Findings of the 2015 Workshop on Statistical Machine Translation

By Ondrej Bojar, Rajen Chatterjee, Christian Federmann, Barry Haddow, Matthias Huck, Chris Hokamp, Philipp Koehn, Varvara Logacheva, Christof Monz, Matteo Negri and 4 more. 2015

344 citations recorded by the source corpus.

This paper presents the results of the WMT15 shared tasks, which included a standard news translation task, a metrics task, a tuning task, a task for run-time estimation of machine translation quality, and an automatic post-editing task. This year, 68 machine translation systems from 24 institutions were submitted to the ten translation directions in the standard translation task. An additional 7 anonymized systems were included, and were then evaluated both automatically and manually. The quality estimation task had three subtasks, with a total of 10 teams, submitting 34 entries. The pilot automatic postediting task had a total of 4 teams, submitting 7 entries.

Subfields: Artificial Intelligence, Molecular Biology, Natural Language Processing, Evaluation & Benchmarks

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Citations in the corpus

Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer (2019, methodology): “For English to French, we use the standard training data from 2015 and newstest2014 as a validation set [Bojar et al., 2015].”
Improving Neural Machine Translation Models with Monolingual Data (2015, methodology): “We use all parallel training data provided by WMT 2015 (Bojar et al., 2015) 4 .”
Findings of the 2016 Conference on Machine Translation (2016, methodology): “This conference builds on nine previous WMT workshops (Koehn and Monz, 2006; Callison-Burch et al., 2007, 2008, 2009, 2010, 2011, 2012; Bojar et al., 2013, 2014, 2015).”
ExT5: Towards Extreme Multi-Task Scaling for Transfer Learning (2021, neutral): “Concretely, we fine-tune and evaluate on Machine Translation: translating sentences from English to other languages (Bojar et al., 2014; 2015; 2016); Reasoning: answering scientific questions on ARC (Clark et al., 2018); and Named Entity Recognition: extracting all entities from sentences on the…”
mSLAM: Massively multilingual joint pre-training for speech and text (2022, neutral): “, 2017) for lv, WMT15 (Bojar et al., 2015) for fr, WMT13 (Bojar et al.”
Findings of the 2018 Conference on Machine Translation (WMT18) (2018, neutral): “This conference builds on twelve previous editions of WMT as workshops and conferences (Koehn and Monz, 2006; Callison-Burch et al., 2007, 2008, 2009, 2010, 2011, 2012; Bojar et al., 2013, 2014, 2015, 2016a, 2017).”
Fast Domain Adaptation for Neural Machine Translation (2016, methodology): “For the German → English translation task, we use an already trained out-of-domain NMT sys-tem (vocabulary size N =100K) trained on the WMT 2015 training data [Bojar et al., 2015] (3.9M parallel sentences).”
SYSTRAN's Pure Neural Machine Translation Systems (2016, methodology): “The generic model is a subsample of the corpora made available for the WMT15 translation task (Bojar et al., 2015).”
Style Transfer Through Back-Translation (2018, background): “In sociolinguistics, gender is known to be one of the most important social categories driving language choice (Eckert and McConnellGinet, 2003; Lakoff and Bucholtz, 2004; Coates, 2015).”