2301.08008/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 20 references that are in the map.
- First posted on arXiv 19 Jan 2023.

## Builds on (most cited first, 12 of 12)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [Neural Machine Translation of Rare Words with Subword Units](https://mlsanity.com/paper/1508.07909/) (2015)
- [A Call for Clarity in Reporting BLEU Scores](https://mlsanity.com/paper/1804.08771/) (2018)
- [Language-agnostic BERT Sentence Embedding](https://mlsanity.com/paper/2007.01852/) (2020)
- [OpenNMT: Open-source Toolkit for Neural Machine Translation](https://mlsanity.com/paper/1709.03815/) (2017)
- [Samanantar: The Largest Publicly Available Parallel Corpora Collection for 11 Indic Languages](https://mlsanity.com/paper/2104.05596/) (2021)
- [The Tatoeba Translation Challenge -- Realistic Data Sets for Low Resource and Multilingual MT](https://mlsanity.com/paper/2010.06354/) (2020)
- [PMIndia -- A Collection of Parallel Corpora of Languages of India](https://mlsanity.com/paper/2001.09907/) (2020)
- [Revisiting Low Resource Status of Indian Languages in Machine Translation](https://mlsanity.com/paper/2008.04860/) (2020)
- [Selecting Parallel In-domain Sentences for Neural Machine Translation Using Monolingual Texts](https://mlsanity.com/paper/2112.06096/) (2021)

## Built on by (most cited first, 7 of 7)

- [Investigating Neural Machine Translation for Low-Resource Languages: Using Bavarian as a Case Study](https://mlsanity.com/paper/2404.08259/) (2024)
- [VAKTA-SETU: A Speech-to-Speech Machine Translation Service in Select Indic Languages](https://mlsanity.com/paper/2305.12518/) (2023)
- ["A Little is Enough": Few-Shot Quality Estimation based Corpus Filtering improves Machine Translation](https://mlsanity.com/paper/2306.03507/) (2023)
- [APE-then-QE: Correcting then Filtering Pseudo Parallel Corpora for MT Training Data Creation](https://mlsanity.com/paper/2312.11312/) (2023)
- [Prediction of Translation Techniques for the Translation Process](https://mlsanity.com/paper/2403.14454/) (2024)
- [KPC-cF: Aspect-Based Sentiment Analysis via Implicit-Feature Alignment with Corpus Filtering](https://mlsanity.com/paper/2407.00342/) (2024)
- [Reconsidering SMT Over NMT for Closely Related Languages: A Case Study of Persian-Hindi Pair](https://mlsanity.com/paper/2412.16877/) (2024)

## Abstract (arXiv)

In this paper, we show that the combination of Phrase Pair Injection and Corpus Filtering boosts the performance of Neural Machine Translation (NMT) systems. We extract parallel phrases and sentences from the pseudo-parallel corpus and augment it with the parallel corpus to train the NMT models. With the proposed approach, we observe an improvement in the Machine Translation (MT) system for 3 low-resource language pairs, Hindi-Marathi, English-Marathi, and English-Pashto, and 6 translation directions by up to 2.7 BLEU points, on the FLORES test data. These BLEU score improvements are over the models trained using the whole pseudo-parallel corpus augmented with the parallel corpus.