DiscoMT

Discourse and MT

• Mitkov, Ruslan. How could rhetorical relations be used in machine translation?(And at least two open questions). Proceedings of the ACL Workshop on Intentionality and Structure in Discourse Relations. 1993.
• Marcu, Daniel, Lynn Carlson, and Maki Watanabe. The automatic translation of discourse structures. Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference. Association for Computational Linguistics, 2000.
• Carpuat, Marine. One translation per discourse. Proceedings of the Workshop on Semantic Evaluations: Recent Achievements and Future Directions. Association for Computational Linguistics, 2009.
• Tiedemann, Jörg. Context adaptation in statistical machine translation using models with exponentially decaying cache. Proceedings of the 2010 Workshop on Domain Adaptation for Natural Language Processing. Association for Computational Linguistics, 2010.
• Tiedemann, Jörg. To cache or not to cache?: experiments with adaptive models in statistical machine translation. Proceedings of the Joint Fifth Workshop on Statistical Machine Translation and MetricsMATR. Association for Computational Linguistics, 2010.
• Meyer, Thomas, et al. Multilingual annotation and disambiguation of discourse connectives for machine translation. Proceedings of the SIGDIAL 2011 Conference. Association for Computational Linguistics, 2011.
• Ture, Ferhan, Douglas W. Oard, and Philip Resnik. Encouraging consistent translation choices. Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Association for Computational Linguistics, 2012.
• Meyer, Thomas, and Andrei Popescu-Belis. Using sense-labeled discourse connectives for statistical machine translation. Proceedings of the Joint Workshop on Exploiting Synergies between Information Retrieval and Machine Translation (ESIRMT) and Hybrid Approaches to Machine Translation (HyTra). Association for Computational Linguistics, 2012.
• Meyer, Thomas, and Bonnie Webber. Implicitation of discourse connectives in (machine) translation. Proceedings of the 1st DiscoMT Workshop at ACL 2013 (51st Annual Meeting of the Association for Computational Linguistics). No. EPFL-CONF-192528. 2013.
• Carpuat, Marine, and Michel Simard. The trouble with SMT consistency. Proceedings of the Seventh Workshop on Statistical Machine Translation. Association for Computational Linguistics, 2012.
• Meyer, Thomas, and Lucie Poláková. Machine translation with many manually labeled discourse connectives. Proceedings of the 1st DiscoMT Workshop at ACL 2013 (51st Annual Meeting of the Association for Computational Linguistics). 2013.
• Francisco Guzmán, Shafiq Joty, Lluís Màrquez, and Preslav Nakov. *Using Discourse Structure Improves Machine Translation Evaluation". ACL. 2014.
• Li, Junyi Jessy, Marine Carpuat, and Ani Nenkova. Cross-lingual Discourse Relation Analysis: A corpus study and a semi-supervised classification system. COLING. 2014
• Li, Junyi Jessy, Marine Carpuat, and Ani Nenkova. Assessing the Discourse Factors that Influence the Quality of Machine Translation. ACL. 2014.
• Hardmeier, Christian. Discourse in Statistical Machine Translation. 2014.
• Webber, Bonnie. Discourse for Machine Translation. 2014.

Neural Networks for MT

• Sutskever, Ilya, Oriol Vinyals, and Quoc V Le. Sequence to sequence learning with neural networks. Advances in Neural Information Processing Systems. 2014.
• Baltescu, Paul and Blunsom, Phil. Pragmatic neural language modeling in machine translation. arXiv:1412.7119v2. 2014.
• Cho, Kyunghyun, et al. On the properties of neural machine translation: Encoder-decoder approaches. arXiv:1409.1259. 2014.
• Cho, Kyunghyun, et al. Learning phrase representations using RNN encoder-decoder for statistical machine translation.. arXiv:1406.1078. 2014.
• Bahdanau, Dzmitry, Kyunghyun Cho, and Yoshua Bengio. Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473. 2014.
• Meng, Fandong, et al. Encoding source language with Convolutional Neural Network for machine translation. arXiv:1503.01838v2. 2015.

Coherence and Entity Grid

• Grosz, Barbara J., Scott Weinstein, and Aravind K. Joshi. Centering: A framework for modeling the local coherence of discourse. Computational linguistics 21.2 (1995): 203-225.
• Barzilay, Regina, and Lillian Lee. Catching the drift: Probabilistic content models, with applications to generation and summarization. HLT-NAACL. 2004
• Miltsakaki, Eleni, and Karen Kukich. Evaluation of text coherence for electronic essay scoring systems. Natural Language Engineering. 2004.
• Lapata, Mirella, and Regina Barzilay. Automatic evaluation of text coherence: Models and representations. IJCAI. 2005.
• Elsner, Micha, Joseph L. Austerweil, and Eugene Charniak. A Unified Local and Global Model for Discourse Coherence. HLT-NAACL. 2007.
• Elsner, Micha, and Eugene Charniak. Coreference-inspired coherence modeling. ACL. 2008.
• Barzilay, Regina, and Mirella Lapata. Modeling local coherence: An entity-based approach. Computational Linguistics 34.1 (2008): 1-34.
• Eisner, Micha, and Eugene Charniak. Extending the entity grid with entity-specific features. ACL. 2011.