First-principle study on honeycomb fluorated-InTe monolayer with large Rashba spin splitting and direct bandgap

[1]  Wendy Grace Lehnert,et al.  The Process of Question Answering , 2022 .

[2]  Jungang Xu,et al.  A Survey on Neural Machine Reading Comprehension , 2019, ArXiv.

[3]  Rajarshi Das,et al.  Multi-step Retriever-Reader Interaction for Scalable Open-domain Question Answering , 2019, ICLR.

[4]  Hui Wang,et al.  R-Trans: RNN Transformer Network for Chinese Machine Reading Comprehension , 2019, IEEE Access.

[5]  Jun Xu,et al.  HAS-QA: Hierarchical Answer Spans Model for Open-domain Question Answering , 2019, AAAI.

[6]  Chenguang Zhu,et al.  SDNet: Contextualized Attention-based Deep Network for Conversational Question Answering , 2018, ArXiv.

[7]  Wentao Ma,et al.  Convolutional Spatial Attention Model for Reading Comprehension with Multiple-Choice Questions , 2018, AAAI.

[8]  Yang Liu,et al.  U-Net: Machine Reading Comprehension with Unanswerable Questions , 2018, ArXiv.

[9]  Jaewoo Kang,et al.  Ranking Paragraphs for Improving Answer Recall in Open-Domain Question Answering , 2018, EMNLP.

[10]  Mark Yatskar,et al.  A Qualitative Comparison of CoQA, SQuAD 2.0 and QuAC , 2018, NAACL.

[11]  Yoshua Bengio,et al.  HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering , 2018, EMNLP.

[12]  Xiaocheng Feng,et al.  Knowledge Based Machine Reading Comprehension , 2018, ArXiv.

[13]  Chao Wang,et al.  Explicit Utilization of General Knowledge in Machine Reading Comprehension , 2018, ACL.

[14]  Nan Yang,et al.  I Know There Is No Answer: Modeling Answer Validation for Machine Reading Comprehension , 2018, NLPCC.

[15]  Danqi Chen,et al.  CoQA: A Conversational Question Answering Challenge , 2018, TACL.

[16]  Eunsol Choi,et al.  QuAC: Question Answering in Context , 2018, EMNLP.

[17]  Furu Wei,et al.  Read + Verify: Machine Reading Comprehension with Unanswerable Questions , 2018, AAAI.

[18]  Zachary C. Lipton,et al.  How Much Reading Does Reading Comprehension Require? A Critical Investigation of Popular Benchmarks , 2018, EMNLP.

[19]  Utpal Garain,et al.  CNN for Text-Based Multiple Choice Question Answering , 2018, ACL.

[20]  Seunghak Yu,et al.  A Multi-Stage Memory Augmented Neural Network for Machine Reading Comprehension , 2018, QA@ACL.

[21]  Jinho D. Choi,et al.  Challenging Reading Comprehension on Daily Conversation: Passage Completion on Multiparty Dialog , 2018, NAACL.

[22]  Todor Mihaylov,et al.  Knowledgeable Reader: Enhancing Cloze-Style Reading Comprehension with External Commonsense Knowledge , 2018, ACL.

[23]  Richard Socher,et al.  Efficient and Robust Question Answering from Minimal Context over Documents , 2018, ACL.

[24]  Furu Wei,et al.  Hierarchical Attention Flow for Multiple-Choice Reading Comprehension , 2018, AAAI.

[25]  X. Miao,et al.  Design lateral heterostructure of monolayer ZrS 2 and HfS 2 from first principles calculations , 2018 .

[26]  Mitesh M. Khapra,et al.  DuoRC: Towards Complex Language Understanding with Paraphrased Reading Comprehension , 2018, ACL.

[27]  Samuel R. Bowman,et al.  Training a Ranking Function for Open-Domain Question Answering , 2018, NAACL.

[28]  Walter Daelemans,et al.  CliCR: a Dataset of Clinical Case Reports for Machine Reading Comprehension , 2018, NAACL.

[29]  Simon Ostermann,et al.  MCScript: A Novel Dataset for Assessing Machine Comprehension Using Script Knowledge , 2018, LREC.

[30]  Luke S. Zettlemoyer,et al.  Deep Contextualized Word Representations , 2018, NAACL.

[31]  Quoc V. Le,et al.  QANet: Combining Local Convolution with Global Self-Attention for Reading Comprehension , 2018, ICLR.

[32]  Chris Dyer,et al.  The NarrativeQA Reading Comprehension Challenge , 2017, TACL.

[33]  Xinyan Xiao,et al.  DuReader: a Chinese Machine Reading Comprehension Dataset from Real-world Applications , 2017, QA@ACL.

[34]  Guokun Lai,et al.  Large-scale Cloze Test Dataset Created by Teachers , 2017, EMNLP.

[35]  Richard Socher,et al.  DCN+: Mixed Objective and Deep Residual Coattention for Question Answering , 2017, ICLR.

[36]  Christopher Clark,et al.  Simple and Effective Multi-Paragraph Reading Comprehension , 2017, ACL.

[37]  Deng Cai,et al.  Smarnet: Teaching Machines to Read and Comprehend Like Human , 2017, ArXiv.

[38]  Jackie Chi Kit Cheung,et al.  World Knowledge for Reading Comprehension: Rare Entity Prediction with Hierarchical LSTMs Using External Descriptions , 2017, EMNLP.

[39]  Richard Socher,et al.  Learned in Translation: Contextualized Word Vectors , 2017, NIPS.

[40]  Deng Cai,et al.  MEMEN: Multi-layer Embedding with Memory Networks for Machine Comprehension , 2017, ArXiv.

[41]  Percy Liang,et al.  Adversarial Examples for Evaluating Reading Comprehension Systems , 2017, EMNLP.

[42]  William W. Cohen,et al.  Quasar: Datasets for Question Answering by Search and Reading , 2017, ArXiv.

[43]  Ming Zhou,et al.  Gated Self-Matching Networks for Reading Comprehension and Question Answering , 2017, ACL.

[44]  Tom M. Mitchell,et al.  Leveraging Knowledge Bases in LSTMs for Improving Machine Reading , 2017, ACL.

[45]  Omer Levy,et al.  Zero-Shot Relation Extraction via Reading Comprehension , 2017, CoNLL.

[46]  Lukasz Kaiser,et al.  Attention is All you Need , 2017, NIPS.

[47]  Ming Zhou,et al.  S-Net: From Answer Extraction to Answer Generation for Machine Reading Comprehension , 2017, AAAI 2017.

[48]  Ming Zhou,et al.  Reinforced Mnemonic Reader for Machine Reading Comprehension , 2017, IJCAI.

[49]  Eunsol Choi,et al.  TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension , 2017, ACL.

[50]  Kyunghyun Cho,et al.  SearchQA: A New Q&A Dataset Augmented with Context from a Search Engine , 2017, ArXiv.

[51]  Guokun Lai,et al.  RACE: Large-scale ReAding Comprehension Dataset From Examinations , 2017, EMNLP.

[52]  Jason Weston,et al.  Reading Wikipedia to Answer Open-Domain Questions , 2017, ACL.

[53]  Li-Rong Dai,et al.  Exploring Question Understanding and Adaptation in Neural-Network-Based Question Answering , 2017, ArXiv.

[54]  Ruslan Salakhutdinov,et al.  A Comparative Study of Word Embeddings for Reading Comprehension , 2017, ArXiv.

[55]  Aldo H. Romero,et al.  Giant tunable Rashba spin splitting in a two-dimensional BiSb monolayer and in BiSb/AlN heterostructures , 2017, 1701.06213.

[56]  X. Cui,et al.  Optical Control of Spin Polarization in Monolayer Transition Metal Dichalcogenides. , 2017, ACS nano.

[57]  Zhiguo Wang,et al.  Multi-Perspective Context Matching for Machine Comprehension , 2016, ArXiv.

[58]  Philip Bachman,et al.  NewsQA: A Machine Comprehension Dataset , 2016, Rep4NLP@ACL.

[59]  Richard Socher,et al.  Dynamic Coattention Networks For Question Answering , 2016, ICLR.

[60]  Ye Yuan,et al.  Words or Characters? Fine-grained Gating for Reading Comprehension , 2016, ICLR.

[61]  Ali Farhadi,et al.  Bidirectional Attention Flow for Machine Comprehension , 2016, ICLR.

[62]  Jianfeng Gao,et al.  MS MARCO: A Human Generated MAchine Reading COmprehension Dataset , 2016, CoCo@NIPS.

[63]  Li Zhao,et al.  Attention-based LSTM for Aspect-level Sentiment Classification , 2016, EMNLP.

[64]  Bowen Zhou,et al.  End-to-End Answer Chunk Extraction and Ranking for Reading Comprehension , 2016, 1610.09996.

[65]  Rudolf Kadlec,et al.  Embracing data abundance: BookTest Dataset for Reading Comprehension , 2016, ICLR.

[66]  Yelong Shen,et al.  ReasoNet: Learning to Stop Reading in Machine Comprehension , 2016, CoCo@NIPS.

[67]  Shuohang Wang,et al.  Machine Comprehension Using Match-LSTM and Answer Pointer , 2016, ICLR.

[68]  David A. McAllester,et al.  Who did What: A Large-Scale Person-Centered Cloze Dataset , 2016, EMNLP.

[69]  Jianfeng Gao,et al.  Bi-directional Attention with Agreement for Dependency Parsing , 2016, EMNLP.

[70]  J. Chu,et al.  Manipulation of the large Rashba spin splitting in polar two-dimensional transition-metal dichalcogenides , 2016, 1606.07985.

[71]  Angeliki Lazaridou,et al.  The LAMBADA dataset: Word prediction requiring a broad discourse context , 2016, ACL.

[72]  Jian Zhang,et al.  SQuAD: 100,000+ Questions for Machine Comprehension of Text , 2016, EMNLP.

[73]  Zachary Chase Lipton.  The mythos of model interpretability , 2016, ACM Queue.

[74]  Danqi Chen,et al.  A Thorough Examination of the CNN/Daily Mail Reading Comprehension Task , 2016, ACL.

[75]  Jason Weston,et al.  Key-Value Memory Networks for Directly Reading Documents , 2016, EMNLP.

[76]  Philip Bachman,et al.  Natural Language Comprehension with the EpiReader , 2016, EMNLP.

[77]  Ruslan Salakhutdinov,et al.  Gated-Attention Readers for Text Comprehension , 2016, ACL.

[78]  Preslav Nakov,et al.  SemEval-2016 Task 4: Sentiment Analysis in Twitter , 2016, *SEMEVAL.

[79]  Anna Shcherbina,et al.  Not Just a Black Box: Learning Important Features Through Propagating Activation Differences , 2016, ArXiv.

[80]  Rudolf Kadlec,et al.  Text Understanding with the Attention Sum Reader Network , 2016, ACL.

[81]  Jason Weston,et al.  The Goldilocks Principle: Reading Children's Books with Explicit Memory Representations , 2015, ICLR.

[82]  Alexander M. Rush,et al.  A Neural Attention Model for Abstractive Sentence Summarization , 2015, EMNLP.

[83]  Christopher D. Manning,et al.  Effective Approaches to Attention-based Neural Machine Translation , 2015, EMNLP.

[84]  R. Duine,et al.  New perspectives for Rashba spin-orbit coupling. , 2015, Nature materials.

[85]  Phil Blunsom,et al.  Teaching Machines to Read and Comprehend , 2015, NIPS.

[86]  Jason Weston,et al.  End-To-End Memory Networks , 2015, NIPS.

[87]  Jason Weston,et al.  Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks , 2015, ICLR.

[88]  Jeffrey Pennington,et al.  GloVe: Global Vectors for Word Representation , 2014, EMNLP.

[89]  Yoshua Bengio,et al.  Neural Machine Translation by Jointly Learning to Align and Translate , 2014, ICLR.

[90]  Harold S. Park,et al.  A review on the flexural mode of graphene: lattice dynamics, thermal conduction, thermal expansion, elasticity and nanomechanical resonance , 2014, Journal of physics. Condensed matter : an Institute of Physics journal.

[91]  Ying Dai,et al.  Emergence of electric polarity in BiTeX (X = Br and I) monolayers and the giant Rashba spin splitting. , 2014, Physical Chemistry, Chemical Physics - PCCP.

[92]  Yoshua Bengio,et al.  Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation , 2014, EMNLP.

[93]  V. Fal’ko,et al.  Electrons and phonons in single layers of hexagonal indium chalcogenides from ab initio calculations , 2014, 1403.4389.

[94]  Jeffrey Dean,et al.  Distributed Representations of Words and Phrases and their Compositionality , 2013, NIPS.

[95]  Matthew Richardson,et al.  MCTest: A Challenge Dataset for the Open-Domain Machine Comprehension of Text , 2013, EMNLP.

[96]  SUPARNA DUTTASINHA,et al.  Van der Waals heterostructures , 2013, Nature.

[97]  E. Chulkov,et al.  Ideal two-dimensional electron systems with a giant Rashba-type spin splitting in real materials: surfaces of bismuth tellurohalides. , 2012, Physical review letters.

[98]  A. Varykhalov,et al.  Topological surface state under graphene for two-dimensional spintronics in air , 2011, 1104.3308.

[99]  Julio Gómez-Herrero,et al.  2D materials: to graphene and beyond. , 2011, Nanoscale.

[100]  Oren Etzioni,et al.  Machine Reading at the University of Washington , 2010, HLT-NAACL 2010.

[101]  E. Annese,et al.  Peculiar Rashba splitting originating from the two-dimensional symmetry of the surface. , 2009, Physical review letters.

[102]  Xi Chen,et al.  Experimental demonstration of topological surface states protected by time-reversal symmetry. , 2009, Physical review letters.

[103]  S Das Sarma,et al.  Generic new platform for topological quantum computation using semiconductor heterostructures. , 2009, Physical review letters.

[104]  Stefan Grimme,et al.  Semiempirical GGA‐type density functional constructed with a long‐range dispersion correction , 2006, J. Comput. Chem..

[105]  K. Kern,et al.  Giant Spin-splitting in the Bi/Ag(111) Surface Alloy , 2005, cond-mat/0509509.

[106]  Chin-Yew Lin,et al.  ROUGE: A Package for Automatic Evaluation of Summaries , 2004, ACL 2004.

[107]  G. Bihlmayer,et al.  Strong spin-orbit splitting on bi surfaces. , 2004, Physical review letters.

[108]  R. Joynt,et al.  Rashba spin-orbit coupling and spin relaxation in silicon quantum wells , 2004, cond-mat/0401615.

[109]  G. Scuseria,et al.  Hybrid functionals based on a screened Coulomb potential , 2003 .

[110]  Salim Roukos,et al.  Bleu: a Method for Automatic Evaluation of Machine Translation , 2002, ACL.

[111]  Matthias Scheffler,et al.  Oxygen adsorption on Ag(111): A density-functional theory investigation , 2002 .

[112]  Ellen Riloff,et al.  A Rule-based Question Answering System for Reading Comprehension Tests , 2000 .

[113]  Lynette Hirschman,et al.  Deep Read: A Reading Comprehension System , 1999, ACL.

[114]  S. Hochreiter,et al.  Long Short-Term Memory , 1997, Neural Computation.

[115]  Hideaki Takayanagi,et al.  Gate Control of Spin-Orbit Interaction in an Inverted In0 , 1997 .

[116]  Burke,et al.  Generalized Gradient Approximation Made Simple. , 1996, Physical review letters.

[117]  Jensen,et al.  Spin Splitting of an Au(111) Surface State Band Observed with Angle Resolved Photoelectron Spectroscopy. , 1996, Physical review letters.

[118]  Drouhin,et al.  Band structure of indium phosphide from near-band-gap photoemission. , 1991, Physical review. B, Condensed matter.

[119]  S. Datta,et al.  Electronic analog of the electro‐optic modulator , 1990 .

[120]  Geoffrey E. Hinton,et al.  Learning representations by back-propagating errors , 1986, Nature.

[121]  Weber,et al.  Computer simulation of local order in condensed phases of silicon. , 1985, Physical review. B, Condensed matter.

[122]  Ming-Wei Chang,et al.  BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding , 2019, NAACL.

[123]  Eunsol Choi,et al.  CONVERSATIONAL MACHINE COMPREHENSION , 2019 .

[124]  Kaixuan Li,et al.  Tunable Rashba spin splitting in two-dimensional graphene/As-I heterostructures , 2018 .

[125]  Paul M. B. Vitányi,et al.  Author ' s personal copy A Fast Quartet tree heuristic for hierarchical clustering , 2010 .

[126]  E. Rashba,et al.  Properties of a 2D electron gas with lifted spectral degeneracy , 1984 .