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Evidence retrieval is a critical stage of question answering (QA), necessary not only to improve performance, but also to explain the decisions of the corresponding QA method.
Superglue: A stickier benchmark for general-purpose language understanding systems
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Explaining explanations: An overview of interpretability of machine learning
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Textgraphs 2019 shared task on multi-hop inference for explanation regeneration
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What’s missing: A knowledge gap guided approach for multi-hop question answering
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Leilani H Gilpin, David Bau, Ben Z Yuan, Ayesha Bajwa, Michael Specter, and Lalana Kagal. 2018 · 2018
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Cross attention for selection-based question answering
Alessio Gravina, Federico Rossetto, Silvia Severini, and Giuseppe Attardi. 2018 · 2018
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Looking beyond the surface: A challenge set for reading comprehension over multiple sentences
Daniel Khashabi, Snigdha Chaturvedi, Michael Roth, Shyam Upadhyay, and Dan Roth. 2018a · 2018
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Denoising distantly supervised open-domain question answering
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Efficient and robust question answering from minimal context over documents
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Tushar Khot, Ashish Sabharwal, and Peter Clark. 2019b · 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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Using natural language relations between answer choices for machine comprehension
Rajkumar Pujari and Dan Goldwasser. 2019 · 2019
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Answering complex open-domain questions through iterative query generation
Peng Qi, Xiaowen Lin, Leo Mehr, Zijian Wang, and Christopher D Manning. 2019 · 2019
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Pullnet: Open domain question answering with iterative retrieval on knowledge bases and text
Haitian Sun, Tania Bedrax-Weiss, and William Cohen. 2019a · 2019
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Improving machine reading comprehension with general reading strategies
Kai Sun, Dian Yu, Dong Yu, and Claire Cardie. 2019c · 2019
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Repurposing entailment for multi-hop question answering tasks
Harsh Trivedi, Heeyoung Kwon, Tushar Khot, Ashish Sabharwal, and Niranjan Balasubramanian. 2019 · 2019
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Alignment over heterogeneous embeddings for question answering
Vikas Yadav, Steven Bethard, and Mihai Surdeanu. 2019a · 2019
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Quick and (not so) dirty: Unsupervised selection of justification sentences for multi-hop question answering
Vikas Yadav, Steven Bethard, and Mihai Surdeanu. 2019b · 2019
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Xlnet: Generalized autoregressive pretraining for language understanding
Zhilin Yang, Zihang Dai, Yiming Yang, Jaime Carbonell, Russ R Salakhutdinov, and Quoc V Le. 2019 · 2019
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