Wolfgang Macherey
Wolfgang Macherey joined Google in 2006 as a research scientist, where
he works in the machine translation group with Franz Josef Och. He has
been working on natural language processing since 1996.
Wolfgang worked as a Research Assistant at RWTH Aachen University from 1999 to 2005. His main research interests are in statistical machine translation and automatic speech recognition with the focus on discriminative training methods, natural language processing, statistical pattern recognition, and machine learning.
He received a PhD in Computer Science from RWTH Aachen University, Germany, in 2010 and his Diploma Degree in Computer Science from RWTH Aachen University in 1999 with a major in statistical pattern recognition and a minor in physical chemistry and thermodynamics.
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SPAE: Semantic Pyramid AutoEncoder for Multimodal Generation with Frozen LLMs
Zhiruo Wang
Yonatan Bisk
Alex Hauptmann
Lu Jiang
NeurIPS (2023)
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In this work, we introduce Semantic Pyramid AutoEncoder (SPAE) for enabling frozen LLMs to perform both understanding and generation tasks involving non-linguistic modalities such as images or videos. SPAE converts between raw pixels and interpretable lexical tokens (or words) extracted from the LLM's vocabulary. The resulting tokens capture both the semantic meaning and the fine-grained details needed for visual reconstruction, effectively translating the visual content into a language comprehensible to the LLM, and empowering it to perform a wide array of multimodal tasks. Our approach is validated through in-context learning experiments with frozen PaLM 2 and GPT 3.5 on a diverse set of image understanding and generation tasks. Our method marks the first successful attempt to enable a frozen LLM to generate image content while surpassing state-of-the-art performance in image understanding tasks, under the same setting, by over 25%.
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We present Mu2SLAM, a multilingual sequence-to-sequence model pre-trained jointly on un-labeled speech, unlabeled text and supervised data spanning Automatic Speech Recognition(ASR), Automatic Speech Translation (AST)and Machine Translation (MT), in over 100 languages. By leveraging a quantized representation of speech as a target, Mu2SLAM trains ona sequence-to-sequence masked denoising objective similar to T5 on both unlabeled speech and text, while utilizing the supervised tasks to improve cross-lingual and cross-modal representation alignment within the model. On CoVoSTAST, Mu2SLAM establishes a new state-of-the-art for models trained on public datasets, improv-ing on xx-en translation over the previous best by 1.9 Bleu points and on en-xx translation by 0.9 Bleu points. On Voxpopuli ASR, our model matches the performance of a mSLAM model finetuned with a RNN-T decoder, despite using a relatively weaker sequence-to-sequence architecture. On text understanding tasks, our model improves by more than 6% over mSLAM on XNLI, getting closer to the performance of mT5 models of comparable capacity on XNLI and TydiQA, paving the way towards a single model for all speech and text understanding tasks.
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Building Machine Translation Systems for the Next Thousand Languages
Julia Kreutzer
Mengmeng Niu
Pallavi Nikhil Baljekar
Xavier Garcia
Maxim Krikun
Pidong Wang
Apu Shah
Macduff Richard Hughes
Google Research (2022)
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Multilingual neural machine translation (NMT) typically learns to maximize the likelihood of training examples from a combination set of multiple language pairs. However, this mechanical combination only relies on the basic sharing to learn the inductive bias, which undermines the generalization and transferability of multilingual NMT models. In this paper, we introduce a multilingual crossover encoder-decoder (mXEnDec) to fuse language pairs at instance level to exploit cross-lingual signals. For better fusions on multilingual data, we propose several techniques to deal with the language interpolation, dissimilar language fusion and heavy data imbalance. Experimental results on a large-scale WMT multilingual data set show that our approach significantly improves model performance on general multilingual test sets and the model transferability on zero-shot test sets (up to $+5.53$ BLEU).
Results on noisy inputs demonstrates the capability of our approach to improve model robustness against the code-switching noise. 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
George Foster
David Grangier
Viresh Ratnakar
Qijun Tan
Transactions of the Association for Computational Linguistics, 9, pp. 1460-1474
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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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Recently, self-supervised pre-training of text representations has been success-fully applied to low-resource Neural Machine Translation (NMT). However, it usually fails to achieve dramatic success on resource-rich NMT. In this paper, we propose a joint training approach, F2-XEnDec, to jointly self-supervised and supervised train NMT models. To this end, a new task called crossover encoder-decoder (XEnDec) is designed to entangle their representations. The key idea is to combine pseudo parallel sentences (also generated byXEnDec)) used in self-supervised training and parallel sentences in supervised training through a second crossover. Experiments on two resource-rich translation benchmarks, WMT’14English-German and English-French, demonstrate our approach achieve substantial improvements over the Transformer. We also show that our approach is capable of improving the model robustness against input perturbations, in particular for code-switched perturbations.
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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-tosequence learning. Experiments on ChineseEnglish, English-French, and English-German
translation benchmarks show that AdvAug achieves significant improvements over the Transformer (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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We investigate the problem of simultaneous machine translation of long-form speech content. We target a continuous speech-to-text scenario, generating translated captions for a live audio feed, such as a lecture or play-by-play commentary. As this scenario allows for revisions to our incremental translations, we adopt a re-translation approach to simultaneous translation, where the source is repeatedly translated from scratch as it grows. This approach naturally exhibits very low latency and high final quality, but at the cost of incremental instability as the output is continuously refined. We experiment with a pipeline of industry-grade speech recognition and translation tools, augmented with simple inference heuristics to improve stability. We use TED Talks as a source of multilingual test data, developing our techniques on English-to-German spoken language translation. Our minimalist approach to simultaneous translation allows us to easily scale our final evaluation to six more target languages, dramatically improving incremental stability for all of them.
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KoBE: Knowledge-Based Machine Translation Evaluation
Zorik Gekhman
Findings of EMNLP (2020)
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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 Ari
George Foster
IWSLT 2020, Association for Computational Linguistics
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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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