A Constituent-Centric Neural Architecture for Reading Comprehension
task:reading comprehension || data:SQuAD
A FOFE-based Local Detection Approach for Named Entity Recognition and Mention Detection
task:NER、mention detection
data:WebQuestions || focus：question representation（问题无论它的候选答案是什么，都会被转换成为一个固定长度的vector）
Attention-over-Attention Neural Networks for Reading Comprehension
task:阅读理解
Automatically Labeled Data Generation for Large Scale Event Extraction
focus:对event extraction提供标注数据的方法
Coarse-to-Fine Question Answering for Long Documents
advantage：高效地扩展到长文档(longer documents)的同时，能够维持甚至提升state-of-the-art模型的性能
Comparing Apples to Apples: Learning Semantics of Common Entities Through a Novel Comprehension Task
task:提出阅读理解新任务GuessTwo（给定一个短段落，与两个真实在语义上相似的(semantically-similar)两个entities分别比较，系统应该能猜出来这两个entities是什么）
method：使用encoder-decoder框架来预测生成式keypghrase
Exploiting Argument Information to Improve Event Detection via Supervised Attention Mechanisms
focus:event detection(引入了identifying和categorizing event) || method:利用argument信息来显式地进行event detection
Gated Self-Matching Networks for Reading Comprehension and Question Answering
method:gated self-matching networks || data:SQuAD
Gated-Attention Readers for Text Comprehension
data:document上回答cloze风格 || method:multi-hop架构和一个新的attention机制结合
data:kbqa
Going out on a limb : Joint Extraction of Entity Mentions and Relations without Dependency Trees
http://www.pilevar.com/taher/pubs/ACL2017b_Gritta_etal.pdf
focus:转喻(metonymic)与NER || data:SemEval2007的Metonymy Resolution
Search-based Neural Structured Learning for Sequential Question Answering
method:dynamic neural semantic parsing,使用弱监督的reward-guided search
Reading Wikipedia to Answer Open-Domain Questions
method:bigram hashing进行搜索和使用RNN进行TF-IDF matching ||data:Wikipedia
Neural Symbolic Machines: Learning Semantic Parsers on Freebase with Weak Supervision
method:neural programmer(比如说一个端到端的模型来将语言映射到程序)+symbolic computer(比如说一个能够执行程序的Lisp的解释器)+rl
method:hierarchical RNN +residual learning || data:single-relation(SimpleQuestions)和multi-relation(WebQSP)
Joint Extraction of Entities and Relations Based on a Novel Tagging Scheme
entities和relations的joint extraction--->tagging problem
Joint Extraction of Relations with Class Ties via Effective Deep Ranking
一个实体tuple可能有多个关系fact、三个新的ranking loss function
Leveraging Knowledge Bases in LSTMs for Improving Machine Reading data:ACE2005的entity extraction和event extraction