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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 7 of 7 1 The Impact of language models and loss functions on repair disfluency detection Zwarts, Simon; Johnson, Mark. - : Association for Computational Linguistics (ACL), 2011 BASE Show details 2 Detecting speech repairs incrementally using a noisy channel approach Zwarts, Simon; Johnson, Mark; Dale, Robert. - : Beijing, China : Tsinghua University Press, 2010 BASE Show details 3 Dialogue reference in a visual domain Viethen, Jette; Zwarts, Simon; Dale, Robert. - : Valetta, Malta : European Language Resources Association, 2010 BASE Show details 4 Choosing the right translation : a syntactically informed classification approach Zwarts, Simon; Dras, Mark. - : Association for Computational Linguistics, 2008 BASE Show details 5 Morphosyntactic target language matching in statistical machine translation Zwarts, Simon; Dras, Mark. - : Australia : ALTA, 2008 BASE Show details 6 Syntax-based word reordering in phrase-based statistical machine translation : why does it work? Zwarts, Simon; Dras, Mark. - : Allschwil, Switzerland : European Association for Machine Translation, 2007 BASE Show details 7 This phrase-based SMT system is out of order : generalised word reordering in machine translation Zwarts, Simon; Dras, Mark. - : Carlton, Vic : Australasian Language Technology Association, 2006 Abstract: Many natural language processes have some degree of preprocessing of data: tokenisation, stemming and so on. In the domain of Statistical Machine Translation it has been shown that word reordering as a preprocessing step can help the translation process. Recently, hand-written rules for reordering in German–English translation have shown good results, but this is clearly a labour-intensive and language pair-specific approach. Two possible sources of the observed improvement are that (1) the reordering explicitly matches the syntax of the source language more closely to that of the target language, or that (2) it fits the data better to the mechanisms of phrasal SMT; but it is not clear which. In this paper, we apply a general principle based on dependency distance minimisation to produce reorderings. Our languageindependent approach achieves half of the improvement of a reimplementation of the handcrafted approach, and suggests that reason (2) is a possible explanation for why that reordering approach works. ; 8 page(s) Keyword: 080100 Artificial Intelligence and Image Processing URL: http://hdl.handle.net/1959.14/101350 BASE Hide details

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