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We aim to improve question answering (QA) by decomposing hard questions into simpler sub-questions that existing QA systems are capable of answering.
RoBERTa: A robustly optimized bert pretraining approach
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Huggingface’s transformers: State-of-the-art natural language processing
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Building watson: An overview of the deepqa project
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Learning continuous word embedding with metadata for question retrieval in community question answering
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Aligning books and movies: Towards story-like visual explanations by watching movies and reading books
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Learning to compose neural networks for question answering
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Improving neural machine translation models with monolingual data
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Enriching word vectors with subword information
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Reading Wikipedia to answer open-domain questions
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Learning cooperative visual dialog agents with deep reinforcement learning
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spaCy 2: Natural language understanding with Bloom embeddings, convolutional neural networks and incremental parsing
Matthew Honnibal and Ines Montani. 2017 · 2017
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Reinforcement learning with unsupervised auxiliary tasks
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Bag of tricks for efficient text classification
Armand Joulin, Edouard Grave, Piotr Bojanowski, and Tomas Mikolov. 2017 · 2017
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Task-oriented query reformulation with reinforcement learning
Rodrigo Nogueira and Kyunghyun Cho. 2017 · 2017
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Mixed precision training
Paulius Micikevicius, Sharan Narang, Jonah Alben, Gregory Diamos, Erich Elsen, David Garcia, Boris Ginsburg, Michael Houston, Oleksii Kuchaiev, Ganesh Venkatesh, and Hao Wu. 2018 · 2018
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SimpleQuestions nearly solved: A new upperbound and baseline approach
Michael Petrochuk and Luke Zettlemoyer. 2018 · 2018
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The web as a knowledge-base for answering complex questions
Alon Talmor and Jonathan Berant. 2018 · 2018
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FEVER: a large-scale dataset for fact extraction and verification
James Thorne, Andreas Vlachos, Christos Christodoulopoulos, and Arpit Mittal. 2018 · 2018
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HotpotQA: A dataset for diverse, explainable multi-hop question answering
Zhilin Yang, Peng Qi, Saizheng Zhang, Yoshua Bengio, William Cohen, Ruslan Salakhutdinov, and Christopher D. Manning. 2018 · 2018
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End-to-end optimization of goal-driven and visually grounded dialogue systems
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Ł ukasz Kaiser, and Illia Polosukhin. 2017 · 2017
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Zipporah: a fast and scalable data cleaning system for noisy web-crawled parallel corpora
Hainan Xu and Philipp Koehn. 2017 · 2017
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Unsupervised neural machine translation
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Supervising strong learners by amplifying weak experts
Paul Francis Christiano, Buck Shlegeris, and Dario Amodei. 2018 · 2018
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Simple and effective multi-paragraph reading comprehension
Christopher Clark and Matt Gardner. 2018 · 2018
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Identifying well-formed natural language questions
Manaal Faruqui and Dipanjan Das. 2018 · 2018
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Margin-based parallel corpus mining with multilingual sentence embeddings
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BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
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Hierarchical graph network for multi-hop question answering
Yuwei Fang, Siqi Sun, Zhe Gan, Rohit Pillai, Shuohang Wang, and Jingjing Liu. 2019 · 2019
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Cross-lingual language model pretraining
Guillaume Lample and Alexis Conneau. 2019 · 2019
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Unsupervised question answering by cloze translation
Patrick Lewis, Ludovic Denoyer, and Sebastian Riedel. 2019 · 2019
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Revealing the importance of semantic retrieval for machine reading at scale
Yixin Nie, Songhe Wang, and Mohit Bansal. 2019 · 2019
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Language models are unsupervised multitask learners
Alec Radford, Jeff Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. 2019 · 2019
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The FEVER2.0 shared task
James Thorne, Andreas Vlachos, Oana Cocarascu, Christos Christodoulopoulos, and Arpit Mittal. 2019 · 2019
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Select, answer and explain: Interpretable multi-hop reading comprehension over multiple documents
Ming Tu, Kevin Huang, Guangtao Wang, Jing Huang, Xiaodong He, and Bowen Zhou. 2020 · 2020
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Break it down: A question understanding benchmark
Tomer Wolfson, Mor Geva, Ankit Gupta, Matt Gardner, Yoav Goldberg, Daniel Deutch, and Jonathan Berant. 2020 · 2020
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