Update render script and Makefile

terminal/qa

@@ -1,7 +1,7 @@
-                                                   Awesome Question Answering !Awesome (https://awesome.re/badge.svg) (https://github.com/sindresorhus/awesome) 
+                                         Awesome Question Answering !Awesome (https://awesome.re/badge.svg) (https://github.com/sindresorhus/awesome) 
 
-_A curated list of the __Question Answering (QA) (https://en.wikipedia.org/wiki/Question_answering)__ subject which is a computer science discipline within the fields of information retrieval and natural 
-language processing (NLP) toward using machine learning and deep learning_
+_A curated list of the __Question Answering (QA) (https://en.wikipedia.org/wiki/Question_answering)__ subject which is a computer science discipline within the fields of information retrieval
+and natural language processing (NLP) toward using machine learning and deep learning_
 
 _정보 검색 및 자연 언어 처리 분야의 질의응답에 관한 큐레이션 - 머신러닝과 딥러닝 단계까지_
 _问答系统主题的精选列表，是信息检索和自然语言处理领域的计算机科学学科 - 使用机器学习和深度学习_
@@ -69,7 +69,8 @@
   - Matching Article Pairs with Graphical Decomposition and Convolutions (https://arxiv.org/pdf/1802.07459v2.pdf), Bang Liu, et al., ACL 2019, May 2019.
   - Episodic Memory Reader: Learning what to Remember for Question Answering from Streaming Data (https://arxiv.org/abs/1903.06164), Moonsu Han, et al., ACL 2019, Mar 2019.
   - Natural Questions: a Benchmark for Question Answering Research (https://ai.google/research/pubs/pub47761), Tom Kwiatkowski, et al., TACL 2019, Jan 2019.
-  - Textbook Question Answering with Multi-modal Context Graph Understanding and Self-supervised Open-set Comprehension (https://arxiv.org/abs/1811.00232), Daesik Kim, et al., ACL 2019, Nov 2018.
+  - Textbook Question Answering with Multi-modal Context Graph Understanding and Self-supervised Open-set Comprehension (https://arxiv.org/abs/1811.00232), Daesik Kim, et al., ACL 2019, Nov 
+2018.
 EMNLP-IJCNLP 2019
   - Language Models as Knowledge Bases? (https://arxiv.org/pdf/1909.01066v2.pdf), Fabio Petron, et al., EMNLP-IJCNLP 2019, Sep 2019.
   - LXMERT: Learning Cross-Modality Encoder Representations from Transformers (https://arxiv.org/pdf/1908.07490v3.pdf), Hao Tan, et al., EMNLP-IJCNLP 2019, Dec 2019.
@@ -132,21 +133,22 @@
 
 Competitions in QA
 
-│   │                                  Dataset                                   │Language│     Organizer     │Since│         Top Rank          │               Model               │Status│Over Human Performance│
-├───┼────────────────────────────────────────────────────────────────────────────┼────────┼───────────────────┼─────┼───────────────────────────┼───────────────────────────────────┼──────┼──────────────────────┤
-│0  │Story Cloze Test                                                            │English │Univ. of Rochester │2016 │msap                       │Logistic regression                │Closed│x                     │
-│   │ (http://cs.rochester.edu/~nasrinm/files/Papers/lsdsem17-shared-task.pdf)   │        │                   │     │                           │                                   │      │                      │
-│1  │MS MARCO                                                                    │English │Microsoft          │2016 │YUANFUDAO research NLP     │MARS                               │Closed│o                     │
-│2  │MS MARCO V2                                                                 │English │Microsoft          │2018 │NTT Media Intelli. Lab.    │Masque Q&A Style                   │Opened│x                     │
-│3  │SQuAD (https://arxiv.org/abs/1606.05250)                                    │English │Univ. of Stanford  │2018 │XLNet (single model)       │XLNet Team                         │Closed│o                     │
-│4  │SQuAD 2.0 (https://rajpurkar.github.io/SQuAD-explorer/)                     │English │Univ. of Stanford  │2018 │PINGAN Omni-Sinitic        │ALBERT + DAAF + Verifier (ensemble)│Opened│o                     │
-│5  │TriviaQA (http://nlp.cs.washington.edu/triviaqa/)                           │English │Univ. of Washington│2017 │Ming Yan                   │-                                  │Closed│-                     │
-│6  │decaNLP (https://decanlp.com/)                                              │English │Salesforce Research│2018 │Salesforce Research        │MQAN                               │Closed│x                     │
-│7  │DuReader Ver1. (https://ai.baidu.com/broad/introduction)                    │Chinese │Baidu              │2015 │Tryer                      │T-Reader (single)                  │Closed│x                     │
-│8  │DuReader Ver2. (https://ai.baidu.com/broad/introduction)                    │Chinese │Baidu              │2017 │renaissance                │AliReader                          │Opened│-                     │
-│9  │KorQuAD (https://korquad.github.io/KorQuad%201.0/)                          │Korean  │LG CNS AI Research │2018 │Clova AI LaRva Team        │LaRva-Kor-Large+ + CLaF (single)   │Closed│o                     │
-│10 │KorQuAD 2.0 (https://korquad.github.io/)                                    │Korean  │LG CNS AI Research │2019 │Kangwon National University│KNU-baseline(single model)         │Opened│x                     │
-│11 │CoQA (https://stanfordnlp.github.io/coqa/)                                  │English │Univ. of Stanford  │2018 │Zhuiyi Technology          │RoBERTa + AT + KD (ensemble)       │Opened│o                     │
+│   │                        Dataset                         │Language│     Organizer     │Since│         Top Rank          │               Model               │Status│Over Human Performance│
+├───┼────────────────────────────────────────────────────────┼────────┼───────────────────┼─────┼───────────────────────────┼───────────────────────────────────┼──────┼──────────────────────┤
+│0  │Story Cloze Test                                        │English │Univ. of Rochester │2016 │msap                       │Logistic regression                │Closed│x                     │
+│   │(http://cs.rochester.edu/~nasrinm/files/Papers/lsdsem17-│        │                   │     │                           │                                   │      │                      │
+│   │shared-task.pdf)                                        │        │                   │     │                           │                                   │      │                      │
+│1  │MS MARCO                                                │English │Microsoft          │2016 │YUANFUDAO research NLP     │MARS                               │Closed│o                     │
+│2  │MS MARCO V2                                             │English │Microsoft          │2018 │NTT Media Intelli. Lab.    │Masque Q&A Style                   │Opened│x                     │
+│3  │SQuAD (https://arxiv.org/abs/1606.05250)                │English │Univ. of Stanford  │2018 │XLNet (single model)       │XLNet Team                         │Closed│o                     │
+│4  │SQuAD 2.0 (https://rajpurkar.github.io/SQuAD-explorer/) │English │Univ. of Stanford  │2018 │PINGAN Omni-Sinitic        │ALBERT + DAAF + Verifier (ensemble)│Opened│o                     │
+│5  │TriviaQA (http://nlp.cs.washington.edu/triviaqa/)       │English │Univ. of Washington│2017 │Ming Yan                   │-                                  │Closed│-                     │
+│6  │decaNLP (https://decanlp.com/)                          │English │Salesforce Research│2018 │Salesforce Research        │MQAN                               │Closed│x                     │
+│7  │DuReader Ver1. (https://ai.baidu.com/broad/introduction)│Chinese │Baidu              │2015 │Tryer                      │T-Reader (single)                  │Closed│x                     │
+│8  │DuReader Ver2. (https://ai.baidu.com/broad/introduction)│Chinese │Baidu              │2017 │renaissance                │AliReader                          │Opened│-                     │
+│9  │KorQuAD (https://korquad.github.io/KorQuad%201.0/)      │Korean  │LG CNS AI Research │2018 │Clova AI LaRva Team        │LaRva-Kor-Large+ + CLaF (single)   │Closed│o                     │
+│10 │KorQuAD 2.0 (https://korquad.github.io/)                │Korean  │LG CNS AI Research │2019 │Kangwon National University│KNU-baseline(single model)         │Opened│x                     │
+│11 │CoQA (https://stanfordnlp.github.io/coqa/)              │English │Univ. of Stanford  │2018 │Zhuiyi Technology          │RoBERTa + AT + KD (ensemble)       │Opened│o                     │
 
 Publications
 - Papers
@@ -154,27 +156,29 @@
 : Show only what you want in Text
   - "Deep Joint Entity Disambiguation with Local Neural Attention" (https://arxiv.org/pdf/1704.04920.pdf), Octavian-Eugen Ganea and Thomas Hofmann, 2017.
   - "BI-DIRECTIONAL ATTENTION FLOW FOR MACHINE COMPREHENSION" (https://arxiv.org/pdf/1611.01603.pdf), Minjoon Seo, Aniruddha Kembhavi, Ali Farhadi, Hananneh Hajishirzi, ICLR, 2017.
-  - "Capturing Semantic Similarity for Entity Linking with Convolutional Neural Networks" (http://nlp.cs.berkeley.edu/pubs/FrancisLandau-Durrett-Klein_2016_EntityConvnets_paper.pdf), Matthew Francis-Landau, Greg
-Durrett and Dan Klei, NAACL-HLT 2016.
+  - "Capturing Semantic Similarity for Entity Linking with Convolutional Neural Networks" (http://nlp.cs.berkeley.edu/pubs/FrancisLandau-Durrett-Klein_2016_EntityConvnets_paper.pdf), Matthew 
+Francis-Landau, Greg Durrett and Dan Klei, NAACL-HLT 2016.
 - https://GitHub.com/matthewfl/nlp-entity-convnet
-  - "Entity Linking with a Knowledge Base: Issues, Techniques, and Solutions" (https://ieeexplore.ieee.org/document/6823700/), Wei Shen, Jianyong Wang, Jiawei Han, IEEE Transactions on Knowledge and Data 
-Engineering(TKDE), 2014.
+  - "Entity Linking with a Knowledge Base: Issues, Techniques, and Solutions" (https://ieeexplore.ieee.org/document/6823700/), Wei Shen, Jianyong Wang, Jiawei Han, IEEE Transactions on 
+Knowledge and Data Engineering(TKDE), 2014.
   - "Introduction to “This is Watson" (https://ieeexplore.ieee.org/document/6177724/), IBM Journal of Research and Development, D. A. Ferrucci, 2012.
   - "A survey on question answering technology from an information retrieval perspective" (https://www.sciencedirect.com/science/article/pii/S0020025511003860), Information Sciences, 2011.
-  - "Question Answering in Restricted Domains: An Overview" (https://www.mitpressjournals.org/doi/abs/10.1162/coli.2007.33.1.41), Diego Mollá and José Luis Vicedo, Computational Linguistics, 2007
+  - "Question Answering in Restricted Domains: An Overview" (https://www.mitpressjournals.org/doi/abs/10.1162/coli.2007.33.1.41), Diego Mollá and José Luis Vicedo, Computational Linguistics, 
+2007
   - "Natural language question answering: the view from here" (), L Hirschman, R Gaizauskas, natural language engineering, 2001.
   - Entity Disambiguation / Entity Linking
 
 Codes
-- BiDAF (https://github.com/allenai/bi-att-flow) - Bi-Directional Attention Flow (BIDAF) network is a multi-stage hierarchical process that represents the context at different levels of granularity and uses 
-bi-directional attention flow mechanism to obtain a query-aware context representation without early summarization. 
+- BiDAF (https://github.com/allenai/bi-att-flow) - Bi-Directional Attention Flow (BIDAF) network is a multi-stage hierarchical process that represents the context at different levels of 
+granularity and uses bi-directional attention flow mechanism to obtain a query-aware context representation without early summarization. 
   - Official; Tensorflow v1.2
   - Paper (https://arxiv.org/pdf/1611.01603.pdf)
-- QANet (https://github.com/NLPLearn/QANet) - A Q&A architecture does not require recurrent networks: Its encoder consists exclusively of convolution and self-attention, where convolution models local 
-interactions and self-attention models global interactions.
+- QANet (https://github.com/NLPLearn/QANet) - A Q&A architecture does not require recurrent networks: Its encoder consists exclusively of convolution and self-attention, where convolution 
+models local interactions and self-attention models global interactions.
   - Google; Unofficial; Tensorflow v1.5
   - Paper (#qanet)
-- R-Net (https://github.com/HKUST-KnowComp/R-Net) - An end-to-end neural networks model for reading comprehension style question answering, which aims to answer questions from a given passage.
+- R-Net (https://github.com/HKUST-KnowComp/R-Net) - An end-to-end neural networks model for reading comprehension style question answering, which aims to answer questions from a given 
+passage.
   - MS; Unofficially by HKUST; Tensorflow v1.5
   - Paper (https://www.microsoft.com/en-us/research/wp-content/uploads/2017/05/r-net.pdf)
 - R-Net-in-Keras (https://github.com/YerevaNN/R-NET-in-Keras) - R-NET re-implementation in Keras.
@@ -183,8 +187,8 @@
 - DrQA (https://github.com/hitvoice/DrQA) - DrQA is a system for reading comprehension applied to open-domain question answering.
   - Facebook; Official; Pytorch v0.4
   - Paper (#drqa)
-- BERT (https://github.com/google-research/bert) - A new language representation model which stands for Bidirectional Encoder Representations from Transformers. Unlike recent language representation models, BERT
-is designed to pre-train deep bidirectional representations by jointly conditioning on both left and right context in all layers. 
+- BERT (https://github.com/google-research/bert) - A new language representation model which stands for Bidirectional Encoder Representations from Transformers. Unlike recent language 
+representation models, BERT is designed to pre-train deep bidirectional representations by jointly conditioning on both left and right context in all layers. 
   - Google; Official implementation; Tensorflow v1.11.0
   - Paper (https://arxiv.org/abs/1810.04805)
 
@@ -193,7 +197,8 @@
 
 Slides
 - Question Answering with Knowledge Bases, Web and Beyond (https://github.com/scottyih/Slides/blob/master/QA%20Tutorial.pdf) - By Scott Wen-tau Yih & Hao Ma | Microsoft Research | 2016.
-- Question Answering (https://hpi.de/fileadmin/user_upload/fachgebiete/plattner/teaching/NaturalLanguageProcessing/NLP2017/NLP8_QuestionAnswering.pdf) - By Dr. Mariana Neves | Hasso Plattner Institut | 2017.
+- Question Answering (https://hpi.de/fileadmin/user_upload/fachgebiete/plattner/teaching/NaturalLanguageProcessing/NLP2017/NLP8_QuestionAnswering.pdf) - By Dr. Mariana Neves | Hasso Plattner 
+Institut | 2017.
 
 Dataset Collections
 - NLIWOD's Question answering datasets (https://github.com/dice-group/NLIWOD/tree/master/qa.datasets)
@@ -201,16 +206,16 @@
 
 Datasets
 - AI2 Science Questions v2.1(2017) (http://data.allenai.org/ai2-science-questions/)
-  - It consists of questions used in student assessments in the United States across elementary and middle school grade levels. Each question is 4-way multiple choice format and may or may not include a diagram 
-element.
+  - It consists of questions used in student assessments in the United States across elementary and middle school grade levels. Each question is 4-way multiple choice format and may or may 
+not include a diagram element.
   - Paper: http://ai2-website.s3.amazonaws.com/publications/AI2ReasoningChallenge2018.pdf
 - Children's Book Test (https://uclmr.github.io/ai4exams/data.html)
-- It is one of the bAbI project of Facebook AI Research which is organized towards the goal of automatic text understanding and reasoning. The CBT is designed to measure directly how well language models can 
-exploit wider linguistic context.
+- It is one of the bAbI project of Facebook AI Research which is organized towards the goal of automatic text understanding and reasoning. The CBT is designed to measure directly how well 
+language models can exploit wider linguistic context.
 - CODAH Dataset (https://github.com/Websail-NU/CODAH)
 - DeepMind Q&A Dataset; CNN/Daily Mail (https://github.com/deepmind/rc-data)
-  - Hermann et al. (2015) created two awesome datasets using news articles for Q&A research. Each dataset contains many documents (90k and 197k each), and each document companies on average 4 questions 
-approximately. Each question is a sentence with one missing word/phrase which can be found from the accompanying document/context.
+  - Hermann et al. (2015) created two awesome datasets using news articles for Q&A research. Each dataset contains many documents (90k and 197k each), and each document companies on average 4
+questions approximately. Each question is a sentence with one missing word/phrase which can be found from the accompanying document/context.
   - Paper: https://arxiv.org/abs/1506.03340
 - ELI5 (https://github.com/facebookresearch/ELI5)
   - Paper: https://arxiv.org/abs/1907.09190
@@ -231,23 +236,23 @@
   - A machine comprehension dataset
   - Paper: https://arxiv.org/pdf/1611.09830.pdf
 - Qestion-Answer Dataset by CMU (http://www.cs.cmu.edu/~ark/QA-data/)
-  - This is a corpus of Wikipedia articles, manually-generated factoid questions from them, and manually-generated answers to these questions, for use in academic research. These data were collected by Noah 
-Smith, Michael Heilman, Rebecca Hwa, Shay Cohen, Kevin Gimpel, and many students at Carnegie Mellon University and the University of Pittsburgh between 2008 and 2010.
+  - This is a corpus of Wikipedia articles, manually-generated factoid questions from them, and manually-generated answers to these questions, for use in academic research. These data were 
+collected by Noah Smith, Michael Heilman, Rebecca Hwa, Shay Cohen, Kevin Gimpel, and many students at Carnegie Mellon University and the University of Pittsburgh between 2008 and 2010.
 - SQuAD1.0 (https://rajpurkar.github.io/SQuAD-explorer/)
-  - Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment 
-of text, or span, from the corresponding reading passage, or the question might be unanswerable.
+  - Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every 
+question is a segment of text, or span, from the corresponding reading passage, or the question might be unanswerable.
   - Paper: https://arxiv.org/abs/1606.05250
 - SQuAD2.0 (https://rajpurkar.github.io/SQuAD-explorer/)
-  - SQuAD2.0 combines the 100,000 questions in SQuAD1.1 with over 50,000 new, unanswerable questions written adversarially by crowdworkers to look similar to answerable ones. To do well on SQuAD2.0, systems must
-not only answer questions when possible, but also determine when no answer is supported by the paragraph and abstain from answering.
+  - SQuAD2.0 combines the 100,000 questions in SQuAD1.1 with over 50,000 new, unanswerable questions written adversarially by crowdworkers to look similar to answerable ones. To do well on 
+SQuAD2.0, systems must not only answer questions when possible, but also determine when no answer is supported by the paragraph and abstain from answering.
   - Paper: https://arxiv.org/abs/1806.03822
 - Story cloze test (http://cs.rochester.edu/nlp/rocstories/)
-  - 'Story Cloze Test' is a new commonsense reasoning framework for evaluating story understanding, story generation, and script learning. This test requires a system to choose the correct ending to a 
-four-sentence story.
+  - 'Story Cloze Test' is a new commonsense reasoning framework for evaluating story understanding, story generation, and script learning. This test requires a system to choose the correct 
+ending to a four-sentence story.
   - Paper: https://arxiv.org/abs/1604.01696
 - TriviaQA (http://nlp.cs.washington.edu/triviaqa/)
-  - TriviaQA is a reading comprehension dataset containing over 650K question-answer-evidence triples. TriviaQA includes 95K question-answer pairs authored by trivia enthusiasts and independently gathered 
-evidence documents, six per question on average, that provide high quality distant supervision for answering the questions. 
+  - TriviaQA is a reading comprehension dataset containing over 650K question-answer-evidence triples. TriviaQA includes 95K question-answer pairs authored by trivia enthusiasts and 
+independently gathered evidence documents, six per question on average, that provide high quality distant supervision for answering the questions. 
   - Paper: https://arxiv.org/abs/1705.03551
 - WikiQA (https://www.microsoft.com/en-us/download/details.aspx?id=52419&from=https%3A%2F%2Fresearch.microsoft.com%2Fen-US%2Fdownloads%2F4495da01-db8c-4041-a7f6-7984a4f6a905%2Fdefault.aspx)
   - A publicly available set of question and sentence pairs for open-domain question answering.
@@ -261,15 +266,16 @@
 - 2014
   - "Problem-oriented patient record summary: An early report on a Watson application", M. Devarakonda, Dongyang Zhang, Ching-Huei Tsou, M. Bornea, Healthcom, 2014.
   - "WatsonPaths: Scenario-based Question Answering and Inference over Unstructured Information" 
-(http://domino.watson.ibm.com/library/Cyberdig.nsf/1e4115aea78b6e7c85256b360066f0d4/088f74984a07645485257d5f006ace96!OpenDocument&Highlight=0,RC25489), Adam Lally, Sugato Bachi, Michael A. Barborak, David W. 
-Buchanan, Jennifer Chu-Carroll, David A. Ferrucci, Michael R. Glass, Aditya Kalyanpur, Erik T. Mueller, J. William Murdock, Siddharth Patwardhan, John M. Prager, Christopher A. Welty, IBM Research Report 
-RC25489, 2014.
+(http://domino.watson.ibm.com/library/Cyberdig.nsf/1e4115aea78b6e7c85256b360066f0d4/088f74984a07645485257d5f006ace96!OpenDocument&Highlight=0,RC25489), Adam Lally, Sugato Bachi, Michael A. 
+Barborak, David W. Buchanan, Jennifer Chu-Carroll, David A. Ferrucci, Michael R. Glass, Aditya Kalyanpur, Erik T. Mueller, J. William Murdock, Siddharth Patwardhan, John M. Prager, 
+Christopher A. Welty, IBM Research Report RC25489, 2014.
   - "Medical Relation Extraction with Manifold Models" (http://acl2014.org/acl2014/P14-1/pdf/P14-1078.pdf), Chang Wang and James Fan, ACL, 2014.
 
 MS Research's publication within 5 years
 - 2018
   - "Characterizing and Supporting Question Answering in Human-to-Human Communication", Xiao Yang, Ahmed Hassan Awadallah, Madian Khabsa, Wei Wang, Miaosen Wang, ACM SIGIR, 2018.
-  - "FigureQA: An Annotated Figure Dataset for Visual Reasoning" (https://arxiv.org/abs/1710.07300), Samira Ebrahimi Kahou, Vincent Michalski, Adam Atkinson, Akos Kadar, Adam Trischler, Yoshua Bengio, ICLR, 2018
+  - "FigureQA: An Annotated Figure Dataset for Visual Reasoning" (https://arxiv.org/abs/1710.07300), Samira Ebrahimi Kahou, Vincent Michalski, Adam Atkinson, Akos Kadar, Adam Trischler, 
+Yoshua Bengio, ICLR, 2018
 - 2017
   - "Multi-level Attention Networks for Visual Question Answering", Dongfei Yu, Jianlong Fu, Tao Mei, Yong Rui, CVPR, 2017.
   - "A Joint Model for Question Answering and Question Generation", Tong Wang, Xingdi (Eric) Yuan, Adam Trischler, ICML, 2017.
@@ -277,52 +283,55 @@
   - "Question-Answering with Grammatically-Interpretable Representations", Hamid Palangi, Paul Smolensky, Xiaodong He, Li Deng, 
   - "Search-based Neural Structured Learning for Sequential Question Answering", Mohit Iyyer, Wen-tau Yih, Ming-Wei Chang, ACL, 2017.
 - 2016
-  - "Stacked Attention Networks for Image Question Answering" (https://www.cv-foundation.org/openaccess/content_cvpr_2016/html/Yang_Stacked_Attention_Networks_CVPR_2016_paper.html), Zichao Yang, Xiaodong He, 
-Jianfeng Gao, Li Deng, Alex Smola, CVPR, 2016.
-  - "Question Answering with Knowledge Base, Web and Beyond" (https://www.microsoft.com/en-us/research/publication/question-answering-with-knowledge-base-web-and-beyond/), Yih, Scott Wen-tau and Ma, Hao, ACM 
-SIGIR, 2016.
-  - "NewsQA: A Machine Comprehension Dataset" (https://arxiv.org/abs/1611.09830), Adam Trischler, Tong Wang, Xingdi Yuan, Justin Harris, Alessandro Sordoni, Philip Bachman, Kaheer Suleman, RepL4NLP, 2016.
+  - "Stacked Attention Networks for Image Question Answering" (https://www.cv-foundation.org/openaccess/content_cvpr_2016/html/Yang_Stacked_Attention_Networks_CVPR_2016_paper.html), Zichao 
+Yang, Xiaodong He, Jianfeng Gao, Li Deng, Alex Smola, CVPR, 2016.
+  - "Question Answering with Knowledge Base, Web and Beyond" (https://www.microsoft.com/en-us/research/publication/question-answering-with-knowledge-base-web-and-beyond/), Yih, Scott Wen-tau 
+and Ma, Hao, ACM SIGIR, 2016.
+  - "NewsQA: A Machine Comprehension Dataset" (https://arxiv.org/abs/1611.09830), Adam Trischler, Tong Wang, Xingdi Yuan, Justin Harris, Alessandro Sordoni, Philip Bachman, Kaheer Suleman, 
+RepL4NLP, 2016.
   - "Table Cell Search for Question Answering" (https://dl.acm.org/citation.cfm?id=2883080), Sun, Huan and Ma, Hao and He, Xiaodong and Yih, Wen-tau and Su, Yu and Yan, Xifeng, WWW, 2016.
 - 2015
-  - "WIKIQA: A Challenge Dataset for Open-Domain Question Answering" (https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/YangYihMeek_EMNLP-15_WikiQA.pdf), Yi Yang, Wen-tau Yih, and Christopher 
-Meek, EMNLP, 2015.
-  - "Web-based Question Answering: Revisiting AskMSR" (https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/AskMSRPlusTR_082815.pdf), Chen-Tse Tsai, Wen-tau Yih, and Christopher J.C. Burges, 
-MSR-TR, 2015.
-  - "Open Domain Question Answering via Semantic Enrichment" (https://dl.acm.org/citation.cfm?id=2741651), Huan Sun, Hao Ma, Wen-tau Yih, Chen-Tse Tsai, Jingjing Liu, and Ming-Wei Chang, WWW, 2015.
+  - "WIKIQA: A Challenge Dataset for Open-Domain Question Answering" (https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/YangYihMeek_EMNLP-15_WikiQA.pdf), Yi Yang, Wen-tau 
+Yih, and Christopher Meek, EMNLP, 2015.
+  - "Web-based Question Answering: Revisiting AskMSR" (https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/AskMSRPlusTR_082815.pdf), Chen-Tse Tsai, Wen-tau Yih, and 
+Christopher J.C. Burges, MSR-TR, 2015.
+  - "Open Domain Question Answering via Semantic Enrichment" (https://dl.acm.org/citation.cfm?id=2741651), Huan Sun, Hao Ma, Wen-tau Yih, Chen-Tse Tsai, Jingjing Liu, and Ming-Wei Chang, WWW,
+2015.
 - 2014
-  - "An Overview of Microsoft Deep QA System on Stanford WebQuestions Benchmark" (https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/Microsoft20Deep20QA.pdf), Zhenghao Wang, Shengquan Yan, 
-Huaming Wang, and Xuedong Huang, MSR-TR, 2014.
+  - "An Overview of Microsoft Deep QA System on Stanford WebQuestions Benchmark" (https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/Microsoft20Deep20QA.pdf), Zhenghao Wang, 
+Shengquan Yan, Huaming Wang, and Xuedong Huang, MSR-TR, 2014.
   - "Semantic Parsing for Single-Relation Question Answering" (), Wen-tau Yih, Xiaodong He, Christopher Meek, ACL, 2014.
   
 Google AI's publication within 5 years
 - 2018
   - Google QA 
-- **"QANet: Combining Local Convolution with Global Self-Attention for Reading Comprehension"** (https://openreview.net/pdf?id=B14TlG-RW), Adams Wei Yu, David Dohan, Minh-Thang Luong, Rui Zhao, Kai Chen, Mohamma                                                                         
-d Norouzi, Quoc V. Le, ICLR, 2018.                                                                                                                                                                                                                                                          
-- **"Ask the Right Questions: Active Question Reformulation with Reinforcement Learning"** (https://openreview.net/pdf?id=S1CChZ-CZ), Christian Buck and Jannis Bulian and Massimiliano Ciaramita and Wojciech Pawe                                                                         
-ł Gajewski and Andrea Gesmundo and Neil Houlsby and Wei Wang, ICLR, 2018.                                                                                                                                                                                                                   
+- **"QANet: Combining Local Convolution with Global Self-Attention for Reading Comprehension"** (https://openreview.net/pdf?id=B14TlG-RW), Adams Wei Yu, David Dohan, Minh-Thang Luong, Rui Zha                                                                                             
+o, Kai Chen, Mohammad Norouzi, Quoc V. Le, ICLR, 2018.                                                                                                                                                                                                                                      
+- **"Ask the Right Questions: Active Question Reformulation with Reinforcement Learning"** (https://openreview.net/pdf?id=S1CChZ-CZ), Christian Buck and Jannis Bulian and Massimiliano Ciarami                                                                                             
+ta and Wojciech Paweł Gajewski and Andrea Gesmundo and Neil Houlsby and Wei Wang, ICLR, 2018.                                                                                                                                                                                               
 - **"Building Large Machine Reading-Comprehension Datasets using Paragraph Vectors"** (https://arxiv.org/pdf/1612.04342.pdf), Radu Soricut, Nan Ding, 2018.                                                                                                                                 
   - Sentence representation
 - **"An efficient framework for learning sentence representations"** (https://arxiv.org/pdf/1803.02893.pdf), Lajanugen Logeswaran, Honglak Lee, ICLR, 2018.
   - "Did the model understand the question?" (https://arxiv.org/pdf/1805.05492.pdf), Pramod K. Mudrakarta and Ankur Taly and Mukund Sundararajan and Kedar Dhamdhere, ACL, 2018.
 - 2017
-  - "Analyzing Language Learned by an Active Question Answering Agent" (https://arxiv.org/pdf/1801.07537.pdf), Christian Buck and Jannis Bulian and Massimiliano Ciaramita and Wojciech Gajewski and Andrea 
-Gesmundo and Neil Houlsby and Wei Wang, NIPS, 2017.
-  - "Learning Recurrent Span Representations for Extractive Question Answering" (https://arxiv.org/pdf/1611.01436.pdf), Kenton Lee and Shimi Salant and Tom Kwiatkowski and Ankur Parikh and Dipanjan Das and 
-Jonathan Berant, ICLR, 2017.
+  - "Analyzing Language Learned by an Active Question Answering Agent" (https://arxiv.org/pdf/1801.07537.pdf), Christian Buck and Jannis Bulian and Massimiliano Ciaramita and Wojciech 
+Gajewski and Andrea Gesmundo and Neil Houlsby and Wei Wang, NIPS, 2017.
+  - "Learning Recurrent Span Representations for Extractive Question Answering" (https://arxiv.org/pdf/1611.01436.pdf), Kenton Lee and Shimi Salant and Tom Kwiatkowski and Ankur Parikh and 
+Dipanjan Das and Jonathan Berant, ICLR, 2017.
   - Identify the same question
-- **"Neural Paraphrase Identification of Questions with Noisy Pretraining"** (https://arxiv.org/pdf/1704.04565.pdf), Gaurav Singh Tomar and Thyago Duque and Oscar Täckström and Jakob Uszkoreit and Dipanjan Das,             
-SCLeM, 2017.                                                                                                                                                                                                                   
+- **"Neural Paraphrase Identification of Questions with Noisy Pretraining"** (https://arxiv.org/pdf/1704.04565.pdf), Gaurav Singh Tomar and Thyago Duque and Oscar Täckström and Jakob Uszkorei                                
+t and Dipanjan Das, SCLeM, 2017.                                                                                                                                                                                               
 - 2014
   - "Great Question! Question Quality in Community Q&A", Sujith Ravi and Bo Pang and Vibhor Rastogi and Ravi Kumar, ICWSM, 2014.
 
 Facebook AI Research's publication within 5 years
 - 2018
-  - Embodied Question Answering (https://research.fb.com/publications/embodied-question-answering/), Abhishek Das, Samyak Datta, Georgia Gkioxari, Stefan Lee, Devi Parikh, and Dhruv Batra, CVPR, 2018
-  - Do explanations make VQA models more predictable to a human? (https://research.fb.com/publications/do-explanations-make-vqa-models-more-predictable-to-a-human/), Arjun Chandrasekaran, Viraj Prabhu, Deshraj 
-Yadav, Prithvijit Chattopadhyay, and Devi Parikh, EMNLP, 2018
-  - Neural Compositional Denotational Semantics for Question Answering (https://research.fb.com/publications/neural-compositional-denotational-semantics-for-question-answering/), Nitish Gupta, Mike Lewis, EMNLP,
-2018
+  - Embodied Question Answering (https://research.fb.com/publications/embodied-question-answering/), Abhishek Das, Samyak Datta, Georgia Gkioxari, Stefan Lee, Devi Parikh, and Dhruv Batra, 
+CVPR, 2018
+  - Do explanations make VQA models more predictable to a human? (https://research.fb.com/publications/do-explanations-make-vqa-models-more-predictable-to-a-human/), Arjun Chandrasekaran, 
+Viraj Prabhu, Deshraj Yadav, Prithvijit Chattopadhyay, and Devi Parikh, EMNLP, 2018
+  - Neural Compositional Denotational Semantics for Question Answering (https://research.fb.com/publications/neural-compositional-denotational-semantics-for-question-answering/), Nitish 
+Gupta, Mike Lewis, EMNLP, 2018
 - 2017
   - DrQA 
 - **Reading Wikipedia to Answer Open-Domain Questions** (https://cs.stanford.edu/people/danqi/papers/acl2017.pdf), Danqi Chen, Adam Fisch, Jason Weston & Antoine Bordes, ACL, 2017.