| Optimizing statistical machine translation for text simplification W Xu, C Napoles, E Pavlick, Q Chen, C Callison-Burch Transactions of the Association for Computational Linguistics 4, 401-415, 2016 | 658 | 2016 |
| Problems in current text simplification research: New data can help W Xu, C Callison-Burch, C Napoles Transactions of the Association for Computational Linguistics 3, 283-297, 2015 | 510 | 2015 |
| Shared tasks of the 2015 workshop on noisy user-generated text: Twitter lexical normalization and named entity recognition T Baldwin, MC De Marneffe, B Han, YB Kim, A Ritter, W Xu Proceedings of the workshop on noisy user-generated text, 126-135, 2015 | 257 | 2015 |
| Paraphrasing for style W Xu, A Ritter, WB Dolan, R Grishman, C Cherry 24th International Conference on Computational Linguistics, 2899 - 2914, 2012 | 209 | 2012 |
| Semeval-2015 task 1: Paraphrase and semantic similarity in twitter (pit) W Xu, C Callison-Burch, WB Dolan Proceedings of the 9th international workshop on semantic evaluation …, 2015 | 184 | 2015 |
| A Continuously Growing Dataset of Sentential Paraphrases W Lan, S Qiu, H He, W Xu Proceedings of 2017 Conference on Empirical Methods in Natural Language …, 2017 | 178 | 2017 |
| Neural Network Models for Paraphrase Identification, Semantic Textual Similarity, Natural Language Inference, and Question Answering W Lan, W Xu Proceedings of COLING 2018, the 27th International Conference on …, 2018 | 164 | 2018 |
| Results of the WNUT16 named entity recognition shared task B Strauss, BE Toma, A Ritter, MC de Marneffe, W Xu Proceedings of the 2nd Workshop on Noisy User-generated Text, 138-144, 2016 | 151 | 2016 |
| Neural CRF model for sentence alignment in text simplification C Jiang, M Maddela, W Lan, Y Zhong, W Xu arXiv preprint arXiv:2005.02324, 2020 | 147 | 2020 |
| Extracting lexically divergent paraphrases from Twitter W Xu, A Ritter, C Callison-Burch, WB Dolan, Y Ji Transactions of the Association for Computational Linguistics 2, 435-448, 2014 | 136 | 2014 |
| The gem benchmark: Natural language generation, its evaluation and metrics S Gehrmann, T Adewumi, K Aggarwal, PS Ammanamanchi, ... arXiv preprint arXiv:2102.01672, 2021 | 135 | 2021 |
| Extractive summarization using inter-and intra-event relevance W Li, W Xu, M Wu, Q Lu, C Yuan Proceedings of the 21st International Conference on Computational …, 2006 | 130 | 2006 |
| Filling Knowledge Base Gaps for Distant Supervision of Relation Extraction W Xu, R Hoffmann, Z Le, R Grishman | 123 | 2013 |
| Code and named entity recognition in stackoverflow J Tabassum, M Maddela, W Xu, A Ritter arXiv preprint arXiv:2005.01634, 2020 | 116 | 2020 |
| Controllable text simplification with explicit paraphrasing M Maddela, F Alva-Manchego, W Xu arXiv preprint arXiv:2010.11004, 2020 | 96 | 2020 |
| A Word-Complexity Lexicon and A Neural Readability Ranking Model for Lexical Simplification M Maddela, W Xu Proceedings of the 2018 Conference on Empirical Methods in Natural Language …, 2018 | 93 | 2018 |
| An empirical study of pre-trained transformers for Arabic information extraction W Lan, Y Chen, W Xu, A Ritter arXiv preprint arXiv:2004.14519, 2020 | 73 | 2020 |
| An Annotated Corpus for Machine Reading of Instructions in Wet Lab Protocols C Kulkarni, W Xu, A Ritter, R Machiraju Character-based Neural Networks for Sentence Pair Modeling, 2018 | 71 | 2018 |
| Infusion of Labeled Data into Distant Supervision for Relation Extraction M Pershina, B Min, W Xu, R Grishman ACL, 2014 | 69 | 2014 |
| Generalizing natural language analysis through span-relation representations Z Jiang, W Xu, J Araki, G Neubig arXiv preprint arXiv:1911.03822, 2019 | 59 | 2019 |
Alan RitterGeorgia Institute of TechnologyVerified email at cc.gatech.edu
