mlsanityData to 7 Oct 2026

Analyzing the Use of Influence Functions for Instance-Specific Data Filtering in Neural Machine Translation

arXiv 2210.13281 ↗First posted 24 Oct 2022cs.CLcited by 3

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Abstract (arXiv)

Customer feedback can be an important signal for improving commercial machine translation systems. One solution for fixing specific translation errors is to remove the related erroneous training instances followed by re-training of the machine translation system, which we refer to as instance-specific data filtering. Influence functions (IF) have been shown to be effective in finding such relevant training examples for classification tasks such as image classification, toxic speech detection and entailment task. Given a probing instance, IF find influential training examples by measuring the similarity of the probing instance with a set of training examples in gradient space. In this work, we examine the use of influence functions for Neural Machine Translation (NMT). We propose two effective extensions to a state of the art influence function and demonstrate on the sub-problem of copied training examples that IF can be applied more generally than handcrafted regular expressions.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 12 of its 21 references that are in the map. First posted on arXiv 24 Oct 2022.

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12 of 21 references.

References · 12

  1. 2017Attention Is All You Needcited by 59,932
  2. 2017Understanding Black-box Predictions via Influence Functionscited by 1,714
  3. 2015Improving Neural Machine Translation Models with Monolingual Datacited by 784
  4. 2020Estimating Training Data Influence by Tracing Gradient Descentcited by 565
  5. 2019Revisiting Self-Training for Neural Sequence Generationcited by 154
  6. 2020Explaining Black Box Predictions and Unveiling Data Artifacts through Influence Functionscited by 144
  7. 2021Scaling Up Influence Functionscited by 130
  8. 2020FastIF: Scalable Influence Functions for Efficient Model Interpretation and Debuggingcited by 120
  9. 2018On the Impact of Various Types of Noise on Neural Machine Translationcited by 76
  10. 2020Language ID in the Wild: Unexpected Challenges on the Path to a Thousand-Language Web Text Corpuscited by 76
  11. 2018Denoising Neural Machine Translation Training with Trusted Data and Online Data Selectioncited by 43
  12. 2018Dual Conditional Cross-Entropy Filtering of Noisy Parallel Corporacited by 40

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Citing papers per year: 2023: 3.

3 papers.

  1. 2023Inseq: An Interpretability Toolkit for Sequence Generation Modelscited by 78
  2. 2023Make Every Example Count: On the Stability and Utility of Self-Influence for Learning from Noisy NLP Datasetscited by 18
  3. 2023How do languages influence each other? Studying cross-lingual data sharing during LM fine-tuningcited by 14