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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 Page: 1 2 3 Hits 41 – 54 of 54 41 Visualizing data structures in parsing-based machine translation Weese, Jonathan; Callison-Burch, Chris In: The Prague bulletin of mathematical linguistics. - Praha : Univ. (2010) 93, 127-136 BLLDB OLC Linguistik Show details 42 Integrating output from specialized modules in machine translation : transliterations in Joshua Irvine, Ann; Thornton, Wren; Callison-Burch, Chris... In: The Prague bulletin of mathematical linguistics. - Praha : Univ. (2010) 93, 107-116 BLLDB OLC Linguistik Show details 43 Hierarchical phrase-based grammar extraction in Joshua : suffix arrays and prefix trees Schwartz, Lane; Callison-Burch, Chris In: The Prague bulletin of mathematical linguistics. - Praha : Univ. (2010) 93, 157-166 BLLDB OLC Linguistik Show details 44 Bucking the trend: large-scale cost-focused active learning for statistical machine translation Bloodgood, Michael; Callison-Burch, Chris In: Association for Computational Linguistics. Proceedings of the conference. - Stroudsburg, Penn. : ACL 48 (2010) 2, 854-864 BLLDB Show details 45 Semantically-Informed Syntactic Machine Translation: A Tree-Grafting Approach ... Baker, Kathryn; Bloodgood, Michael; Callison-Burch, Chris; Dorr, Bonnie; Filardo, Nathaniel; Levin, Lori; Miller, Scott; Piatko, Christine. - : Digital Repository at the University of Maryland, 2010 Abstract: We describe a unified and coherent syntactic framework for supporting a semantically-informed syntactic approach to statistical machine translation. Semantically enriched syntactic tags assigned to the target-language training texts improved translation quality. The resulting system significantly outperformed a linguistically naive baseline model (Hiero), and reached the highest scores yet reported on the NIST 2009 Urdu-English translation task. This finding supports the hypothesis (posed by many researchers in the MT community, e.g., in DARPA GALE) that both syntactic and semantic information are critical for improving translation quality—and further demonstrates that large gains can be achieved for low-resource languages with different word order than English. ... : We thank Basis Technology Corporation for their generous contribution of software components to this work. This work is supported, in part, by the Johns Hopkins Human Language Technology Center of Excellence, by the National Science Foundation under grant IIS-0713448, and by BBN Technologies under GALE DARPA/IPTO Contract No. HR0011-06-C-0022. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the sponsor. ... Keyword: artificial intelligence; computational linguistics; computer science; human language technology; machine learning; machine translation; modality; named entities; natural language processing; negation; semantically-informed machine translation; semantically-informed syntactic machine translation; statistical machine translation; statistical methods; translation technology; tree-grafting; Urdu-English translation URL: http://hdl.handle.net/1903/15579 https://dx.doi.org/10.13016/m2h59f BASE Hide details 46 Semantically-Informed Syntactic Machine Translation: A Tree-Grafting Approach Baker, Kathryn; Piatko, Christine; Miller, Scott. - 2010 BASE Show details 47 Using Mechanical Turk to Build Machine Translation Evaluation Sets Bloodgood, Michael; Callison-Burch, Chris. - : Association for Computational Linguistics, 2010 BASE Show details 48 Bucking the Trend: Large-Scale Cost-Focused Active Learning for Statistical Machine Translation Bloodgood, Michael; Callison-Burch, Chris. - : Association for Computational Linguistics, 2010 BASE Show details 49 Decoding in "Joshua" : open source, parsing-based machine translation Li, Zhifei; Thornton, Wren; Callison-Burch, Chris... In: The Prague bulletin of mathematical linguistics. - Praha : Univ. (2009) 91, 47-56 BLLDB OLC Linguistik Show details 50 Constructing corpora for the development and evaluation of paraphrase systems Lapata, Mirella; Callison-Burch, Chris; Cohn, Trevor In: Computational linguistics. - Cambridge, Mass. : MIT Press 34 (2008) 4, 597-614 BLLDB OLC Linguistik Show details 51 Paraphrasing and Translation Callison-Burch, Chris. - : The University of Edinburgh, 2008 BASE Show details 52 Re-evaluating the role of BLEU in machine translation research Callison-Burch, Chris; Osborne, Miles; Koehn, Philipp In: Association for Computational Linguistics / European Chapter. Conference of the European Chapter of the Association for Computational Linguistics. - Menlo Park, Calif. : ACL 11 (2006), 249-256 BLLDB Show details 53 Statistical natural language processing Callison-Burch, Chris; Osborne, Miles In: Handbook for language engineers. - Stanford, Calif. : CSLI Publ. (2003), 269-297 BLLDB Show details 54 Papers from the Aslib conference held on 29 & 30 November 2001 Schachtl, Stefanie (Mitarb.); Wells, Jonathan Nigel (Mitarb.); Ball, S.I. (Mitarb.). - London : Aslib, 2001 BLLDB UB Frankfurt Linguistik Show details Page: 1 2 3

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