Investigating the Usefulness of Generalized Word Representations in SMT

By Nadir Durrani, Philipp Koehn, Helmut Schmid, Alexander M. Fraser. 2014

40 citations recorded by the source corpus.

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In Natural Language Processing

Citations in the corpus

Edinburgh’s Phrase-based Machine Translation Systems for WMT-14 (2014, methodology): “We explored the use of automatic word clusters in phrase-based models (Durrani et al., 2014a).”
What do Neural Machine Translation Models Learn about Morphology? (2017, background): “…systems that deal with morphologically-rich languages resort to various techniques for representing morphological knowledge, such as word segmentation (Nieﬂen and Ney, 2000 Knight, 2003; Badr et al., 2008) and factored translation and reordering models (Koehn and Hoang, 2007; Durrani et al., 2014).”
The Edinburgh/JHU Phrase-based Machine Translation Systems for WMT 2015 (2015, methodology): “In Edinburgh’s submission from the previous year, we used automatically generated word classes in additional language models and in additional operation sequence models (Durrani et al., 2014b).”
Edinburgh SLT and MT system description for the IWSLT 2014 evaluation (2014, methodology): “We explored the use of automatic word clusters in phrase-based models [9].”
Combined spoken language translation (2014, methodology): “Common features included in the UEDIN phrase-based systems are the language model, phrase translation scores in both directions smoothed with Good-Turing discounting, lexical translation scores in both directions, word and phrase penalties, six simple count-based binary features, distancebased disto”
Improving machine translation via triangulation and transliteration (2014, unknown): “…additionally used hierarchical lexicalized reordering (Galley and Manning, 2008), a 5-gram OSM, (Durrani et al., 2013), sparse lexical and domain features, (Hasler et al., 2012), class-based models (Durrani et al., 2014b), a distortion limit of 6, and the no-reordering-over-punctuation heuristic.”
On the Linguistic Representational Power of Neural Machine Translation Models (2019, background): “2015), rich morphology (Koehn and Hoang 2007; Durrani et al. 2014a), and a smorgasbord of features (Chiang, Knight, and Wang 2009) for modeling different phenomena.”
Understanding and Improving Morphological Learning in the Neural Machine Translation Decoder (2017, methodology): “There have been previous efforts to integrate morphology into MT systems by learning factored models (Koehn and Hoang, 2007; Durrani et al., 2014b) over POS and morphological tags.”
Farasa: A Fast and Furious Segmenter for Arabic (2016, methodology): “We used an unsupervised transliteration model (Durrani et al., 2014c) to transliterate the OOV words.”
On internal language representations in deep learning: an analysis of machine translation and speech recognition (2018, methodology): “Machine translation systems that deal with morphologically-rich languages resort to various techniques for representing morphological knowledge, such as word segmentation [15, 184, 258] and factored translation and reordering models [98, 183]; see [181] for an overview.”