Papers by Wolfgang Macherey
The Best of Both Worlds: Combining Recent Advances in Neural Machine Translation (P18-1)
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Mia Xu Chen, Orhan Firat, Ankur Bapna, Melvin Johnson, Wolfgang Macherey, George Foster, Llion Jones, Mike Schuster, Noam Shazeer, Niki Parmar, Ashish Vaswani, Jakob Uszkoreit, Lukasz Kaiser, Zhifeng Chen, Yonghui Wu, Macduff Hughes
| Challenge: | In recent years, the emergence of seq2seq models has revolutionized the field of machine translation by replacing traditional phrase-based approaches with neural machine translation (NMT) systems based on the encoder-decoder paradigm. |
| Approach: | They propose to use a convolutional seq2seq model to combine the strengths of the two approaches. |
| Outcome: | The proposed architectures outperform the existing models on the WMT’14 benchmark dataset. |
Multilingual Mix: Example Interpolation Improves Multilingual Neural Machine Translation (2022.acl-long)
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| Challenge: | Existing approaches to train multilingual models to learn the inductive bias of a shared vocabulary and set of parameters across languages. |
| Approach: | They propose to use a multilingual crossover encoder-decoder to fuse language pairs at an instance level to encourage sharing of input and output spaces. |
| Outcome: | The proposed approach improves quality on English-to-Many, Many-to English and zero-shot translation tasks from +0.5 BLEU up to +5.5 BLUE points. |
AdvAug: Robust Adversarial Augmentation for Neural Machine Translation (2020.acl-main)
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| Challenge: | Recent work in neural machine translation has led to dramatic improvements in both research and commercial systems. |
| Approach: | They propose a adversarial augmentation method for Neural Machine Translation that minimizes vicinal risk over virtual sentences . they use a novel vicinity distribution for adversarials to describe a smooth interpolated embedding space . |
| Outcome: | The proposed method outperforms the current method on Chinese-English, English-French, and English-German translation benchmarks. |
KoBE: Knowledge-Based Machine Translation Evaluation (2020.findings-emnlp)
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| Challenge: | Existing methods for machine translation evaluation do not require reference translations. |
| Approach: | They propose a method for machine translation evaluation which does not require reference translations. |
| Outcome: | The proposed method achieves highest correlation with human judgements on 9 out of 18 language pairs from the WMT19 benchmark for evaluation without references. |
Revisiting Character-Based Neural Machine Translation with Capacity and Compression (D18-1)
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| Challenge: | Translating characters instead of words or word-fragments can simplify the processing pipeline but results in longer sequences . |
| Approach: | They propose to use sequence-to-sequence architectures of sufficient depth to solve the problem . they also evaluate the performance versus computation time tradeoffs they offer . |
| Outcome: | The proposed models outperform models operating over word fragments in character-level NMT, the authors show . they also show that the proposed models do not match the performance of their deep character baseline model . |
Experts, Errors, and Context: A Large-Scale Study of Human Evaluation for Machine Translation (2021.tacl-1)
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| Challenge: | a large study of machine translation systems shows poor evaluation procedures can lead to erroneous conclusions. |
| Approach: | They propose an evaluation methodology grounded in explicit error analysis based on the Multidimensional Quality Metrics framework. |
| Outcome: | The proposed evaluation methodology outperforms crowd workers in two languages . it shows that human-based metrics outperformed crowd workers . |
Barriers to Effective Evaluation of Simultaneous Interpretation (2024.findings-eacl)
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| Challenge: | Existing studies have relied on out-of-the-box machine translation metrics to evaluate interpretation data, but they do not account for human judgments of interpretation quality. |
| Approach: | They propose to use machine translation metrics to evaluate human interpretations to address potential barriers to disfluency, summarization, paraphrasing and segmentation. |
| Outcome: | The proposed model achieves better correlation with human judgments than state-of-the-art metrics. |
Robust Neural Machine Translation with Doubly Adversarial Inputs (P19-1)
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| Challenge: | Neural machine translation (NMT) models suffer from noisy perturbations in the input . a gradient-based method to craft adversarial examples informed by the translation loss is proposed . |
| Approach: | They propose an approach to improve the robustness of NMT models by attacking the translation model with adversarial source examples and defending the model with a target input. |
| Outcome: | The proposed approach improves translation performance and robustness on clean inputs and higher on noisy data. |
Monotonic Infinite Lookback Attention for Simultaneous Machine Translation (P19-1)
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Naveen Arivazhagan, Colin Cherry, Wolfgang Macherey, Chung-Cheng Chiu, Semih Yavuz, Ruoming Pang, Wei Li, Colin Raffel
| Challenge: | Simultaneous machine translation begins to translate each source sentence before the source speaker has finished speaking, with applications to live and streaming scenarios. |
| Approach: | They propose a simultaneous translation system that learns an adaptive schedule with a neural machine translation model that attends over all source tokens read thus far. |
| Outcome: | The proposed system can achieve latency-quality trade-offs favorable to a proposed wait-k strategy for many latency values. |