mtybilly/Instruct2DS huggingface dataset:
Learning to Explain Entity Relationships in Knowledge Graphs
Authors: Nikos Voskarides, Edgar Meij, Manos Tsagkias, Maarten de Rijke, Wouter Weerkamp
Published in: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) (Long Paper Track), 2015
Bring you to the past: Automatic Generation of Topically Relevant Event Chronicles
Authors: Tao Ge, Wenzhe Pei, Heng Ji, Sujian Li, Baobao Chang, Zhifang Sui
Published in: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) (Long Paper Track), 2015
Context-aware Entity Morph Decoding
Authors: Boliang Zhang, Hongzhao Huang, Xiaoman Pan, Sujian Li, Chin-Yew Lin, Heng Ji, Kevin Knight, Zhen Wen, Yizhou Sun, Jiawei Han, Bulent Yener
Published in: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) (Long Paper Track), 2015
Omnia Mutantur, Nihil Interit: Connecting Past with Present by Finding Corresponding Terms across Time
Authors: Yating Zhang, Adam Jatowt, Sourav Bhowmick, Katsumi Tanaka
Published in: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) (Long Paper Track), 2015
Jointly optimizing word representations for lexical and sentential tasks with the C-PHRASE model
Authors: Nghia The Pham, Germán Kruszewski, Angeliki Lazaridou, Marco Baroni
Published in: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) (Long Paper Track), 2015
Learning Word Representations from Scarce and Noisy Data with Embedding Subspaces
Authors: Ramon F. Astudillo, Silvio Amir, Wang Ling, Mário Silva, Isabel Trancoso
Published in: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) (Long Paper Track), 2015
Predicting Polarities of Tweets by Composing Word Embeddings with Long Short-Term Memory
Authors: Xin Wang, Yuanchao Liu, Chengjie Sun, Baoxun Wang, Xiaolong Wang
Published in: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) (Long Paper Track), 2015
Learning Tag Embeddings and Tag-specific Composition Functions in Recursive Neural Network
Authors: Qiao Qian, Bo Tian, Minlie Huang, Yang Liu, Xuan Zhu, Xiaoyan Zhu
Published in: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) (Long Paper Track), 2015
Tea Party in the House: A Hierarchical Ideal Point Topic Model and Its Application to Republican Legislators in the 112th Congress
Authors: Viet-An Nguyen, Jordan Boyd-Graber, Philip Resnik, Kristina Miler
Published in: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) (Long Paper Track), 2015
A Computationally Efficient Algorithm for Learning Topical Collocation Models
Authors: Zhendong Zhao, Lan Du, Benjamin Börschinger, John K Pate, Massimiliano Ciaramita, Mark Steedman, Mark Johnson
Published in: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) (Long Paper Track), 2015
Adding Semantics to Data-Driven Paraphrasing
Authors: Ellie Pavlick, Johan Bos, Malvina Nissim, Charley Beller, Benjamin Van Durme, Chris Callison-Burch
Published in: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) (Long Paper Track), 2015
Abstractive Multi-Document Summarization via Phrase Selection and Merging
Authors: Lidong Bing, Piji Li, Yi Liao, Wai Lam, Weiwei Guo, Rebecca Passonneau
Published in: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) (Long Paper Track), 2015
Linguistic Harbingers of Betrayal: A Case Study on an Online Strategy Game
Authors: Vlad Niculae, Srijan Kumar, Jordan Boyd-Graber, Cristian Danescu-Niculescu-Mizil
Published in: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) (Long Paper Track), 2015
Who caught a cold ? - Identifying the subject of a symptom
Authors: Shin Kanouchi, Mamoru Komachi, Naoaki Okazaki, Eiji Aramaki, Hiroshi Ishikawa
Published in: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) (Long Paper Track), 2015
Inverted indexing for cross-lingual NLP
Authors: Anders Søgaard, Željko Agić, Héctor Mart\ńez Alonso, Barbara Plank, Bernd Bohnet, Anders Johannsen
Published in: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) (Long Paper Track), 2015
Inducing Word and Part-of-Speech with Pitman-Yor Hidden Semi-Markov Models
Authors: Kei Uchiumi, Hiroshi Tsukahara, Daichi Mochihashi
Published in: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) (Long Paper Track), 2015
Domain-Specific Paraphrase Extraction
Authors: Ellie Pavlick, Juri Ganitkevitch, Tsz Ping Chan, Xuchen Yao, Benjamin Van Durme, Chris Callison-Burch
Published in: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers) (Short Paper Track), 2015
Language Models for Image Captioning: The Quirks and What Works
Authors: Jacob Devlin, Hao Cheng, Hao Fang, Saurabh Gupta, Li Deng, Xiaodong He, Geoffrey Zweig, Margaret Mitchell
Published in: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers) (Short Paper Track), 2015
Discriminative Preordering Meets Kendall`s $τ$ Maximization
Authors: Sho Hoshino, Yusuke Miyao, Katsuhito Sudoh, Katsuhiko Hayashi, Masaaki Nagata
Published in: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers) (Short Paper Track), 2015
Exploring the Planet of the APEs: a Comparative Study of State-of-the-art Methods for MT Automatic Post-Editing
Authors: Rajen Chatterjee, Marion Weller, Matteo Negri, Marco Turchi
Published in: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers) (Short Paper Track), 2015
Frame-Semantic Role Labeling with Heterogeneous Annotations
Authors: Meghana Kshirsagar, Sam Thomson, Nathan Schneider, Jaime Carbonell, Noah A. Smith, Chris Dyer
Published in: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers) (Short Paper Track), 2015
Semantic Interpretation of Superlative Expressions via Structured Knowledge Bases
Authors: Sheng Zhang, Yansong Feng, Songfang Huang, Kun Xu, Zhe Han, Dongyan Zhao
Published in: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers) (Short Paper Track), 2015
A Lexicalized Tree Kernel for Open Information Extraction
Authors: Ying Xu, Christoph Ringlstetter, Mi-Young Kim, Grzegorz Kondrak, Randy Goebel, Yusuke Miyao
Published in: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers) (Short Paper Track), 2015
Learning Topic Hierarchies for Wikipedia Categories
Authors: Linmei Hu, Xuzhong Wang, Mengdi Zhang, Juanzi Li, Xiaoli Li, Chao Shao, Jie Tang, Yongbin Liu
Published in: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers) (Short Paper Track), 2015
Semantic Clustering and Convolutional Neural Network for Short Text Categorization
Authors: Peng Wang, Jiaming Xu, Bo Xu, Chenglin Liu, Heng Zhang, Fangyuan Wang, Hongwei Hao
Published in: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers) (Short Paper Track), 2015
FrameNet+: Fast Paraphrastic Tripling of FrameNet
Authors: Ellie Pavlick, Travis Wolfe, Pushpendre Rastogi, Chris Callison-Burch, Mark Dredze, Benjamin Van Durme
Published in: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers) (Short Paper Track), 2015
TR9856: A Multi-word Term Relatedness Benchmark
Authors: Ran Levy, Liat Ein-Dor, Shay Hummel, Ruty Rinott, Noam Slonim
Published in: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers) (Short Paper Track), 2015
PPDB 2.0: Better paraphrase ranking, fine-grained entailment relations, word embeddings, and style classification
Authors: Ellie Pavlick, Pushpendre Rastogi, Juri Ganitkevitch, Benjamin Van Durme, Chris Callison-Burch
Published in: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers) (Short Paper Track), 2015
deltaBLEU: A Discriminative Metric for Generation Tasks with Intrinsically Diverse Targets
Authors: Michel Galley, Chris Brockett, Alessandro Sordoni, Yangfeng Ji, Michael Auli, Chris Quirk, Margaret Mitchell, Jianfeng Gao, Bill Dolan
Published in: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers) (Short Paper Track), 2015
The Fixed-Size Ordinally-Forgetting Encoding Method for Neural Network Language Models
Authors: ShiLiang Zhang, Hui Jiang, MingBin Xu, JunFeng Hou, LiRong Dai
Published in: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers) (Short Paper Track), 2015
A Computational Approach to Automatic Prediction of Drunk-Texting
Authors: Aditya Joshi, Abhijit Mishra, Balamurali AR, Pushpak Bhattacharyya, Mark J. Carman
Published in: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers) (Short Paper Track), 2015
Reducing infrequent-token perplexity via variational corpora
Authors: Yusheng Xie, Pranjal Daga, Yu Cheng, Kunpeng Zhang, Ankit Agrawal, Alok Choudhary
Published in: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers) (Short Paper Track), 2015
Tackling Sparsity, the Achilles Heel of Social Networks: Language Model Smoothing via Social Regularization
Authors: Rui Yan, Xiang Li, Mengwen Liu, Xiaohua Hu
Published in: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers) (Short Paper Track), 2015
Automatic Keyword Extraction on Twitter
Authors: Luís Marujo, Wang Ling, Isabel Trancoso, Chris Dyer, Alan W. Black, Anatole Gershman, David Martins de Matos, João Neto, Jaime Carbonell
Published in: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers) (Short Paper Track), 2015
Towards a Contextual Pragmatic Model to Detect Irony in Tweets
Authors: Jihen Karoui, Farah Benamara Zitoune, Véronique Moriceau, Nathalie Aussenac-Gilles, Lamia Hadrich Belguith
Published in: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers) (Short Paper Track), 2015
Thread-Level Information for Comment Classification in Community Question Answering
Authors: Alberto Barrón-Cedeño, Simone Filice, Giovanni Da San Martino, Shafiq Joty, Lluís Màrquez, Preslav Nakov, Alessandro Moschitti
Published in: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers) (Short Paper Track), 2015
Linguistic Template Extraction for Recognizing Reader-Emotion and Emotional Resonance Writing Assistance
Authors: Yung-Chun Chang, Cen-Chieh Chen, Yu-Lun Hsieh, Chien Chin Chen, Wen-Lian Hsu
Published in: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers) (Short Paper Track), 2015
Multi-domain Dialog State Tracking using Recurrent Neural Networks
Authors: Nikola Mrkšić, Diarmuid Ó Séaghdha, Blaise Thomson, ́ Milica Gaši\, Pei-Hao Su, David Vandyke, Tsung-Hsien Wen, Steve Young
Published in: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers) (Short Paper Track), 2015
The Role of Lexico-Semantic Feedback in Open-Domain Textual Question-Answering
Authors: Sanda Harabagiu, Dan Moldovan, Marius Pasca, Rada Mihalcea, Mihai Surdeanu, Razvan Bunsecu, Roxana Girju, Vasile Rus, Paul Morarescu
Published in: Proceedings of the 39th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2001
REPT: Bridging Language Models and Machine Reading Comprehension via Retrieval-Based Pre-training
Authors: Fangkai Jiao, Yangyang Guo, Yilin Niu, Feng Ji, Feng-Lin Li, Liqiang Nie
Published in: Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 (Findings Track), 2021
CasEE: A Joint Learning Framework with Cascade Decoding for Overlapping Event Extraction
Authors: Jiawei Sheng, Shu Guo, Bowen Yu, Qian Li, Yiming Hei, Lihong Wang, Tingwen Liu, Hongbo Xu
Published in: Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 (Findings Track), 2021
CoDesc: A Large Code--Description Parallel Dataset
Authors: Masum Hasan, Tanveer Muttaqueen, Abdullah Al Ishtiaq, Kazi Sajeed Mehrab, Md. Mahim Anjum Haque, Tahmid Hasan, Wasi Ahmad, Anindya Iqbal, Rifat Shahriyar
Published in: Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 (Findings Track), 2021
Reader-Guided Passage Reranking for Open-Domain Question Answering
Authors: Yuning Mao, Pengcheng He, Xiaodong Liu, Yelong Shen, Jianfeng Gao, Jiawei Han, Weizhu Chen
Published in: Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 (Findings Track), 2021
GLGE: A New General Language Generation Evaluation Benchmark
Authors: Dayiheng Liu, Yu Yan, Yeyun Gong, Weizhen Qi, Hang Zhang, Jian Jiao, Weizhu Chen, Jie Fu, Linjun Shou, Ming Gong, Pengcheng Wang, Jiusheng Chen, Daxin Jiang, Jiancheng Lv, Ruofei Zhang, Winnie Wu, Ming Zhou, Nan Duan
Published in: Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 (Findings Track), 2021
GeoQA: A Geometric Question Answering Benchmark Towards Multimodal Numerical Reasoning
Authors: Jiaqi Chen, Jianheng Tang, Jinghui Qin, Xiaodan Liang, Lingbo Liu, Eric Xing, Liang Lin
Published in: Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 (Findings Track), 2021
KGPool: Dynamic Knowledge Graph Context Selection for Relation Extraction
Authors: Abhishek Nadgeri, Anson Bastos, Kuldeep Singh, Isaiah Onando Mulang′, Johannes Hoffart, Saeedeh Shekarpour, Vijay Saraswat
Published in: Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 (Findings Track), 2021
Deep Learning against COVID-19: Respiratory Insufficiency Detection in Brazilian Portuguese Speech
Authors: Edresson Casanova, Lucas Gris, Augusto Camargo, Daniel da Silva, Murilo Gazzola, Ester Sabino, Anna Levin, Arnaldo Candido Jr, Sandra Aluisio, Marcelo Finger
Published in: Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 (Findings Track), 2021
A Dialogue-based Information Extraction System for Medical Insurance Assessment
Authors: Shuang Peng, Mengdi Zhou, Minghui Yang, Haitao Mi, Shaosheng Cao, Zujie Wen, Teng Xu, Hongbin Wang, Lei Liu
Published in: Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 (Findings Track), 2021
Read, Listen, and See: Leveraging Multimodal Information Helps Chinese Spell Checking
Authors: Heng-Da Xu, Zhongli Li, Qingyu Zhou, Chao Li, Zizhen Wang, Yunbo Cao, Heyan Huang, Xian-Ling Mao
Published in: Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 (Findings Track), 2021
Hashing based Efficient Inference for Image-Text Matching
Authors: Rong-Cheng Tu, Lei Ji, Huaishao Luo, Botian Shi, Heyan Huang, Nan Duan, Xian-Ling Mao
Published in: Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 (Findings Track), 2021
COM2SENSE: A Commonsense Reasoning Benchmark with Complementary Sentences
Authors: Shikhar Singh, Nuan Wen, Yu Hou, Pegah Alipoormolabashi, Te-lin Wu, Xuezhe Ma, Nanyun Peng
Published in: Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 (Findings Track), 2021
Handling Cross- and Out-of-Domain Samples in Thai Word Segmentation
Authors: Peerat Limkonchotiwat, Wannaphong Phatthiyaphaibun, Raheem Sarwar, Ekapol Chuangsuwanich, Sarana Nutanong
Published in: Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 (Findings Track), 2021
Investigating the Reordering Capability in CTC-based Non-Autoregressive End-to-End Speech Translation
Authors: Shun-Po Chuang, Yung-Sung Chuang, Chih-Chiang Chang, Hung-yi Lee
Published in: Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 (Findings Track), 2021
Do Explanations Help Users Detect Errors in Open-Domain QA? An Evaluation of Spoken vs. Visual Explanations
Authors: Ana Valeria González, Gagan Bansal, Angela Fan, Yashar Mehdad, Robin Jia, Srinivasan Iyer
Published in: Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 (Findings Track), 2021
Learning Algebraic Recombination for Compositional Generalization
Authors: Chenyao Liu, Shengnan An, Zeqi Lin, Qian Liu, Bei Chen, Jian-Guang Lou, Lijie Wen, Nanning Zheng, Dongmei Zhang
Published in: Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 (Findings Track), 2021
On-the-Fly Attention Modulation for Neural Generation
Authors: Yue Dong, Chandra Bhagavatula, Ximing Lu, Jena D. Hwang, Antoine Bosselut, Jackie Chi Kit Cheung, Yejin Choi
Published in: Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 (Findings Track), 2021
Manual Evaluation Matters: Reviewing Test Protocols of Distantly Supervised Relation Extraction
Authors: Tianyu Gao, Xu Han, Yuzhuo Bai, Keyue Qiu, Zhiyu Xie, Yankai Lin, Zhiyuan Liu, Peng Li, Maosong Sun, Jie Zhou
Published in: Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 (Findings Track), 2021
GCRC: A New Challenging MRC Dataset from Gaokao Chinese for Explainable Evaluation
Authors: Hongye Tan, Xiaoyue Wang, Yu Ji, Ru Li, Xiaoli Li, Zhiwei Hu, Yunxiao Zhao, Xiaoqi Han
Published in: Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 (Findings Track), 2021
MRN: A Locally and Globally Mention-Based Reasoning Network for Document-Level Relation Extraction
Authors: Jingye Li, Kang Xu, Fei Li, Hao Fei, Yafeng Ren, Donghong Ji
Published in: Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 (Findings Track), 2021
LICHEE: Improving Language Model Pre-training with Multi-grained Tokenization
Authors: Weidong Guo, Mingjun Zhao, Lusheng Zhang, Di Niu, Jinwen Luo, Zhenhua Liu, Zhenyang Li, Jianbo Tang
Published in: Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 (Findings Track), 2021
Detecting Hallucinated Content in Conditional Neural Sequence Generation
Authors: Chunting Zhou, Graham Neubig, Jiatao Gu, Mona Diab, Francisco Guzmán, Luke Zettlemoyer, Marjan Ghazvininejad
Published in: Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 (Findings Track), 2021
K-Adapter: Infusing Knowledge into Pre-Trained Models with Adapters
Authors: Ruize Wang, Duyu Tang, Nan Duan, Zhongyu Wei, Xuanjing Huang, Jianshu Ji, Guihong Cao, Daxin Jiang, Ming Zhou
Published in: Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 (Findings Track), 2021
Better Robustness by More Coverage: Adversarial and Mixup Data Augmentation for Robust Finetuning
Authors: Chenglei Si, Zhengyan Zhang, Fanchao Qi, Zhiyuan Liu, Yasheng Wang, Qun Liu, Maosong Sun
Published in: Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 (Findings Track), 2021
KACC: A Multi-task Benchmark for Knowledge Abstraction, Concretization and Completion
Authors: Jie Zhou, Shengding Hu, Xin Lv, Cheng Yang, Zhiyuan Liu, Wei Xu, Jie Jiang, Juanzi Li, Maosong Sun
Published in: Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 (Findings Track), 2021
Gaussian Process based Deep Dyna-Q approach for Dialogue Policy Learning
Authors: Guanlin Wu, Wenqi Fang, Ji Wang, Jiang Cao, Weidong Bao, Yang Ping, Xiaomin Zhu, Zheng Wang
Published in: Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 (Findings Track), 2021
“Does it Matter When I Think You Are Lying?” Improving Deception Detection by Integrating Interlocutor`s Judgements in Conversations
Authors: Huang-Cheng Chou, Woan-Shiuan Chien, Da-Cheng Juan, Chi-Chun Lee
Published in: Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 (Findings Track), 2021
RetroGAN: A Cyclic Post-Specialization System for Improving Out-of-Knowledge and Rare Word Representations
Authors: Pedro Colon-Hernandez, Yida Xin, Henry Lieberman, Catherine Havasi, Cynthia Breazeal, Peter Chin
Published in: Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 (Findings Track), 2021
Language-based General Action Template for Reinforcement Learning Agents
Authors: Ryosuke Kohita, Akifumi Wachi, Daiki Kimura, Subhajit Chaudhury, Michiaki Tatsubori, Asim Munawar
Published in: Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 (Findings Track), 2021
Improving Event Causality Identification via Self-Supervised Representation Learning on External Causal Statement
Authors: Xinyu Zuo, Pengfei Cao, Yubo Chen, Kang Liu, Jun Zhao, Weihua Peng, Yuguang Chen
Published in: Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 (Findings Track), 2021
PAIR: Leveraging Passage-Centric Similarity Relation for Improving Dense Passage Retrieval
Authors: Ruiyang Ren, Shangwen Lv, Yingqi Qu, Jing Liu, Wayne Xin Zhao, QiaoQiao She, Hua Wu, Haifeng Wang, Ji-Rong Wen
Published in: Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 (Findings Track), 2021
Correcting Chinese Spelling Errors with Phonetic Pre-training
Authors: Ruiqing Zhang, Chao Pang, Chuanqiang Zhang, Shuohuan Wang, Zhongjun He, Yu Sun, Hua Wu, Haifeng Wang
Published in: Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 (Findings Track), 2021
CONDA: a CONtextual Dual-Annotated dataset for in-game toxicity understanding and detection
Authors: Henry Weld, Guanghao Huang, Jean Lee, Tongshu Zhang, Kunze Wang, Xinghong Guo, Siqu Long, Josiah Poon, Caren Han
Published in: Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 (Findings Track), 2021
PLATO-2: Towards Building an Open-Domain Chatbot via Curriculum Learning
Authors: Siqi Bao, Huang He, Fan Wang, Hua Wu, Haifeng Wang, Wenquan Wu, Zhen Guo, Zhibin Liu, Xinchao Xu
Published in: Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 (Findings Track), 2021
JointGT: Graph-Text Joint Representation Learning for Text Generation from Knowledge Graphs
Authors: Pei Ke, Haozhe Ji, Yu Ran, Xin Cui, Liwei Wang, Linfeng Song, Xiaoyan Zhu, Minlie Huang
Published in: Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 (Findings Track), 2021
GEM: A General Evaluation Benchmark for Multimodal Tasks
Authors: Lin Su, Nan Duan, Edward Cui, Lei Ji, Chenfei Wu, Huaishao Luo, Yongfei Liu, Ming Zhong, Taroon Bharti, Arun Sacheti
Published in: Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 (Findings Track), 2021
Making Flexible Use of Subtasks: A Multiplex Interaction Network for Unified Aspect-based Sentiment Analysis
Authors: Guoxin Yu, Xiang Ao, Ling Luo, Min Yang, Xiaofei Sun, Jiwei Li, Qing He
Published in: Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 (Findings Track), 2021
When Time Makes Sense: A Historically-Aware Approach to Targeted Sense Disambiguation
Authors: Kaspar Beelen, Federico Nanni, Mariona Coll Ardanuy, Kasra Hosseini, Giorgia Tolfo, Barbara McGillivray
Published in: Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 (Findings Track), 2021
Detecting Harmful Memes and Their Targets
Authors: Shraman Pramanick, Dimitar Dimitrov, Rituparna Mukherjee, Shivam Sharma, Md. Shad Akhtar, Preslav Nakov, Tanmoy Chakraborty
Published in: Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 (Findings Track), 2021
HacRED: A Large-Scale Relation Extraction Dataset Toward Hard Cases in Practical Applications
Authors: Qiao Cheng, Juntao Liu, Xiaoye Qu, Jin Zhao, Jiaqing Liang, Zhefeng Wang, Baoxing Huai, Nicholas Jing Yuan, Yanghua Xiao
Published in: Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 (Findings Track), 2021
Combining Static Word Embeddings and Contextual Representations for Bilingual Lexicon Induction
Authors: Jinpeng Zhang, Baijun Ji, Nini Xiao, Xiangyu Duan, Min Zhang, Yangbin Shi, Weihua Luo
Published in: Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 (Findings Track), 2021
Self-Supervised Document Similarity Ranking via Contextualized Language Models and Hierarchical Inference
Authors: Dvir Ginzburg, Itzik Malkiel, Oren Barkan, Avi Caciularu, Noam Koenigstein
Published in: Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 (Findings Track), 2021
IndoCollex: A Testbed for Morphological Transformation of Indonesian Colloquial Words
Authors: Haryo Akbarianto Wibowo, Made Nindyatama Nityasya, Afra Feyza Akyürek, Suci Fitriany, Alham Fikri Aji, Radityo Eko Prasojo, Derry Tanti Wijaya
Published in: Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 (Findings Track), 2021
Disfl-QA: A Benchmark Dataset for Understanding Disfluencies in Question Answering
Authors: Aditya Gupta, Jiacheng Xu, Shyam Upadhyay, Diyi Yang, Manaal Faruqui
Published in: Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 (Findings Track), 2021
Does Robustness Improve Fairness? Approaching Fairness with Word Substitution Robustness Methods for Text Classification
Authors: Yada Pruksachatkun, Satyapriya Krishna, Jwala Dhamala, Rahul Gupta, Kai-Wei Chang
Published in: Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 (Findings Track), 2021
Multilingual Translation from Denoising Pre-Training
Authors: Yuqing Tang, Chau Tran, Xian Li, Peng-Jen Chen, Naman Goyal, Vishrav Chaudhary, Jiatao Gu, Angela Fan
Published in: Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 (Findings Track), 2021
Towards Protecting Vital Healthcare Programs by Extracting Actionable Knowledge from Policy
Authors: Vanessa Lopez, Nagesh Yadav, Gabriele Picco, Inge Vejsbjerg, Eoin Carrol, Seamus Brady, Marco Luca Sbodio, Lam Thanh Hoang, Miao Wei, John Segrave
Published in: Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 (Findings Track), 2021
Leveraging Abstract Meaning Representation for Knowledge Base Question Answering
Authors: Pavan Kapanipathi, Ibrahim Abdelaziz, Srinivas Ravishankar, Salim Roukos, Alexander Gray, Ramón Fernandez Astudillo, Maria Chang, Cristina Cornelio, Saswati Dana, Achille Fokoue, Dinesh Garg, Alfio Gliozzo, Sairam Gurajada, Hima Karanam, Naweed Khan, Dinesh Khandelwal, Young-Suk Lee, Yunyao Li, Francois Luus, Ndivhuwo Makondo, Nandana Mihindukulasooriya, Tahira Naseem, Sumit Neelam, Lucian Popa, Revanth Gangi Reddy, Ryan Riegel, Gaetano Rossiello, Udit Sharma, G P Shrivatsa Bhargav, Mo Yu
Published in: Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 (Findings Track), 2021
Constructing Flow Graphs from Procedural Cybersecurity Texts
Authors: Kuntal Kumar Pal, Kazuaki Kashihara, Pratyay Banerjee, Swaroop Mishra, Ruoyu Wang, Chitta Baral
Published in: Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 (Findings Track), 2021
Cluster-Former: Clustering-based Sparse Transformer for Question Answering
Authors: Shuohang Wang, Luowei Zhou, Zhe Gan, Yen-Chun Chen, Yuwei Fang, Siqi Sun, Yu Cheng, Jingjing Liu
Published in: Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 (Findings Track), 2021
Minimally-Supervised Morphological Segmentation using Adaptor Grammars with Linguistic Priors
Authors: Ramy Eskander, Cass Lowry, Sujay Khandagale, Francesca Callejas, Judith Klavans, Maria Polinsky, Smaranda Muresan
Published in: Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 (Findings Track), 2021
Multi-Task Learning and Adapted Knowledge Models for Emotion-Cause Extraction
Authors: Elsbeth Turcan, Shuai Wang, Rishita Anubhai, Kasturi Bhattacharjee, Yaser Al-Onaizan, Smaranda Muresan
Published in: Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 (Findings Track), 2021
Learning Contextualized Knowledge Structures for Commonsense Reasoning
Authors: Jun Yan, Mrigank Raman, Aaron Chan, Tianyu Zhang, Ryan Rossi, Handong Zhao, Sungchul Kim, Nedim Lipka, Xiang Ren
Published in: Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 (Findings Track), 2021
On the Distribution, Sparsity, and Inference-time Quantization of Attention Values in Transformers
Authors: Tianchu Ji, Shraddhan Jain, Michael Ferdman, Peter Milder, H. Andrew Schwartz, Niranjan Balasubramanian
Published in: Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 (Findings Track), 2021
VLM: Task-agnostic Video-Language Model Pre-training for Video Understanding
Authors: Hu Xu, Gargi Ghosh, Po-Yao Huang, Prahal Arora, Masoumeh Aminzadeh, Christoph Feichtenhofer, Florian Metze, Luke Zettlemoyer
Published in: Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 (Findings Track), 2021
PSED: A Dataset for Selecting Emphasis in Presentation Slides
Authors: Amirreza Shirani, Giai Tran, Hieu Trinh, Franck Dernoncourt, Nedim Lipka, Jose Echevarria, Thamar Solorio, Paul Asente
Published in: Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 (Findings Track), 2021
Exploring Self-Identified Counseling Expertise in Online Support Forums
Authors: Allison Lahnala, Yuntian Zhao, Charles Welch, Jonathan K. Kummerfeld, Lawrence C An, Kenneth Resnicow, Rada Mihalcea, Verónica Pérez-Rosas
Published in: Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 (Findings Track), 2021
He is very intelligent, she is very beautiful? On Mitigating Social Biases in Language Modelling and Generation
Authors: Aparna Garimella, Akhash Amarnath, Kiran Kumar, Akash Pramod Yalla, Anandhavelu N, Niyati Chhaya, Balaji Vasan Srinivasan
Published in: Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 (Findings Track), 2021
HIT - A Hierarchically Fused Deep Attention Network for Robust Code-mixed Language Representation
Authors: Ayan Sengupta, Sourabh Kumar Bhattacharjee, Tanmoy Chakraborty, Md. Shad Akhtar
Published in: Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 (Findings Track), 2021
XL-Sum: Large-Scale Multilingual Abstractive Summarization for 44 Languages
Authors: Tahmid Hasan, Abhik Bhattacharjee, Md. Saiful Islam, Kazi Mubasshir, Yuan-Fang Li, Yong-Bin Kang, M. Sohel Rahman, Rifat Shahriyar
Published in: Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 (Findings Track), 2021
Are Multilingual Models the Best Choice for Moderately Under-resourced Languages? A Comprehensive Assessment for Catalan
Authors: Jordi Armengol-Estapé, Casimiro Pio Carrino, Carlos Rodriguez-Penagos, Ona de Gibert Bonet, Carme Armentano-Oller, Aitor Gonzalez-Agirre, Maite Melero, Marta Villegas
Published in: Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 (Findings Track), 2021
Automatic Speech Recognition in Sanskrit: A New Speech Corpus and Modelling Insights
Authors: Devaraja Adiga, Rishabh Kumar, Amrith Krishna, Preethi Jyothi, Ganesh Ramakrishnan, Pawan Goyal
Published in: Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 (Findings Track), 2021
Knowledge Distillation for Quality Estimation
Authors: Amit Gajbhiye, Marina Fomicheva, Fernando Alva-Manchego, Frédéric Blain, Abiola Obamuyide, Nikolaos Aletras, Lucia Specia
Published in: Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 (Findings Track), 2021
Investigating label suggestions for opinion mining in German Covid-19 social media
Authors: Tilman Beck, Ji-Ung Lee, Christina Viehmann, Marcus Maurer, Oliver Quiring, Iryna Gurevych
Published in: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) (Long Paper Track), 2021

Abstract: This work investigates the use of interactively updated label suggestions to improve upon the efficiency of gathering annotations on the task of opinion mining in German Covid-19 social media data. We develop guidelines to conduct a controlled annotation study with social science students and find that suggestions from a model trained on a small, expert-annotated dataset already lead to a substantial improvement -- in terms of inter-annotator agreement (+.14 Fleiss' \ensuremathąppa) and annotation quality -- compared to students that do not receive any label suggestions. We further find that label suggestions from interactively trained models do not lead to an improvement over suggestions from a static model. Nonetheless, our analysis of suggestion bias shows that annotators remain capable of reflecting upon the suggested label in general. Finally, we confirm the quality of the annotated data in transfer learning experiments between different annotator groups. To facilitate further research in opinion mining on social media data, we release our collected data consisting of 200 expert and 2,785 student annotations.
How Did This Get Funded?! Automatically Identifying Quirky Scientific Achievements
Authors: Chen Shani, Nadav Borenstein, Dafna Shahaf
Published in: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) (Long Paper Track), 2021

Abstract: Humor is an important social phenomenon, serving complex social and psychological functions. However, despite being studied for millennia humor is computationally not well understood, often considered an AI-complete problem. In this work, we introduce a novel setting in humor mining: automatically detecting funny and unusual scientific papers. We are inspired by the Ig Nobel prize, a satirical prize awarded annually to celebrate funny scientific achievements (example past winner: “Are cows more likely to lie down the longer they stand?”). This challenging task has unique characteristics that make it particularly suitable for automatic learning. We construct a dataset containing thousands of funny papers and use it to learn classifiers, combining findings from psychology and linguistics with recent advances in NLP. We use our models to identify potentially funny papers in a large dataset of over 630,000 articles. The results demonstrate the potential of our methods, and more broadly the utility of integrating state-of-the-art NLP methods with insights from more traditional disciplines
Engage the Public: Poll Question Generation for Social Media Posts
Authors: Zexin Lu, Keyang Ding, Yuji Zhang, Jing Li, Baolin Peng, Lemao Liu
Published in: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) (Long Paper Track), 2021

Abstract: This paper presents a novel task to generate poll questions for social media posts. It offers an easy way to hear the voice from the public and learn from their feelings to important social topics. While most related work tackles formal languages (e.g., exam papers), we generate poll questions for short and colloquial social media messages exhibiting severe data sparsity. To deal with that, we propose to encode user comments and discover latent topics therein as contexts. They are then incorporated into a sequence-to-sequence (S2S) architecture for question generation and its extension with dual decoders to additionally yield poll choices (answers). For experiments, we collect a large-scale Chinese dataset from Sina Weibo containing over 20K polls. The results show that our model outperforms the popular S2S models without exploiting topics from comments and the dual decoder design can further benefit the prediction of both questions and answers. Human evaluations further exhibit our superiority in yielding high-quality polls helpful to draw user engagements.
HateCheck: Functional Tests for Hate Speech Detection Models
Authors: Paul Röttger, Bertie Vidgen, Dong Nguyen, Zeerak Waseem, Helen Margetts, Janet Pierrehumbert
Published in: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) (Long Paper Track), 2021

Abstract: Detecting online hate is a difficult task that even state-of-the-art models struggle with. Typically, hate speech detection models are evaluated by measuring their performance on held-out test data using metrics such as accuracy and F1 score. However, this approach makes it difficult to identify specific model weak points. It also risks overestimating generalisable model performance due to increasingly well-evidenced systematic gaps and biases in hate speech datasets. To enable more targeted diagnostic insights, we introduce HateCheck, a suite of functional tests for hate speech detection models. We specify 29 model functionalities motivated by a review of previous research and a series of interviews with civil society stakeholders. We craft test cases for each functionality and validate their quality through a structured annotation process. To illustrate HateCheck`s utility, we test near-state-of-the-art transformer models as well as two popular commercial models, revealing critical model weaknesses.
Unified Dual-view Cognitive Model for Interpretable Claim Verification
Authors: Lianwei Wu, Yuan Rao, Yuqian Lan, Ling Sun, Zhaoyin Qi
Published in: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) (Long Paper Track), 2021

Abstract: Recent studies constructing direct interactions between the claim and each single user response (a comment or a relevant article) to capture evidence have shown remarkable success in interpretable claim verification. Owing to different single responses convey different cognition of individual users (i.e., audiences), the captured evidence belongs to the perspective of individual cognition. However, individuals' cognition of social things is not always able to truly reflect the objective. There may be one-sided or biased semantics in their opinions on a claim. The captured evidence correspondingly contains some unobjective and biased evidence fragments, deteriorating task performance. In this paper, we propose a Dual-view model based on the views of Collective and Individual Cognition (CICD) for interpretable claim verification. From the view of the collective cognition, we not only capture the word-level semantics based on individual users, but also focus on sentence-level semantics (i.e., the overall responses) among all users and adjust the proportion between them to generate global evidence. From the view of individual cognition, we select the top-$k$ articles with high degree of difference and interact with the claim to explore the local key evidence fragments. To weaken the bias of individual cognition-view evidence, we devise inconsistent loss to suppress the divergence between global and local evidence for strengthening the consistent shared evidence between the both. Experiments on three benchmark datasets confirm that CICD achieves state-of-the-art performance.
DeepRapper: Neural Rap Generation with Rhyme and Rhythm Modeling
Authors: Lanqing Xue, Kaitao Song, Duocai Wu, Xu Tan, Nevin L. Zhang, Tao Qin, Wei-Qiang Zhang, Tie-Yan Liu
Published in: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) (Long Paper Track), 2021

Abstract: Rap generation, which aims to produce lyrics and corresponding singing beats, needs to model both rhymes and rhythms. Previous works for rap generation focused on rhyming lyrics, but ignored rhythmic beats, which are important for rap performance. In this paper, we develop DeepRapper, a Transformer-based rap generation system that can model both rhymes and rhythms. Since there is no available rap datasets with rhythmic beats, we develop a data mining pipeline to collect a large-scale rap dataset, which includes a large number of rap songs with aligned lyrics and rhythmic beats. Second, we design a Transformer-based autoregressive language model which carefully models rhymes and rhythms. Specifically, we generate lyrics in the reverse order with rhyme representation and constraint for rhyme enhancement, and insert a beat symbol into lyrics for rhythm/beat modeling. To our knowledge, DeepRapper is the first system to generate rap with both rhymes and rhythms. Both objective and subjective evaluations demonstrate that DeepRapper generates creative and high-quality raps with rhymes and rhythms.
PENS: A Dataset and Generic Framework for Personalized News Headline Generation
Authors: Xiang Ao, Xiting Wang, Ling Luo, Ying Qiao, Qing He, Xing Xie
Published in: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) (Long Paper Track), 2021

Abstract: In this paper, we formulate the personalized news headline generation problem whose goal is to output a user-specific title based on both a user`s reading interests and a candidate news body to be exposed to her. To build up a benchmark for this problem, we publicize a large-scale dataset named PENS (PErsonalized News headlineS). The training set is collected from user impressions logs of Microsoft News, and the test set is manually created by hundreds of native speakers to enable a fair testbed for evaluating models in an offline mode. We propose a generic framework as a preparatory solution to our problem. At its heart, user preference is learned by leveraging the user behavioral data, and three kinds of user preference injections are proposed to personalize a text generator and establish personalized headlines. We investigate our dataset by implementing several state-of-the-art user modeling methods in our framework to demonstrate a benchmark score for the proposed dataset. The dataset is available at r̆lhttps://msnews.github.io/pens.html.
Enhancing Content Preservation in Text Style Transfer Using Reverse Attention and Conditional Layer Normalization
Authors: Dongkyu Lee, Zhiliang Tian, Lanqing Xue, Nevin L. Zhang
Published in: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) (Long Paper Track), 2021

Abstract: Text style transfer aims to alter the style (e.g., sentiment) of a sentence while preserving its content. A common approach is to map a given sentence to content representation that is free of style, and the content representation is fed to a decoder with a target style. Previous methods in filtering style completely remove tokens with style at the token level, which incurs the loss of content information. In this paper, we propose to enhance content preservation by implicitly removing the style information of each token with reverse attention, and thereby retain the content. Furthermore, we fuse content information when building the target style representation, making it dynamic with respect to the content. Our method creates not only style-independent content representation, but also content-dependent style representation in transferring style. Empirical results show that our method outperforms the state-of-the-art baselines by a large margin in terms of content preservation. In addition, it is also competitive in terms of style transfer accuracy and fluency.