mlsanityData to 7 Oct 2026

Improving Machine Translation with Phrase Pair Injection and Corpus Filtering

arXiv 2301.08008 ↗First posted 19 Jan 2023cs.CL cs.LGcited by 7

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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.

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

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12 of 20 references.

References · 12

  1. 2017Attention Is All You Needcited by 59,932
  2. 2014Neural Machine Translation by Jointly Learning to Align and Translatecited by 4,530
  3. 2014Sequence to Sequence Learning with Neural Networkscited by 2,974
  4. 2015Neural Machine Translation of Rare Words with Subword Unitscited by 2,898
  5. 2018A Call for Clarity in Reporting BLEU Scorescited by 1,790
  6. 2020Language-agnostic BERT Sentence Embeddingcited by 621
  7. 2017OpenNMT: Open-source Toolkit for Neural Machine Translationcited by 251
  8. 2021Samanantar: The Largest Publicly Available Parallel Corpora Collection for 11 Indic Languagescited by 133
  9. 2020The Tatoeba Translation Challenge -- Realistic Data Sets for Low Resource and Multilingual MTcited by 127
  10. 2020PMIndia -- A Collection of Parallel Corpora of Languages of Indiacited by 40
  11. 2020Revisiting Low Resource Status of Indian Languages in Machine Translationcited by 12
  12. 2021Selecting Parallel In-domain Sentences for Neural Machine Translation Using Monolingual Textscited by 4

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Cited by year

Citing papers per year: 2023: 3; 2024: 4.

7 papers.

  1. 2024Investigating Neural Machine Translation for Low-Resource Languages: Using Bavarian as a Case Studycited by 5
  2. 2023VAKTA-SETU: A Speech-to-Speech Machine Translation Service in Select Indic Languagescited by 2
  3. 2023"A Little is Enough": Few-Shot Quality Estimation based Corpus Filtering improves Machine Translationcited by 2
  4. 2023APE-then-QE: Correcting then Filtering Pseudo Parallel Corpora for MT Training Data Creationcited by 0
  5. 2024Prediction of Translation Techniques for the Translation Processcited by 0
  6. 2024KPC-cF: Aspect-Based Sentiment Analysis via Implicit-Feature Alignment with Corpus Filteringcited by 0
  7. 2024Reconsidering SMT Over NMT for Closely Related Languages: A Case Study of Persian-Hindi Paircited by 0