Findings of the 2013 Workshop on Statistical Machine Translation

By Ondrej Bojar, Christian Buck, Chris Callison-Burch, Christian Federmann, Barry Haddow, Philipp Koehn, Christof Monz, Matt Post, Radu Soricut, Lucia Specia. 2013

387 citations recorded by the source corpus.

Subfields: Natural Language Processing, Evaluation & Benchmarks

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Citations in the corpus

SemEval-2017 Task 1: Semantic Textual Similarity Multilingual and Crosslingual Focused Evaluation (2017, methodology): “We release one thousand new SpanishEnglish STS pairs sourced from the 2013 WMT translation task and produced by a phrase-based Moses SMT system (Bojar et al., 2013).”
Edinburgh’s Phrase-based Machine Translation Systems for WMT-14 (2014, methodology): “, 2013b) that we used for the Eighth Workshop on Statistical Machine Translation (Bojar et al., 2013).”
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).”
Findings of the 2014 Workshop on Statistical Machine Translation (2014, methodology): “It has proved robust across a range of language pairs, MT systems, and text domains for predicting various forms of post-editing effort (Callison-Burch et al., 2012; Bojar et al., 2013).”
Complete Multilingual Neural Machine Translation (2020, neutral): “The selection of the languages was driven by the fact that the WMT 2013 evaluation campaign (Bojar et al., 2013) Some of the extracted non-English training examples are multi-way parallel by construction.”
Scalable Cross Lingual Pivots to Model Pronoun Gender for Translation (2020, positive): “To understand why this eﬀect is so strong, we analyzed the development set of WMT’13 (Table 7).”
mSLAM: Massively multilingual joint pre-training for speech and text (2022, neutral): “…et al., 2020) for ja, ta, WMT19 (Barrault et al., 2019) for de, ru, zh, WMT18 (Bojar et al., 2018) for et, tr, WMT17 (Bojar et al., 2017) for lv, WMT15 (Bojar et al., 2015) for fr, WMT13 (Bojar et al., 2013) and TED59 (Qi et al., 2018) for ar, fa, id, it, nl, pt, sl, sv, leaving ca and cy unpaired.”
chrF: character n-gram F-score for automatic MT evaluation (2015, methodology): “The CHR F scores were calculated for all available translation outputs from the WMT 12 (Callison-Burch et al., 2012), WMT 13 (Bojar et al., 2013) and WMT 14 (Bojar et al., 2014) shared tasks, and then compared with human rankings.”
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).”
Findings of the 2017 Conference on Machine Translation (WMT17) (2017, methodology): “This conference builds on eleven previous editions of WMT as workshops and conference (Koehn and Monz, 2006; Callison-Burch et al., 2007, 2008, 2009, 2010, 2011, 2012; Bojar et al., 2013, 2014, 2015, 2016a).”
Findings of the 2019 Conference on Machine Translation (WMT19) (2019, neutral): “This conference builds on 13 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, 2016, 2017, 2018).”
Results of the WMT16 Metrics Shared Task (2016, methodology): “The RR manual judgments were provided by MT researchers taking part in WMT tasks, as in recent years of the campaign, after it was empirically established that judgments of RR collected through crowd-sourcing platforms were not reliable (Bojar et al., 2013).”
Accurate Evaluation of Segment-level Machine Translation Metrics (2015, methodology): “We examine the accuracy of segment scores collected with our proposed method by replicating components of the WMT-13 human evaluation (Bojar et al., 2013b), with the sole aim of optimizing agreement in segment scores to provide an effective gold standard for evaluating segment-level metrics.”
Integrating an Unsupervised Transliteration Model into Statistical Machine Translation (2014, methodology): “For Russian, we used WMT-13 data (Bojar et al., 2013), and we used half of the news-test2012 for tuning and other half for testing.”