Bibliography Of Thesis

Bibliography Of Thesis-56
Robert F Simmons, Sheldon Klein, and Keren Mc Conlogue. Indexing and depen- dency logic for answering English questions. Rupesh K Srivastava, Klaus Greff, and Ju ̈rgen Schmidhuber. Saku Sugawara, Kentaro Inui, Satoshi Sekine, and Akiko Aizawa. Saku Sugawara, Yusuke Kido, Hikaru Yokono, and Akiko Aizawa. Evaluation met- rics for machine reading comprehension: Prerequisite skills and readability. Sequence to sequence learning with neural networks. The web as a knowledge-base for answering com- plex questions. Wenhui Wang, Nan Yang, Furu Wei, Baobao Chang, and Ming Zhou. Gated self- matching networks for reading comprehension and question answering. In Association for Computational Linguistics (ACL), volume 1, pages 1405–1414. In International Conference on Learning Representations (ICLR). In International Conference on Learn- ing Representations (ICLR).In Associa- tion for Computational Linguistics (ACL), volume 1, pages 806–817. In Advances in Neural Information Processing Systems (NIPS), pages 3104–3112. In North American Association for Computational Linguistics (NAACL), volume 1, pages 641–651. In Association for Computational Linguistics (ACL), volume 1, pages 189–198. In 3rd Workshop on Noisy User-generated Text, pages 94–106. In Interna- tional Conference on Learning Representations (ICLR). Zhilin Yang, Peng Qi, Saizheng Zhang, Yoshua Bengio, William Cohen, Ruslan Salakhut- dinov, and Christopher D Manning. Hotpot QA: A dataset for diverse, explainable multi-hop question answering.

In Association for Computational Linguistics (ACL): System Demonstrations, pages 55–60.

Bryan Mc Cann, James Bradbury, Caiming Xiong, and Richard Socher. Learned in translation: Contextualized word vectors. In Advances in Neural Information Processing Systems (NIPS), pages 6297–6308. Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean. Dis- tributed representations of words and phrases and their compositionality.

In Empirical Methods in Natural Language Processing (EMNLP), pages 2249–2255. In Association for Computational Linguistics (ACL), pages 1470–1480. In Empirical Methods in Natural Language Processing (EMNLP), pages 1532–1543. In North American Association for Computational Linguistics (NAACL), volume 1, pages 2227– 2237. Martin Raison, Pierre-Emmanuel Mazare ́, Rajarshi Das, and Antoine Bordes. Weaver: Deep co-encoding of questions and documents for machine reading. Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang. SQu AD: 100,000 questions for machine comprehension of text. In ANLP/NAACL Workshop on Reading comprehension tests as evaluation for computer-based language understanding sytems, pages 13–19. Marzieh Saeidi, Max Bartolo, Patrick Lewis, Sameer Singh, Tim Rockta ̈schel, Mike Shel- don, Guillaume Bouchard, and Sebastian Riedel. Interpretation of natural language rules in conversational machine reading. Complex sequential question answering: Towards learning to converse over linked question answer pairs with a knowledge graph. Minjoon Seo, Aniruddha Kembhavi, Ali Farhadi, and Hannaneh Hajishirzi. Bidi- rectional attention flow for machine comprehension. Hai Wang, Mohit Bansal, Kevin Gimpel, and David Mc Allester. Machine compre- hension with syntax, frames, and semantics. Machine comprehension using Match-LSTM and answer pointer.

Jeffrey Pennington, Richard Socher, and Christopher Manning. Matthew Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer. Alec Radford, Karthik Narasimhan, Tim Salimans, and Ilya Sutskever. Improving language understanding by generative pre-training. In Empirical Methods in Natu- ral Language Processing (EMNLP), pages 2383–2392. Se- quence level training with recurrent neural networks. Co QA: A conversational question answering challenge. In Empirical Methods in Natural Language Processing (EMNLP), pages 2087–2097. Khapra, Karthik Sankaranarayanan, and Sarath Chandar. In International Conference on Learning Representations (ICLR). In International Conference on Learning Representations (ICLR). In Association for Computational Linguis- tics (ACL), volume 2, pages 700–706. In International Conference on Learning Representations (ICLR).

In Association for Computational Linguistics (ACL), pages 1003–1011.

Tom M Mitchell, Justin Betteridge, Andrew Carlson, Estevam Hruschka, and Richard Wang. Populating the semantic web by macro-reading internet text.It is included at the end of your report, on the last page (or last few pages).You will find it easier to prepare your final bibliography if you keep track of each book, encyclopedia, or article you use as you are reading and taking notes. In ACL workshop on intrinsic and extrinsic evaluation measures for machine translation and/or summarization, pages 65–72. In POSTER 2015—19th International Student Conference on Electrical Engineering, pages 1156– 1165. In North American Asso- ciation for Computational Linguistics (NAACL), pages 110–119. In Association for Computational Linguistics (ACL), volume 1, pages 1736–1745. METEOR: An automatic metric for mt evalu- ation with improved correlation with human judgments. Yoda QA: a modular question answering system pipeline. Jiwei Li, Michel Galley, Chris Brockett, Jianfeng Gao, and Bill Dolan. A diversity- promoting objective function for neural conversation models. ROUGE: A package for automatic evaluation of summaries. Yankai Lin, Haozhe Ji, Zhiyuan Liu, and Maosong Sun. Denoising distantly super- vised open-domain question answering.Makarand Tapaswi, Yukun Zhu, Rainer Stiefelhagen, Antonio Torralba, Raquel Urtasun, and Sanja Fidler. Movie QA: Understanding stories in movies through question- answering. Kilian Weinberger, Anirban Dasgupta, John Langford, Alex Smola, and Josh Attenberg. Feature hashing for large scale multitask learning. Johannes Welbl, Pontus Stenetorp, and Sebastian Riedel. Constructing datasets for multi-hop reading comprehension across documents. Qiang Wu, Christopher JC Burges, Krysta M Svore, and Jianfeng Gao. Adapting boosting for information retrieval measures. In Empirical Methods in Natural Language Processing (EMNLP), pages 2369–2380.In Conference on computer vision and pattern recognition (CVPR), pages 4631–4640. In Ad- vances in Neural Information Processing Systems (NIPS), pages 5998–6008. In International Conference on Machine Learning (ICML), pages 1113–1120. Transactions of the Association for Computational Linguistics, 7–302. Xuchen Yao, Jonathan Berant, and Benjamin Van Durme. Freebase QA: Information extraction or semantic parsing?Kenton Lee, Shimi Salant, Tom Kwiatkowski, Ankur Parikh, Dipanjan Das, and Jonathan Berant. Learning recurrent span representations for extractive question answering. Christopher D Manning, Mihai Surdeanu, John Bauer, Jenny Finkel, Steven J Bethard, and David Mc Closky. The Stanford Core NLP natural language processing toolkit. In Empirical Methods in Natural Language Processing (EMNLP), pages 4470–4481. Jacob Andreas, Marcus Rohrbach, Trevor Darrell, and Dan Klein. Learning to com- pose neural networks for question answering. In International Conference on Learning Representations (ICLR). Transactions of the Association of Computational Linguistics (TACL), 7–328. In ACM SIGIR conference on Research and development in information retrieval, pages 181–190. In Empirical Methods in Natural Language Processing (EMNLP), pages 2122–2132. Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. Neural machine transla- tion by jointly learning to align and translate. Embracing data abundance: Book Test dataset for reading comprehension. Petr Baudisˇ and Jan Sˇedivy, Jonathan Schwarz, Phil Blunsom, Chris Dyer, Karl Moritz Hermann, Ga ́abor Melis, and Edward Grefenstette. The Narrative QA reading comprehen- sion challenge. MURAX: A robust linguistic approach for question answering using an on-line encyclopedia. Chia-Wei Liu, Ryan Lowe, Iulian Serban, Mike Noseworthy, Laurent Charlin, and Joelle Pineau. How NOT to evaluate your dialogue system: An empirical study of unsu- pervised evaluation metrics for dialogue response generation.

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