Findings of the 2019 Conference on Machine Translation (WMT19)

By Loïc Barrault, Ondrej Bojar, Marta Ruiz Costa-jussà, Christian Federmann, Mark Fishel, Yvette Graham, Barry Haddow, Matthias Huck, Philipp Koehn, Shervin Malmasi and 5 more. 2019

630 citations recorded by the source corpus.

This paper presents the results of the premier shared task organized alongside the Conference on Machine Translation (WMT) 2019. Participants were asked to build machine translation systems for any of 18 language pairs, to be evaluated on a test set of news stories. The main metric for this task is human judgment of translation quality. The task was also opened up to additional test suites to probe specific aspects of translation.

Subfields: Artificial Intelligence, Language and Linguistics, Natural Language Processing, Evaluation & Benchmarks

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Language and Linguistics

In Natural Language Processing

In Evaluation & Benchmarks

Citations in the corpus

CTRL: A Conditional Transformer Language Model for Controllable Generation (2019, background): “We train on 140 GB of text drawing from a wide variety of domains: Wikipedia (En, De, Es, Fr), Project Gutenberg 1 , submissions from 45 subreddits, OpenWebText 2 , a large collection of news data (Hermann et al., 2015; Barrault et al., 2019; Sandhaus, 2008; Grusky et al., 2018), Amazon Reviews (McA”
Beyond English-Centric Multilingual Machine Translation (2020, neutral): “We consider data for 13 languages (Ondrej et al., 2017; Bojar et al., 2018; Barrault et al., 2019).”
mSLAM: Massively multilingual joint pre-training for speech and text (2022, neutral): “, 2020) for ja, ta, WMT19 (Barrault et al., 2019) for de, ru, zh, WMT18 (Bojar et al.”
Scaling Laws for Neural Machine Translation (2021, neutral): “The news-domain test sets come from the WMT2019 (Barrault et al., 2019) evaluation campaign (newstest2019) for all language pairs.”
Nearest Neighbor Machine Translation (2020, methodology): “N EWSTEST : The newstest2018 and newstest2019 test sets from WMT (Bojar et al., 2018; Barrault et al., 2019) are used as validation and test sets for the multilingual experiments.”
Improving Massively Multilingual Neural Machine Translation and Zero-Shot Translation (2020, neutral): “With the great success of neural machine translation (NMT) on bilingual datasets (Bahdanau et al., 2015; Vaswani et al., 2017; Barrault et al., 2019), there is renewed interest in multilingual translation where a single NMT model is optimized for the translation of multiple language pairs (Firat et…”
Stanza: A Python Natural Language Processing Toolkit for Many Human Languages (2020, methodology): “For the character-level language models in the NER component, we pretrained them on a mix of the Common Crawl and Wikipedia dumps, and the news corpora released by the WMT19 Shared Task (Barrault et al., 2019), except for English and Chinese, for which we pretrained on the Google One Billion Word (C”
CCMatrix: Mining Billions of High-Quality Parallel Sentences on the Web (2019, methodology): “The workshop on machine translation (WMT) has a long history of or-ganising evaluations of machine translation, and many comparative results are published for these tasks (Barrault et al., 2019).”
Reducing Gender Bias in Neural Machine Translation as a Domain Adaptation Problem (2020, positive): “For en-de , we use 17.6M sentence pairs from WMT19 news task datasets (Barrault et al., 2019).”
Results of the WMT20 Metrics Shared Task (2020, background): “It has been shown that context is really important when humans are rating MT outputs (Toral et al., 2018), and the WMT human evaluation is moving towards evaluating segments with the document context (Barrault et al., 2019).”