Wolfgang Macherey


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Multilingual Mix: Example Interpolation Improves Multilingual Neural Machine Translation
Yong Cheng | Ankur Bapna | Orhan Firat | Yuan Cao | Pidong Wang | Wolfgang Macherey
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Multilingual neural machine translation models are trained to maximize the likelihood of a mix of examples drawn from multiple language pairs. The dominant inductive bias applied to these models is a shared vocabulary and a shared set of parameters across languages; the inputs and labels corresponding to examples drawn from different language pairs might still reside in distinct sub-spaces. In this paper, we introduce multilingual crossover encoder-decoder (mXEncDec) to fuse language pairs at an instance level. Our approach interpolates instances from different language pairs into joint ‘crossover examples’ in order to encourage sharing input and output spaces across languages. To ensure better fusion of examples in multilingual settings, we propose several techniques to improve example interpolation across dissimilar languages under heavy data imbalance. Experiments on a large-scale WMT multilingual dataset demonstrate that our approach significantly improves quality on English-to-Many, Many-to-English and zero-shot translation tasks (from +0.5 BLEU up to +5.5 BLEU points). Results on code-switching sets demonstrate the capability of our approach to improve model generalization to out-of-distribution multilingual examples. We also conduct qualitative and quantitative representation comparisons to analyze the advantages of our approach at the representation level.


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Experts, Errors, and Context: A Large-Scale Study of Human Evaluation for Machine Translation
Markus Freitag | George Foster | David Grangier | Viresh Ratnakar | Qijun Tan | Wolfgang Macherey
Transactions of the Association for Computational Linguistics, Volume 9

Abstract Human evaluation of modern high-quality machine translation systems is a difficult problem, and there is increasing evidence that inadequate evaluation procedures can lead to erroneous conclusions. While there has been considerable research on human evaluation, the field still lacks a commonly accepted standard procedure. As a step toward this goal, we propose an evaluation methodology grounded in explicit error analysis, based on the Multidimensional Quality Metrics (MQM) framework. We carry out the largest MQM research study to date, scoring the outputs of top systems from the WMT 2020 shared task in two language pairs using annotations provided by professional translators with access to full document context. We analyze the resulting data extensively, finding among other results a substantially different ranking of evaluated systems from the one established by the WMT crowd workers, exhibiting a clear preference for human over machine output. Surprisingly, we also find that automatic metrics based on pre-trained embeddings can outperform human crowd workers. We make our corpus publicly available for further research.


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AdvAug: Robust Adversarial Augmentation for Neural Machine Translation
Yong Cheng | Lu Jiang | Wolfgang Macherey | Jacob Eisenstein
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

In this paper, we propose a new adversarial augmentation method for Neural Machine Translation (NMT). The main idea is to minimize the vicinal risk over virtual sentences sampled from two vicinity distributions, in which the crucial one is a novel vicinity distribution for adversarial sentences that describes a smooth interpolated embedding space centered around observed training sentence pairs. We then discuss our approach, AdvAug, to train NMT models using the embeddings of virtual sentences in sequence-to-sequence learning. Experiments on Chinese-English, English-French, and English-German translation benchmarks show that AdvAug achieves significant improvements over theTransformer (up to 4.9 BLEU points), and substantially outperforms other data augmentation techniques (e.g.back-translation) without using extra corpora.

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KoBE: Knowledge-Based Machine Translation Evaluation
Zorik Gekhman | Roee Aharoni | Genady Beryozkin | Markus Freitag | Wolfgang Macherey
Findings of the Association for Computational Linguistics: EMNLP 2020

We propose a simple and effective method for machine translation evaluation which does not require reference translations. Our approach is based on (1) grounding the entity mentions found in each source sentence and candidate translation against a large-scale multilingual knowledge base, and (2) measuring the recall of the grounded entities found in the candidate vs. those found in the source. Our approach achieves the highest correlation with human judgements on 9 out of the 18 language pairs from the WMT19 benchmark for evaluation without references, which is the largest number of wins for a single evaluation method on this task. On 4 language pairs, we also achieve higher correlation with human judgements than BLEU. To foster further research, we release a dataset containing 1.8 million grounded entity mentions across 18 language pairs from the WMT19 metrics track data.

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Re-translation versus Streaming for Simultaneous Translation
Naveen Arivazhagan | Colin Cherry | Wolfgang Macherey | George Foster
Proceedings of the 17th International Conference on Spoken Language Translation

There has been great progress in improving streaming machine translation, a simultaneous paradigm where the system appends to a growing hypothesis as more source content becomes available. We study a related problem in which revisions to the hypothesis beyond strictly appending words are permitted. This is suitable for applications such as live captioning an audio feed. In this setting, we compare custom streaming approaches to re-translation, a straightforward strategy where each new source token triggers a distinct translation from scratch. We find re-translation to be as good or better than state-of-the-art streaming systems, even when operating under constraints that allow very few revisions. We attribute much of this success to a previously proposed data-augmentation technique that adds prefix-pairs to the training data, which alongside wait-k inference forms a strong baseline for streaming translation. We also highlight re-translation’s ability to wrap arbitrarily powerful MT systems with an experiment showing large improvements from an upgrade to its base model.


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Monotonic Infinite Lookback Attention for Simultaneous Machine Translation
Naveen Arivazhagan | Colin Cherry | Wolfgang Macherey | Chung-Cheng Chiu | Semih Yavuz | Ruoming Pang | Wei Li | Colin Raffel
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Simultaneous machine translation begins to translate each source sentence before the source speaker is finished speaking, with applications to live and streaming scenarios. Simultaneous systems must carefully schedule their reading of the source sentence to balance quality against latency. We present the first simultaneous translation system to learn an adaptive schedule jointly with a neural machine translation (NMT) model that attends over all source tokens read thus far. We do so by introducing Monotonic Infinite Lookback (MILk) attention, which maintains both a hard, monotonic attention head to schedule the reading of the source sentence, and a soft attention head that extends from the monotonic head back to the beginning of the source. We show that MILk’s adaptive schedule allows it to arrive at latency-quality trade-offs that are favorable to those of a recently proposed wait-k strategy for many latency values.

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Robust Neural Machine Translation with Doubly Adversarial Inputs
Yong Cheng | Lu Jiang | Wolfgang Macherey
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Neural machine translation (NMT) often suffers from the vulnerability to noisy perturbations in the input. We propose an approach to improving the robustness of NMT models, which consists of two parts: (1) attack the translation model with adversarial source examples; (2) defend the translation model with adversarial target inputs to improve its robustness against the adversarial source inputs. For the generation of adversarial inputs, we propose a gradient-based method to craft adversarial examples informed by the translation loss over the clean inputs. Experimental results on Chinese-English and English-German translation tasks demonstrate that our approach achieves significant improvements (2.8 and 1.6 BLEU points) over Transformer on standard clean benchmarks as well as exhibiting higher robustness on noisy data.


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The Best of Both Worlds: Combining Recent Advances in Neural Machine Translation
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
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

The past year has witnessed rapid advances in sequence-to-sequence (seq2seq) modeling for Machine Translation (MT). The classic RNN-based approaches to MT were first out-performed by the convolutional seq2seq model, which was then out-performed by the more recent Transformer model. Each of these new approaches consists of a fundamental architecture accompanied by a set of modeling and training techniques that are in principle applicable to other seq2seq architectures. In this paper, we tease apart the new architectures and their accompanying techniques in two ways. First, we identify several key modeling and training techniques, and apply them to the RNN architecture, yielding a new RNMT+ model that outperforms all of the three fundamental architectures on the benchmark WMT’14 English to French and English to German tasks. Second, we analyze the properties of each fundamental seq2seq architecture and devise new hybrid architectures intended to combine their strengths. Our hybrid models obtain further improvements, outperforming the RNMT+ model on both benchmark datasets.

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Revisiting Character-Based Neural Machine Translation with Capacity and Compression
Colin Cherry | George Foster | Ankur Bapna | Orhan Firat | Wolfgang Macherey
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Translating characters instead of words or word-fragments has the potential to simplify the processing pipeline for neural machine translation (NMT), and improve results by eliminating hyper-parameters and manual feature engineering. However, it results in longer sequences in which each symbol contains less information, creating both modeling and computational challenges. In this paper, we show that the modeling problem can be solved by standard sequence-to-sequence architectures of sufficient depth, and that deep models operating at the character level outperform identical models operating over word fragments. This result implies that alternative architectures for handling character input are better viewed as methods for reducing computation time than as improved ways of modeling longer sequences. From this perspective, we evaluate several techniques for character-level NMT, verify that they do not match the performance of our deep character baseline model, and evaluate the performance versus computation time tradeoffs they offer. Within this framework, we also perform the first evaluation for NMT of conditional computation over time, in which the model learns which timesteps can be skipped, rather than having them be dictated by a fixed schedule specified before training begins.


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Improved Domain Adaptation for Statistical Machine Translation
Wei Wang | Klaus Macherey | Wolfgang Macherey | Franz Och | Peng Xu
Proceedings of the 10th Conference of the Association for Machine Translation in the Americas: Research Papers

We present a simple and effective infrastructure for domain adaptation for statistical machine translation (MT). To build MT systems for different domains, it trains, tunes and deploys a single translation system that is capable of producing adapted domain translations and preserving the original generic accuracy at the same time. The approach unifies automatic domain detection and domain model parameterization into one system. Experiment results on 20 language pairs demonstrate its viability.


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Expected Sequence Similarity Maximization
Cyril Allauzen | Shankar Kumar | Wolfgang Macherey | Mehryar Mohri | Michael Riley
Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics


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Efficient Minimum Error Rate Training and Minimum Bayes-Risk Decoding for Translation Hypergraphs and Lattices
Shankar Kumar | Wolfgang Macherey | Chris Dyer | Franz Och
Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP


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Lattice Minimum Bayes-Risk Decoding for Statistical Machine Translation
Roy Tromble | Shankar Kumar | Franz Och | Wolfgang Macherey
Proceedings of the 2008 Conference on Empirical Methods in Natural Language Processing

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Lattice-based Minimum Error Rate Training for Statistical Machine Translation
Wolfgang Macherey | Franz Och | Ignacio Thayer | Jakob Uszkoreit
Proceedings of the 2008 Conference on Empirical Methods in Natural Language Processing


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Improving Word Alignment with Bridge Languages
Shankar Kumar | Franz J. Och | Wolfgang Macherey
Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL)

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An Empirical Study on Computing Consensus Translations from Multiple Machine Translation Systems
Wolfgang Macherey | Franz J. Och
Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL)