mlsanityData to 14 Oct 2026

Wait-info Policy: Balancing Source and Target at Information Level for Simultaneous Machine Translation

arXiv 2210.11220 ↗First posted 20 Oct 2022cs.CL cs.AIcited by 16

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Abstract (arXiv)

Simultaneous machine translation (SiMT) outputs the translation while receiving the source inputs, and hence needs to balance the received source information and translated target information to make a reasonable decision between waiting for inputs or outputting translation. Previous methods always balance source and target information at the token level, either directly waiting for a fixed number of tokens or adjusting the waiting based on the current token. In this paper, we propose a Wait-info Policy to balance source and target at the information level. We first quantify the amount of information contained in each token, named info. Then during simultaneous translation, the decision of waiting or outputting is made based on the comparison results between the total info of previous target outputs and received source inputs. Experiments show that our method outperforms strong baselines under and achieves better balance via the proposed info.

Key facts. Cited by 16 papers in the map (data through 14 Oct 2026). Builds on 20 of its 35 references that are in the map. First posted on arXiv 20 Oct 2022.

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20 of 35 references.

References · 20

  1. 2017Attention Is All You Needcited by 60,361
  2. 2015Neural Machine Translation of Rare Words with Subword Unitscited by 2,918
  3. 2019fairseq: A Fast, Extensible Toolkit for Sequence Modelingcited by 1,488
  4. 2019Monotonic Infinite Lookback Attention for Simultaneous Machine Translationcited by 104
  5. 2016Learning to Translate in Real-time with Neural Machine Translationcited by 97
  6. 2019Monotonic Multihead Attentioncited by 93
  7. 2016Can neural machine translation do simultaneous translation?cited by 86
  8. 2020Efficient Wait-k Models for Simultaneous Machine Translationcited by 61
  9. 2020Norm-Based Curriculum Learning for Neural Machine Translationcited by 60
  10. 2020Simultaneous Translation Policies: From Fixed to Adaptivecited by 43
  11. 2019Simpler and Faster Learning of Adaptive Policies for Simultaneous Translationcited by 37
  12. 2021Universal Simultaneous Machine Translation with Mixture-of-Experts Wait-k Policycited by 32
8 more
  1. 2020Future-Guided Incremental Transformer for Simultaneous Translationcited by 30
  2. 2022Modeling Dual Read/Write Paths for Simultaneous Machine Translationcited by 21
  3. 2021Modeling Concentrated Cross-Attention for Neural Machine Translation with Gaussian Mixture Modelcited by 18
  4. 2022Gaussian Multi-head Attention for Simultaneous Machine Translationcited by 17
  5. 2022Reducing Position Bias in Simultaneous Machine Translation with Length-Aware Frameworkcited by 16
  6. 2018Learning When to Concentrate or Divert Attention: Self-Adaptive Attention Temperature for Neural Machine Translationcited by 10
  7. 2019Interrogating the Explanatory Power of Attention in Neural Machine Translationcited by 7
  8. 2022Conditional Bilingual Mutual Information Based Adaptive Training for Neural Machine Translationcited by 6

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Citing papers per year: 2022: 2; 2023: 10; 2024: 4.

16 papers.

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