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1	Deep Syntax in Statistical Machine Translation
	Graham, Yvette. - : Dublin City University. National Centre for Language Technology (NCLT), 2011. : Dublin City University. School of Computing, 2011
	In: Graham, Yvette (2011) Deep Syntax in Statistical Machine Translation. PhD thesis, Dublin City University. (2011)
	Abstract: Statistical Machine Translation (SMT) via deep syntactic transfer employs a three-stage architecture, (i) parse source language (SL) input, (ii) transfer SL deep syntactic structure to the target language (TL), and (iii) generate a TL translation. The deep syntactic transfer architecture achieves a high level of language pair independence compared to other Machine Translation (MT) approaches, as translation is carried out at the more language independent deep syntactic representation. TL word order can be generated independently of SL word order and therefore no reordering model between source and target words is required. In addition, words in dependency relations are adjacent in the deep syntactic structure, allowing the extraction of more general transfer rules, compared to other rules/phrases extracted from the surface form corpus, as such words are often distant in surface form strings, as well as allowing the use of a TL deep syntax language model, which models a deeper notion of fluency than a string-based language model and may lead to better lexical choice. The deep syntactic representation also contains words in lemma form with morpho-syntactic information, and this enables new inflections of lemmas not observed in bilingual training data, that are out of coverage for other SMT approaches, to fall within coverage of deep syntactic transfer. In this thesis, we adapt existing methods already successful in Phrase-Based SMT (PB-SMT) to deep syntactic transfer as well as presenting new methods of our own. We present a new definition for consistent deep syntax transfer rules, inspired by the definition for a consistent phrase in PB-SMT, and we extract all rules consistent with the node alignment, as smaller rules provide high coverage of unseen data, while larger rules provide more fluent combinations of TL words. Since large numbers of consistent transfer rules exist per sentence pair, we also provide an efficient method of extracting rules as well as an efficient method of storing them. We also present a deep syntax translation model, as in other SMT approaches, we use a log-linear combination of features functions, and include a translation model computed from relative frequencies of transfer rules, lexical weighting, as well as a deep syntax language model and string-based language model. In addition, we describe methods of carrying out transfer decoding, the search for TL deep syntactic structures, and how we efficiently integrate a deep syntax trigram language model to decoding, as well as methods of translating morpho-syntactic information separately from lemmas, using an adaptation of Factored Models. Finally, we include an experimental evaluation, in which we compare MT output for different configurations of our SMT via deep syntactic transfer system. We investigate various methods of word alignment, methods of translating morpho-syntactic information, limits on transfer rule size, different beam sizes during transfer decoding, generating from different sized lists of TL decoder output structures, as well as deterministic versus non-deterministic generation. We also include an evaluation of the deep syntax language model in isolation to the MT system and compare it to a string-based language model. Finally, we compare the performance and types of translations our system produces with a state-of-the-art phrase-based statistical machine translation system and although the deep syntax system in general currently under-performs, it does achieve state-of-the-art performance for translation of a specific syntactic construction, the compound noun, and for translations within coverage of the TL precision grammar used for generation. We provide the software for transfer rule extraction, as well as the transfer decoder, as open source tools to assist future research.
	Keyword: Lexical Functional Grammar; Machine translating
	URL: http://doras.dcu.ie/16078/
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2	Deep syntax language models and statistical machine translation
	Graham, Yvette; van Genabith, Josef
	In: Graham, Yvette and van Genabith, Josef orcid:0000-0003-1322-7944 (2010) Deep syntax language models and statistical machine translation. In: SSST-4 - 4th Workshop on Syntax and Structure in Statistical Translation at COLING 2010, 28 August 2010, Beijing, China. (2010)
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3	Guessing the grammatical function of a non-root f-structure in LFG
	Bryl, Anton; van Genabith, Josef; Graham, Yvette
	In: Bryl, Anton, van Genabith, Josef and Graham, Yvette orcid:0000-0003-1322-7944 (2009) Guessing the grammatical function of a non-root f-structure in LFG. In: IWPT 2009 - 11th International Conference on Parsing Technologies, 7-9 October 2009, Paris, France. (2009)
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4	F-structure transfer-based statistical machine translation
	Graham, Yvette; van Genabith, Josef; Bryl, Anton
	In: Graham, Yvette, van Genabith, Josef orcid:0000-0003-1322-7944 and Bryl, Anton (2009) F-structure transfer-based statistical machine translation. In: Lexical Functional Grammar 2009, 13-16 July 2009, Cambridge, UK. (2009)
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5	Packed rules for automatic transfer-rule induction
	Graham, Yvette; van Genabith, Josef
	In: Graham, Yvette and van Genabith, Josef (2008) Packed rules for automatic transfer-rule induction. In: the European Association of Machine Translation Conference 2008, Hamburg, Germany. (2008)
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6	Using F-structures in machine translation evaluation
	Owczarzak, Karolina; van Genabith, Josef; Graham, Yvette...
	In: Owczarzak, Karolina, van Genabith, Josef, Graham, Yvette and Way, Andy orcid:0000-0001-5736-5930 (2007) Using F-structures in machine translation evaluation. In: Lexical Functional Grammar 2007, 28-30 July 2007, California, USA. (2007)
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