Machine Reading w/ Neural Nets (3/30/2021)
- Machine Reading Datasets
- Methods for Encoding Context
- Methods for Multi-hop Reasoning
- Cavevats about Datasets
- Reference: MCTest (Richardson et al. 2013)
- Reference: RACE (Lai et al. 2017)
- Reference: SQuAD (Rajpurkar et al. 2016)
- Reference: TriviaQA (Joshi et al. 2017)
- Reference: A Thorough Examination of the CNN/Daily Mail Task (Chen et al. 2016)
- Reference: Adversarial Examples in SQuAD (Jia and Liang 2017)
- Reference: Adversarial Construction of Datasets (Zellers et al. 2018)
- Reference: Natural Questions (Kwiatkowski et al. 2019)
- Reference: Teaching Machines to Read and Comprehend (Hermann et al. 2015)
- Reference: Attention Sum (Kadlec et al. 2016)
- Reference: Attention over Attention (Cui et al. 2017)
- Reference: Bidirectional Attention Flow (Seo et al. 2017)
- Reference: Dynamic Coattention Networks (Xiong et al. 2017)
- Reference: Gated Attention Readers (Dhingra et al. 2017)
- Reference: Representation and Inference for Natural Language (Blackburn and Bos 1999)
- Reference: HotpotQA (Yang et al. 2018)
- Reference: Dataset Bias in Multi-hop Reasoning (Chen and Durrett 2019)
- Reference: Memory Networks (Weston et al. 2015)
- Reference: End-to-end Memory Networks (Sukhbaatar et al. 2015)
- Reference: Dynamic Memory Networks (Kumar et al. 2016)
- Reference: Learning to Stop Reading (Shen et al. 2017)
- Reference: Coarse-to-fine Question Answering (Choi et al. 2017)
- Reference: Reading Wikipedia to Answer Open-Domain Questions (Chen et al. 2017)
- Reference: BREAK Dataset (Wolfson et al. 2020)
- Reference: Question Decomposition (Min et al. 2019)
- Reference: Language Models as Knowledge Bases (Petroni et al. 2019)
- Reference: Retrieval-based Language Model Pre-training (Guu et al. 2020)
- Reference: Exploring Numeracy in Embeddings (Naik et al. 2019)
- Reference: Do NLP Models Know Numbers (Wallace et al. 2019)
- Reference: DROP Dataset (Dua et al. 2019)
- Reference: Neural Module Networsk for Reasoning Over Text (Gupta et al. 2020)
- Reference: End-to-end Differentiable Proving (Rocktäschel and Riedel 2017)
- Reference: Question Answering in Context (Choi et al. 2018)
- Reference: Contextual Question Answering (Reddy et al. 2018)
Slides: Machine Reading Slides
Video: Machine Reading Video