- 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
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1 factoid question
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2 narrative question(Opinion,instruction (how–to question))
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3 multi-modal(Visual qa, Travel assistant)
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4 AI ability tests(Reading comprehension,Elementary school science and math)
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1 structured data(Databases & knowledge bases)
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2 semi-structured data(Web tables)
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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