mlsanityData to 7 Oct 2026

Input Augmentation Improves Constrained Beam Search for Neural Machine Translation: NTT at WAT 2021

arXiv 2106.05450 ↗First posted 10 Jun 2021cs.CLcited by 4

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Abstract (arXiv)

This paper describes our systems that were submitted to the restricted translation task at WAT 2021. In this task, the systems are required to output translated sentences that contain all given word constraints. Our system combined input augmentation and constrained beam search algorithms. Through experiments, we found that this combination significantly improves translation accuracy and can save inference time while containing all the constraints in the output. For both En->Ja and Ja->En, our systems obtained the best evaluation performances in automatic evaluation.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 19 of its 31 references that are in the map. First posted on arXiv 10 Jun 2021.

Builds on

19 of 31 references.

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7 more
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Citing papers per year: 2023: 4.

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  2. 2023Gender Neutralization for an Inclusive Machine Translation: from Theoretical Foundations to Open Challengescited by 22
  3. 2023Songs Across Borders: Singable and Controllable Neural Lyric Translationcited by 11
  4. 2023Joint Learning of Wording and Formatting for Singable Melody-to-Lyric Generationcited by 1