• Gated Self-Matching Networks for Reading Comprehension and Question Answering
• Generating Natural Answers by Incorporating Copying and Retrieving Mechanisms in Sequence-to-Sequence Learning
• Coarse-to-Fine Question Answering for Long Documents
• An End-to-End Model for Question Answering over Knowledge Base with Cross-Attention Combining Global Knowledge
• Attention-over-Attention Neural Networks for Reading Comprehension
• Evaluation Metrics for Machine Reading Comprehension: Prerequisite Skills and Readability
• Semi-Supervised QA with Generative Domain-Adaptive Nets
• Learning to Ask: Neural Question Generation for Reading Comprehension
• A Constituent-Centric Neural Architecture for Reading Comprehension
• Leveraging Knowledge Bases in LSTMs for Improving Machine Reading
• TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension
• Search-based Neural Structured Learning for Sequential Question Answering
• Gated-Attention Readers for Text Comprehension
• Reading Wikipedia to Answer Open-Domain Questions

• 1 factoid question

• 2 narrative question(Opinion,instruction (how–to question))

• 3 multi-modal(Visual qa, Travel assistant)

• 4 AI ability tests(Reading comprehension,Elementary school science and math)

• 1 structured data(Databases & knowledge bases)

• 2 semi-structured data(Web tables)

• 3 unstructured text(Newswire corpora, web)

• web QA ：WebQA is a large scale Chinese human annotated real-world QA dataset which contains 42k questions and 579k evidences, where an evidence is a piece of text which may contain information for answering the question.All the questions are of single-entity factoid type, which means (1) each question is a factoid question and (2) its answer i nvolves only one entity (but may have multiple words).

datasets(freebase,microsoft satori,DBpedia)

• semantic parsing on freebase from question answer pair (emnlp 2013)
• semantic parsing via paraphrasing (acl 2014)
• large-scale semantic parsing without Question-answer pairs (tacl 2014)
• knowledge-based question answer as machine translation (acl 2014)
• semantic parsing via staged query graph generation:Question answer wit knowledge base (acl 2015)[paper][ppt]
• information extraction over structure data: question answer with freebase(acl 2014)
• question answer with subgraph embeddings(emnlp 2014)[paper][ppt]
• limitation learning of agenda-based sematic parsers (tacl 2015)
• transforming dependncy structures to logical form for semantic parsing(tacl 2016)
• question answer on freebase via relation extraction and textual evidence(acl 2016)

• Entity linking and retrieval for semantic search(wsdm 2014)
• knowledge base completion via search-based question answering(www 2014)
• learning question classifiers (coling 2012)
• question answer (Dan jurafsky stanford book,chapter 28)
• open domain question and answer via semantic enrichment(www 2015)[paper][ppt]
• table cell search for question answer [www 2016]

datasets(Facebook bAbi,Squad,MS MARCO,Baidu ild webqa, trivia )

• memery network(iclr 2015)[paper]
• reasoning in vector space(iclr 2016)
• R-NET: Machine Reading Comprehension with Self-matching Networks[paper] [code_tf][ppt]
• LEARNING RECURRENT SPAN REPRESENTATIONS FOR EXTRACTIVE QUESTION ANSWERING[paper][paper_v1] [code_1][code_2][ppt]
• ReasoNet: Learning to Stop Reading in Machine Comprehension[paper][code_cntk][ppt]
• Machine Comprehension Using Match-LSTM and Answer Pointer[paper]
• Making Neural QA as Simple as Possible but not Simpler [paper]
• Bidirectional Attention Flow for Machine Comprehension[paper][ppt][code_tf]
• MEMEN: Multi-layer Embedding with Memory Networks for Machine Comprehension[paper]
• Mnemonic Reader: Machine Comprehension with Iterative Aligning and Multi-hop Answer Pointing[paper]
• Structural Embedding of Syntactic Trees for Machine Comprehension