The IWSLT 2015 Evaluation Campaign

By Mauro Cettolo, Jan Niehues, Sebastian Stüker, Luisa Bentivogli, Roldano Cattoni, Marcello Federico. 2015

165 citations recorded by the source corpus.

Subfields: Natural Language Processing, Evaluation & Benchmarks

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Citations in the corpus

Semi-Supervised Sequence Modeling with Cross-View Training (2018, methodology): “Machine Translation: We use the English-Vietnamese translation dataset from IWSLT 2015 (Cettolo et al., 2015).”
Learned in Translation: Contextualized Word Vectors (2017, methodology): “Our medium-sized MT dataset is the 2016 version of the machine translation task prepared for the International Workshop on Spoken Language Translation [Cettolo et al., 2015].”
Multilingual Denoising Pre-training for Neural Machine Translation (2020, methodology): “The Zh-En dataset is from IWSLT 2014 and 2015 (Cettolo et al., 2012, 2015).”
Masked Language Model Scoring (2019, methodology): “, 2018) and for IWSLT 2015 English-Vietnamese (Cettolo et al., 2015), which we also describe in a dedicated, concurrent work (Nguyen and Salazar, 2019).”
Understanding and Improving Layer Normalization (2019, neutral): “Machine translation includes three widely-used datasets, WMT English-German (En-De), IWSLT 14 German-English (De-En) [Cettolo et al., 2014] and IWSLT 15 English-Vietnamese (En-Vi) [Cettolo et al., 2015].”
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).”
Transformers without Tears: Improving the Normalization of Self-Attention (2019, methodology): “On five low-resource pairs from the TED Talks [10] and IWSLT '15 [11] corpora, we first train state-of-theart Transformer models (+4.”
Online and Linear-Time Attention by Enforcing Monotonic Alignments (2017, methodology): “Our translation experiments therefore involved English to Vietnamese translation using the parallel corpus of TED talks (133K sentence pairs) provided by the IWSLT 2015 Evaluation Campaign (Cettolo et al., 2015).”
An Empirical Comparison of Domain Adaptation Methods for Neural Machine Translation (2017, methodology): “The spoken domain MT was conducted on the Chinese-English subtask (IWSLT-CE) of the TED talk MT task at the IWSLT 2015 workshop (Cettolo et al., 2015).”
A Survey of Domain Adaptation for Neural Machine Translation (2018, background): “, 2013), but for the spoken language domain parallel corpus there are only 200k sentences available (Cettolo et al., 2015).”
Overview of the IWSLT 2017 Evaluation Campaign (2017, methodology): “For each language direction, the output of the selected 9 systems on the HE set was assigned to professional translators to be post-edited (for all the details about data preparation and postediting see [20, 21, 22]).”
Towards Neural Phrase-based Machine Translation (2017, methodology): “Experimental results show that NPMT outperforms attention-based NMT baselines in terms of the BLEU score (Papineni et al., 2002) on IWSLT 2014 German-English/English-German and IWSLT 2015 English-Vietnamese translation tasks.”
FINDINGS OF THE IWSLT 2020 EVALUATION CAMPAIGN (2020, unknown): “…conference (Akiba et al., 2004; Eck and Hori, 2005; Paul, 2006; Fordyce, 2007; Paul, 2008, 2009; Paul et al., 2010; Federico et al., 2011, 2012; Cettolo et al., 2013, 2014, 2015, 2016, 2017; Niehues et al., 2018, 2019) for the study, development and evaluation of spoken language translation…”