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Advanced MT Seminar Spring 2010

Advanced MT Seminar Spring 2010. Papers and Presentations. Kevin Gimpel.

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Advanced MT Seminar Spring 2010

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  1. Advanced MT SeminarSpring 2010 Papers and Presentations

  2. Kevin Gimpel • Translation Modeling:S. DeNeefe and K. Knight. Synchronous Tree Adjoining Machine Translation, EMNLP 2009.Link: http://www.isi.edu/natural-language/mt/adjoin09.pdfMichael Auli, Adam Lopez, Hieu Hoang, and Philipp Koehn.  A Systematic Analysis of Translation Model Search Spaces. WMT 2009.Link: http://homepages.inf.ed.ac.uk/alopez/papers/wmt09-auli+lopez+hoang+koehn.pdfAlexandra Birch, Phil Blunsom and Miles Osborne. A Quantitative Analysis of Reordering Phenomena. WMT 2009.Link: http://www.aclweb.org/anthology/W/W09/W09-0434.pdf • Alternative Training Criteria:Adam Pauls, John DeNero, and Dan Klein. Consensus Training for Consensus Decoding in Machine Translation. EMNLP 2009.Link: http://www.eecs.berkeley.edu/~denero/research/papers/emnlp09_pauls_tuning.pdfZhifei Li and Sanjeev Khudanpur. Forest Reranking for Machine Translation with the Perceptron Algorithm. To appear in the GALE book chapter on "MT from text", 2009.Link: http://www.cs.jhu.edu/%7Ezfli/pubs/forest_reranking_mt_zhifei_galebook09.pdf • Presentation dates: prefer to not lead discussion on February 3rd or 10th.

  3. Greg Hanneman • "Decoding by Dynamic Chunking for Statistical Machine Translation"Yahyaei and MonzMT Summit 2009http://www.mt-archive.info/MTS-2009-Yahyaei.pdf"Learning Accurate, Compact, and Interpretable Tree Annotation"Petrov, Barrett, Thibaux, and KleinACL 2006http://www.aclweb.org/anthology/P/P06/P06-1055.pdf"Discriminative Reordering with Chinese Grammatical Relations Features"Chang, Tseng, Jurafsky, and ManningSSST 2009http://www.aclweb.org/anthology-new/W/W09/W09-2307.pdf"Rule Filtering by Pattern for Efficient Hierarchical Translation"Iglesias, de Gispert, Banga, and ByrneEACL 2009http://www.aclweb.org/anthology-new/E/E09/E09-1044.pdf"Improved Word Alignment with Statistics and Linguistic Heuristics"HermjakobEMNLP 2009http://www.aclweb.org/anthology-new/D/D09/D09-1024.pdf

  4. Jaedy Kim • 1. Topic List     MT approach convergence     Data processing (pre- and post-)     Automatic transfer rule learning     Efficient decoding (for SMT, Syntax-based MT) 2. Paper Selection     Chunk-Level Reordering of Source Language Sentences with Automatically Learned Rules for Statistical Machine Translation.: Y. Zhang, R. Zens and H. Ney     Example-based Machine Translation Based on Syntactic Transfer with Statistical Models: Kenji Imamura, Hideo Okuma, Taro Watanabe and Eiichiro Sumita (ATR)     Pivot Language Approach for Phrase-Based Statistical Machine Translation: H. Wu and  H. Wang, (Toshiba, Beijing)     Information Retrieval as Statistical Translation : A. Berger and J. Lafferty     Generating Complex Morphology for Machine Translation: E. Minkov, K. Toutanova and H. Suzuki (MSR)

  5. Michael Denkowski • Variational Decoding for Statistical Machine Translation(Zhifei Li; Jason Eisner; Sanjeev Khudanpur)http://aclweb.org/anthology-new/P/P09/P09-1067.pdfEfficient Minimum Error Rate Training and Minimum Bayes-Risk Decoding for Translation Hypergraphs and Lattices(Shankar Kumar; Wolfgang Macherey; Chris Dyer; Franz Och)http://aclweb.org/anthology-new/P/P09/P09-1019.pdfImproved Statistical Machine Translation Using Monolingually-Derived Paraphrases (Yuval Marton; Chris Callison-Burch; Philip Resnik)http://www.aclweb.org/anthology/D/D09/D09-1040.pdfUsing a maximum entropy model to build segmentation lattices for MT(Chris Dyer)http://www.aclweb.org/anthology/N/N09/N09-1046.pdfFeasibility of Human-in-the-loop Minimum Error Rate Training (Omar F. Zaidan; Chris Callison-Burch)http://www.aclweb.org/anthology/D/D09/D09-1006.pdfThe Contribution of Linguistic Features to Automatic Machine Translation Evaluation(Enrique Amigó; Jesús Giménez; Julio Gonzalo; Felisa Verdejo)http://aclweb.org/anthology-new/P/P09/P09-1035.pdf

  6. Nguyen Bach • + Sentence SimplificationHybrid Spoken Language Translation. Using Sentence Splitting Based on Syntax Structure. Satoshi Kamatani, Tetsuro Chino and Kazuo Sumita, MT-Summit 2009Sentence Compression Beyond Word Deletion, Trevor Cohn and Mirella Lapata, COLING 2008A Maximum Entropy-based Sentence Simplifier for Machine Translation, Finch et al., NLP-KE-2005Sentence splitting based on n-grams and select the best one by measuring sentence similarity, Doi and Sumita,  COLING 2004 • + Word Sense DisambiguationWord sense disambiguation improves statistical machine translation. Yee Seng Chan, Hwee Tou Ng, and David Chiang, ACL-2007 • I prefer to discuss these papers sometimes on the 3rd or 4th week of March.

  7. Qin Gao • Bodrumlu, T., K. Knight, and S. Ravi. “A new objective function for word alignment.” Proceedings of the Workshop on Integer Linear Programming for Natural Langauge Processing. 2009. 28–35. Link • Cherry, C., and D. Lin. “A probability model to improve word alignment.” Proceedings of the 41st Annual Meeting on Association for Computational Linguistics. 2003. 88–95. Link (Early paper) • Fossum, V., K. Knight, and S. Abney. “Using syntax to improve word alignment precision for syntax-based machine translation.” Proceedings of the Third Workshop on Statistical Machine Translation. 2008. 44–52. Link • Fraser, A., and D. Marcu. “Getting the structure right for word alignment: LEAF.” Proc. EMNLP-CoNLL. 2007. Link • Hermjakob, U. “Improved Word Alignment with Statistics and Linguistic Heuristics.”ACL 2009 Link

  8. Jon Clark • Cube Pruning as A* Search (LanguageWeaver): http://www.aclweb.org/anthology/D/D09/D09-1007.pdf • Efficient Parsing for Transducer Grammars (DeNeero): http://www.eecs.berkeley.edu/~denero/research/papers/naacl09_denero_parsing.pdf • Unaligned Word Removal (Aachen): http://www.mt-archive.info/EAMT-2009-Zhang.pdf • Bayesian Tree to String Grammar Induction (Blunsom) http://www.clg.ox.ac.uk/blunsom/pubs/cohn-blunsom-emnlp09.pdf • MERT and MBR on Hypergraphs (Dyer) http://www.aclweb.org/anthology-new/P/P09/P09-1019.pdf

  9. Schedule • Jan 27: Hassan Al-Haj • Feb 3: • Feb 10: • Feb 17: • Feb 24: • Mar 3: • Mar 10: • Mar 17: • Mar 24: • Mar 31: • Apr 7: • Apr 14: • Apr 21: • Apr 28:

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