Findings of the 2016 Conference on Machine Translation

By Ondrej Bojar, Rajen Chatterjee, Christian Federmann, Yvette Graham, Barry Haddow, Matthias Huck, Antonio Jimeno-Yepes, Philipp Koehn, Varvara Logacheva, Christof Monz and 11 more. 2016

838 citations recorded by the source corpus.

This paper presents the results of the WMT16 shared tasks, which included five machine translation (MT) tasks (standard news, IT-domain, biomedical, multimodal, pronoun), three evaluation tasks (metrics, tuning, run-time estimation of MT quality), and an automatic post-editing task and bilingual document alignment task. This year, 102 MT systems from 24 institutions (plus 36 anonymized online systems) were submitted to the 12 translation directions in the news translation task. The IT-domain task received 31 submissions from 12 institutions in 7 directions and the Biomedical task received 15 submissions systems from 5 institutions. Evaluation was both automatic and manual (relative ranking and 100-point scale assessments). The quality estimation task had three subtasks, with a total of 14 teams, submitting 39 entries. The automatic post-editing task had a total of 6 teams, submitting 11 entries.

Subfields: Artificial Intelligence, 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 Romanian, which is a standard lower-resource machine translation benchmark, we use the train and validation sets from WMT 2016 [Bojar et al., 2016].”
Massively Multilingual Neural Machine Translation (2019, methodology): “For comparison, this is signiﬁcantly “more multilingual” than the data available from all previous WMT news translation shared task evaluations throughout the years – the latest being Bojar et al. (2016, 2017, 2018), which included 14 languages so far.”
Massively Multilingual Neural Machine Translation in the Wild: Findings and Challenges (2019, background): “…al., 2014; Bahdanau et al., 2014) have been widely adopted as the state-of-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.,…”
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…”
Is Reinforcement Learning (Not) for Natural Language Processing?: Benchmarks, Baselines, and Building Blocks for Natural Language Policy Optimization (2022, background): “WMT-16 (en-de) (Bojar et al., 2016) Machine Translation Text (English) Translated text (German).”
Input-Tuning: Adapting Unfamiliar Inputs to Frozen Pretrained Models (2022, methodology): “For Ro-En, we use the development and test sets from WMT 2016 (Bojar et al., 2016).”
Six Challenges for Neural Machine Translation (2017, background): “12 https://github.com/rsennrich/wmt16-scripts Note that the attention model may produce better word alignments by guided alignment training (Chen et al., 2016; Liu et al., 2016) where supervised word alignments (such as the ones produced by fast-align) are provided to model training.”
Toward Multilingual Neural Machine Translation with Universal Encoder and Decoder (2016, background): “Neural Machine Translation (NMT) has shown its effectiveness in translation tasks when NMT systems perform best in recent machine translation campaigns (Cettolo et al., 2015; Bojar et al., 2016).”
A Challenge Set Approach to Evaluating Machine Translation (2017, methodology): “The WMT 2016 News Translation Task (Bojar et al., 2016) evaluated submitted systems according to both BLEU and human judgments.”
Language Modeling Teaches You More Syntax than Translation Does: Lessons Learned Through Auxiliary Task Analysis (2018, methodology): “We con-trol for the data domain by exclusively training on datasets from the 2016 Conference on Machine Translation (WMT; Bojar et al., 2016).”
Towards a Unified View of Parameter-Efficient Transfer Learning (2021, methodology): “, 2018) is an English summarization dataset where models predict a summary given a news article; (2) English to Romanian translation using the WMT 2016 en-ro dataset (Bojar et al., 2016); (3) MNLI (Williams et al.”