Findings of the 2017 Conference on Machine Translation (WMT17)
01 Sep 2017-pp 169-214
TL;DR: The results of the WMT17 shared tasks, which included three machine translation (MT) tasks (news, biomedical, and multimodal), two evaluation tasks (metrics and run-time estimation of MT quality), an automatic post-editing task, a neural MT training task, and a bandit learning task are presented.
Abstract: This paper presents the results of the WMT17 shared tasks, which included
three machine translation (MT) tasks (news, biomedical, and multimodal), two evaluation tasks (metrics and run-time estimation of MT quality), an automatic post-editing task, a neural MT training task, and a bandit learning task.
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23 Apr 2018
TL;DR: Pointing to the success of the parsing community, it is suggested machine translation researchers settle upon the BLEU scheme, which does not allow for user-supplied reference processing, and provide a new tool, SACREBLEU, to facilitate this.
Abstract: The field of machine translation faces an under-recognized problem because of inconsistency in the reporting of scores from its dominant metric. Although people refer to “the” BLEU score, BLEU is in fact a parameterized metric whose values can vary wildly with changes to these parameters. These parameters are often not reported or are hard to find, and consequently, BLEU scores between papers cannot be directly compared. I quantify this variation, finding differences as high as 1.8 between commonly used configurations. The main culprit is different tokenization and normalization schemes applied to the reference. Pointing to the success of the parsing community, I suggest machine translation researchers settle upon the BLEU scheme used by the annual Conference on Machine Translation (WMT), which does not allow for user-supplied reference processing, and provide a new tool, SACREBLEU, to facilitate this.
1,219 citations
Posted Content•
TL;DR: The authors found differences as high as 1.8 between commonly used configurations of the BLEU score between different tokenization and normalization schemes applied to the reference, and suggested that machine translation researchers settle upon the standard WMT scheme, which does not allow for user-supplied reference processing.
Abstract: The field of machine translation faces an under-recognized problem because of inconsistency in the reporting of scores from its dominant metric. Although people refer to "the" BLEU score, BLEU is in fact a parameterized metric whose values can vary wildly with changes to these parameters. These parameters are often not reported or are hard to find, and consequently, BLEU scores between papers cannot be directly compared. I quantify this variation, finding differences as high as 1.8 between commonly used configurations. The main culprit is different tokenization and normalization schemes applied to the reference. Pointing to the success of the parsing community, I suggest machine translation researchers settle upon the BLEU scheme used by the annual Conference on Machine Translation (WMT), which does not allow for user-supplied reference processing, and provide a new tool, SacreBLEU, to facilitate this.
867 citations
Posted Content•
TL;DR: This work creates a true Many-to-Many multilingual translation model that can translate directly between any pair of 100 languages and explores how to effectively increase model capacity through a combination of dense scaling and language-specific sparse parameters to create high quality models.
Abstract: Existing work in translation demonstrated the potential of massively multilingual machine translation by training a single model able to translate between any pair of languages. However, much of this work is English-Centric by training only on data which was translated from or to English. While this is supported by large sources of training data, it does not reflect translation needs worldwide. In this work, we create a true Many-to-Many multilingual translation model that can translate directly between any pair of 100 languages. We build and open source a training dataset that covers thousands of language directions with supervised data, created through large-scale mining. Then, we explore how to effectively increase model capacity through a combination of dense scaling and language-specific sparse parameters to create high quality models. Our focus on non-English-Centric models brings gains of more than 10 BLEU when directly translating between non-English directions while performing competitively to the best single systems of WMT. We open-source our scripts so that others may reproduce the data, evaluation, and final M2M-100 model.
378 citations
Additional excerpts
...We consider data for 13 languages (Ondrej et al., 2017; Bojar et al., 2018; Barrault et al., 2019)....
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Posted Content•
TL;DR: This framework leverages recent breakthroughs in cross-lingual pretrained language modeling resulting in highly multilingual and adaptable MT evaluation models that exploit information from both the source input and a target-language reference translation in order to more accurately predict MT quality.
Abstract: We present COMET, a neural framework for training multilingual machine translation evaluation models which obtains new state-of-the-art levels of correlation with human judgements. Our framework leverages recent breakthroughs in cross-lingual pretrained language modeling resulting in highly multilingual and adaptable MT evaluation models that exploit information from both the source input and a target-language reference translation in order to more accurately predict MT quality. To showcase our framework, we train three models with different types of human judgements: Direct Assessments, Human-mediated Translation Edit Rate and Multidimensional Quality Metrics. Our models achieve new state-of-the-art performance on the WMT 2019 Metrics shared task and demonstrate robustness to high-performing systems.
319 citations
Cites background from "Findings of the 2017 Conference on ..."
...For DA, it is common practice to convert scores into relative rankings (DARR) when the number of annotations per segment is limited (Bojar et al., 2017b; Ma et al., 2018, 2019)....
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...Reference-less MT evaluation, also known as Quality Estimation (QE), has historically often regressed on HTER for segment-level evaluation (Bojar et al., 2013, 2014, 2015, 2016, 2017a)....
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Posted Content•
TL;DR: This work sets a milestone by building a single massively multilingual NMT model handling 103 languages trained on over 25 billion examples, and demonstrates effective transfer learning ability, significantly improving translation quality of low-resource languages, while keeping high-resource language translation quality on-par with competitive bilingual baselines.
Abstract: We introduce our efforts towards building a universal neural machine translation (NMT) system capable of translating between any language pair. We set a milestone towards this goal by building a single massively multilingual NMT model handling 103 languages trained on over 25 billion examples. Our system demonstrates effective transfer learning ability, significantly improving translation quality of low-resource languages, while keeping high-resource language translation quality on-par with competitive bilingual baselines. We provide in-depth analysis of various aspects of model building that are crucial to achieving quality and practicality in universal NMT. While we prototype a high-quality universal translation system, our extensive empirical analysis exposes issues that need to be further addressed, and we suggest directions for future research.
299 citations
Additional excerpts
...…al., 2014; Bahdanau et al., 2014) have been widely adopted as the stateof-the-art approach for machine translation, both in the research community (Bojar et al., 2016a, 2017, 2018b) and for large-scale production systems (Wu et al., 2016; Zhou et al., 2016; Crego et al., 2016; Hassan et al.,…...
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TL;DR: This paper proposed a method of automatic machine translation evaluation that is quick, inexpensive, and language-independent, that correlates highly with human evaluation, and that has little marginal cost per run.
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TL;DR: This paper introduces a simpler and more effective approach, making the NMT model capable of open-vocabulary translation by encoding rare and unknown words as sequences of subword units, and empirically shows that subword models improve over a back-off dictionary baseline for the WMT 15 translation tasks English-German and English-Russian by 1.3 BLEU.
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TL;DR: The functionality of the SRILM toolkit is summarized and its design and implementation is discussed, highlighting ease of rapid prototyping, reusability, and combinability of tools.
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4,904 citations
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TL;DR: It is shown that significantly better results can often be obtained if the final evaluation criterion is taken directly into account as part of the training procedure.
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3,259 citations
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