mtybilly/Instruct2DS huggingface dataset:
Tackling Sparsity, the Achilles Heel of Social Networks: Language Model Smoothing via Social Regularization
Authors: Xiang Li, Mengwen Liu, Xiaohua Hu Rui Yan
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: Wang Ling, Isabel Trancoso, Chris Dyer, Alan W. Black, Anatole Gershman, David Martins de Matos, João Neto, Jaime Carbonell Luís Marujo
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: Farah Benamara Zitoune, Véronique Moriceau, Nathalie Aussenac-Gilles, Lamia Hadrich Belguith Jihen Karoui
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: Simone Filice, Giovanni Da San Martino, Shafiq Joty, Lluís Màrquez, Preslav Nakov, Alessandro Moschitti Alberto Barrón-Cedeño
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: Cen-Chieh Chen, Yu-Lun Hsieh, Chien Chin Chen, Wen-Lian Hsu Yung-Chun Chang
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: Diarmuid Ó Séaghdha, Blaise Thomson, ́ Milica Gaši\, Pei-Hao Su, David Vandyke, Tsung-Hsien Wen, Steve Young Nikola Mrkšić
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: Dan Moldovan, Marius Pasca, Rada Mihalcea, Mihai Surdeanu, Razvan Bunsecu, Roxana Girju, Vasile Rus, Paul Morarescu Sanda Harabagiu
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: Yangyang Guo, Yilin Niu, Feng Ji, Feng-Lin Li, Liqiang Nie Fangkai Jiao
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: Shu Guo, Bowen Yu, Qian Li, Yiming Hei, Lihong Wang, Tingwen Liu, Hongbo Xu Jiawei Sheng
Published in: Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 (Findings Track), 2021
CoDesc: A Large Code--Description Parallel Dataset
Authors: Tanveer Muttaqueen, Abdullah Al Ishtiaq, Kazi Sajeed Mehrab, Md. Mahim Anjum Haque, Tahmid Hasan, Wasi Ahmad, Anindya Iqbal, Rifat Shahriyar Masum Hasan
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: Pengcheng He, Xiaodong Liu, Yelong Shen, Jianfeng Gao, Jiawei Han, Weizhu Chen Yuning Mao
Published in: Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 (Findings Track), 2021
GLGE: A New General Language Generation Evaluation Benchmark
Authors: 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 Dayiheng Liu
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: Jianheng Tang, Jinghui Qin, Xiaodan Liang, Lingbo Liu, Eric Xing, Liang Lin Jiaqi Chen
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: Anson Bastos, Kuldeep Singh, Isaiah Onando Mulang′, Johannes Hoffart, Saeedeh Shekarpour, Vijay Saraswat Abhishek Nadgeri
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: Lucas Gris, Augusto Camargo, Daniel da Silva, Murilo Gazzola, Ester Sabino, Anna Levin, Arnaldo Candido Jr, Sandra Aluisio, Marcelo Finger Edresson Casanova
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: Mengdi Zhou, Minghui Yang, Haitao Mi, Shaosheng Cao, Zujie Wen, Teng Xu, Hongbin Wang, Lei Liu Shuang Peng
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: Zhongli Li, Qingyu Zhou, Chao Li, Zizhen Wang, Yunbo Cao, Heyan Huang, Xian-Ling Mao Heng-Da Xu
Published in: Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 (Findings Track), 2021
Hashing based Efficient Inference for Image-Text Matching
Authors: Lei Ji, Huaishao Luo, Botian Shi, Heyan Huang, Nan Duan, Xian-Ling Mao Rong-Cheng Tu
Published in: Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 (Findings Track), 2021
COM2SENSE: A Commonsense Reasoning Benchmark with Complementary Sentences
Authors: Nuan Wen, Yu Hou, Pegah Alipoormolabashi, Te-lin Wu, Xuezhe Ma, Nanyun Peng Shikhar Singh
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: Wannaphong Phatthiyaphaibun, Raheem Sarwar, Ekapol Chuangsuwanich, Sarana Nutanong Peerat Limkonchotiwat
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: Gagan Bansal, Angela Fan, Yashar Mehdad, Robin Jia, Srinivasan Iyer Ana Valeria González
Published in: Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 (Findings Track), 2021
Learning Algebraic Recombination for Compositional Generalization
Authors: Shengnan An, Zeqi Lin, Qian Liu, Bei Chen, Jian-Guang Lou, Lijie Wen, Nanning Zheng, Dongmei Zhang Chenyao Liu
Published in: Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 (Findings Track), 2021
On-the-Fly Attention Modulation for Neural Generation
Authors: Chandra Bhagavatula, Ximing Lu, Jena D. Hwang, Antoine Bosselut, Jackie Chi Kit Cheung, Yejin Choi Yue Dong
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: Xu Han, Yuzhuo Bai, Keyue Qiu, Zhiyu Xie, Yankai Lin, Zhiyuan Liu, Peng Li, Maosong Sun, Jie Zhou Tianyu Gao
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: Xiaoyue Wang, Yu Ji, Ru Li, Xiaoli Li, Zhiwei Hu, Yunxiao Zhao, Xiaoqi Han Hongye Tan
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: Mingjun Zhao, Lusheng Zhang, Di Niu, Jinwen Luo, Zhenhua Liu, Zhenyang Li, Jianbo Tang Weidong Guo
Published in: Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 (Findings Track), 2021
Detecting Hallucinated Content in Conditional Neural Sequence Generation
Authors: Graham Neubig, Jiatao Gu, Mona Diab, Francisco Guzmán, Luke Zettlemoyer, Marjan Ghazvininejad Chunting Zhou
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: Duyu Tang, Nan Duan, Zhongyu Wei, Xuanjing Huang, Jianshu Ji, Guihong Cao, Daxin Jiang, Ming Zhou Ruize Wang
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: Zhengyan Zhang, Fanchao Qi, Zhiyuan Liu, Yasheng Wang, Qun Liu, Maosong Sun Chenglei Si
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: Shengding Hu, Xin Lv, Cheng Yang, Zhiyuan Liu, Wei Xu, Jie Jiang, Juanzi Li, Maosong Sun Jie Zhou
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: Woan-Shiuan Chien, Da-Cheng Juan, Chi-Chun Lee Huang-Cheng Chou
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: Yida Xin, Henry Lieberman, Catherine Havasi, Cynthia Breazeal, Peter Chin Pedro Colon-Hernandez
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: Akifumi Wachi, Daiki Kimura, Subhajit Chaudhury, Michiaki Tatsubori, Asim Munawar Ryosuke Kohita
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: Pengfei Cao, Yubo Chen, Kang Liu, Jun Zhao, Weihua Peng, Yuguang Chen Xinyu Zuo
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: Shangwen Lv, Yingqi Qu, Jing Liu, Wayne Xin Zhao, QiaoQiao She, Hua Wu, Haifeng Wang, Ji-Rong Wen Ruiyang Ren
Published in: Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 (Findings Track), 2021
Correcting Chinese Spelling Errors with Phonetic Pre-training
Authors: Chao Pang, Chuanqiang Zhang, Shuohuan Wang, Zhongjun He, Yu Sun, Hua Wu, Haifeng Wang Ruiqing Zhang
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: Guanghao Huang, Jean Lee, Tongshu Zhang, Kunze Wang, Xinghong Guo, Siqu Long, Josiah Poon, Caren Han Henry Weld
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: Huang He, Fan Wang, Hua Wu, Haifeng Wang, Wenquan Wu, Zhen Guo, Zhibin Liu, Xinchao Xu Siqi Bao
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: Haozhe Ji, Yu Ran, Xin Cui, Liwei Wang, Linfeng Song, Xiaoyan Zhu, Minlie Huang Pei Ke
Published in: Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 (Findings Track), 2021
GEM: A General Evaluation Benchmark for Multimodal Tasks
Authors: Nan Duan, Edward Cui, Lei Ji, Chenfei Wu, Huaishao Luo, Yongfei Liu, Ming Zhong, Taroon Bharti, Arun Sacheti Lin Su
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: Xiang Ao, Ling Luo, Min Yang, Xiaofei Sun, Jiwei Li, Qing He Guoxin Yu
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: Federico Nanni, Mariona Coll Ardanuy, Kasra Hosseini, Giorgia Tolfo, Barbara McGillivray Kaspar Beelen
Published in: Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 (Findings Track), 2021
Detecting Harmful Memes and Their Targets
Authors: Dimitar Dimitrov, Rituparna Mukherjee, Shivam Sharma, Md. Shad Akhtar, Preslav Nakov, Tanmoy Chakraborty Shraman Pramanick
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: Juntao Liu, Xiaoye Qu, Jin Zhao, Jiaqing Liang, Zhefeng Wang, Baoxing Huai, Nicholas Jing Yuan, Yanghua Xiao Qiao Cheng
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: Baijun Ji, Nini Xiao, Xiangyu Duan, Min Zhang, Yangbin Shi, Weihua Luo Jinpeng Zhang
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: Made Nindyatama Nityasya, Afra Feyza Akyürek, Suci Fitriany, Alham Fikri Aji, Radityo Eko Prasojo, Derry Tanti Wijaya Haryo Akbarianto Wibowo
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: Satyapriya Krishna, Jwala Dhamala, Rahul Gupta, Kai-Wei Chang Yada Pruksachatkun
Published in: Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 (Findings Track), 2021
Multilingual Translation from Denoising Pre-Training
Authors: Chau Tran, Xian Li, Peng-Jen Chen, Naman Goyal, Vishrav Chaudhary, Jiatao Gu, Angela Fan Yuqing Tang
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: Nagesh Yadav, Gabriele Picco, Inge Vejsbjerg, Eoin Carrol, Seamus Brady, Marco Luca Sbodio, Lam Thanh Hoang, Miao Wei, John Segrave Vanessa Lopez
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: 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 Pavan Kapanipathi
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: Luowei Zhou, Zhe Gan, Yen-Chun Chen, Yuwei Fang, Siqi Sun, Yu Cheng, Jingjing Liu Shuohang Wang
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: Cass Lowry, Sujay Khandagale, Francesca Callejas, Judith Klavans, Maria Polinsky, Smaranda Muresan Ramy Eskander
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: Shuai Wang, Rishita Anubhai, Kasturi Bhattacharjee, Yaser Al-Onaizan, Smaranda Muresan Elsbeth Turcan
Published in: Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 (Findings Track), 2021
Learning Contextualized Knowledge Structures for Commonsense Reasoning
Authors: Mrigank Raman, Aaron Chan, Tianyu Zhang, Ryan Rossi, Handong Zhao, Sungchul Kim, Nedim Lipka, Xiang Ren Jun Yan
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: Shraddhan Jain, Michael Ferdman, Peter Milder, H. Andrew Schwartz, Niranjan Balasubramanian Tianchu Ji
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: Gargi Ghosh, Po-Yao Huang, Prahal Arora, Masoumeh Aminzadeh, Christoph Feichtenhofer, Florian Metze, Luke Zettlemoyer Hu Xu
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: Giai Tran, Hieu Trinh, Franck Dernoncourt, Nedim Lipka, Jose Echevarria, Thamar Solorio, Paul Asente Amirreza Shirani
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: Yuntian Zhao, Charles Welch, Jonathan K. Kummerfeld, Lawrence C An, Kenneth Resnicow, Rada Mihalcea, Verónica Pérez-Rosas Allison Lahnala
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: Akhash Amarnath, Kiran Kumar, Akash Pramod Yalla, Anandhavelu N, Niyati Chhaya, Balaji Vasan Srinivasan Aparna Garimella
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: Sourabh Kumar Bhattacharjee, Tanmoy Chakraborty, Md. Shad Akhtar Ayan Sengupta
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: Abhik Bhattacharjee, Md. Saiful Islam, Kazi Mubasshir, Yuan-Fang Li, Yong-Bin Kang, M. Sohel Rahman, Rifat Shahriyar Tahmid Hasan
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: Casimiro Pio Carrino, Carlos Rodriguez-Penagos, Ona de Gibert Bonet, Carme Armentano-Oller, Aitor Gonzalez-Agirre, Maite Melero, Marta Villegas Jordi Armengol-Estapé
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: Rishabh Kumar, Amrith Krishna, Preethi Jyothi, Ganesh Ramakrishnan, Pawan Goyal Devaraja Adiga
Published in: Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 (Findings Track), 2021
Knowledge Distillation for Quality Estimation
Authors: Marina Fomicheva, Fernando Alva-Manchego, Frédéric Blain, Abiola Obamuyide, Nikolaos Aletras, Lucia Specia Amit Gajbhiye
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: Ji-Ung Lee, Christina Viehmann, Marcus Maurer, Oliver Quiring, Iryna Gurevych Tilman Beck
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: Nadav Borenstein, Dafna Shahaf Chen Shani
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: Keyang Ding, Yuji Zhang, Jing Li, Baolin Peng, Lemao Liu Zexin Lu
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: Bertie Vidgen, Dong Nguyen, Zeerak Waseem, Helen Margetts, Janet Pierrehumbert Paul Röttger
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: Yuan Rao, Yuqian Lan, Ling Sun, Zhaoyin Qi Lianwei Wu
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: Kaitao Song, Duocai Wu, Xu Tan, Nevin L. Zhang, Tao Qin, Wei-Qiang Zhang, Tie-Yan Liu Lanqing Xue
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: Xiting Wang, Ling Luo, Ying Qiao, Qing He, Xing Xie Xiang Ao
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: Zhiliang Tian, Lanqing Xue, Nevin L. Zhang Dongkyu Lee
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.
Mention Flags (MF): Constraining Transformer-based Text Generators
Authors: Ian Wood, Stephen Wan, Mark Dras, Mark Johnson Yufei Wang
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 focuses on Seq2Seq (S2S) constrained text generation where the text generator is constrained to mention specific words which are inputs to the encoder in the generated outputs. Pre-trained S2S models or a Copy Mechanism are trained to copy the surface tokens from encoders to decoders, but they cannot guarantee constraint satisfaction. Constrained decoding algorithms always produce hypotheses satisfying all constraints. However, they are computationally expensive and can lower the generated text quality. In this paper, we propose Mention Flags (MF), which traces whether lexical constraints are satisfied in the generated outputs in an S2S decoder. The MF models can be trained to generate tokens in a hypothesis until all constraints are satisfied, guaranteeing high constraint satisfaction. Our experiments on the Common Sense Generation task (CommonGen) (Lin et al., 2020), End2end Restaurant Dialog task (E2ENLG) (Duˇsek et al., 2020) and Novel Object Captioning task (nocaps) (Agrawal et al., 2019) show that the MF models maintain higher constraint satisfaction and text quality than the baseline models and other constrained decoding algorithms, achieving state-of-the-art performance on all three tasks. These results are achieved with a much lower run-time than constrained decoding algorithms. We also show that the MF models work well in the low-resource setting.
Generalising Multilingual Concept-to-Text NLG with Language Agnostic Delexicalisation
Authors: Gerasimos Lampouras Giulio Zhou
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: Concept-to-text Natural Language Generation is the task of expressing an input meaning representation in natural language. Previous approaches in this task have been able to generalise to rare or unseen instances by relying on a delexicalisation of the input. However, this often requires that the input appears verbatim in the output text. This poses challenges in multilingual settings, where the task expands to generate the output text in multiple languages given the same input. In this paper, we explore the application of multilingual models in concept-to-text and propose Language Agnostic Delexicalisation, a novel delexicalisation method that uses multilingual pretrained embeddings, and employs a character-level post-editing model to inflect words in their correct form during relexicalisation. Our experiments across five datasets and five languages show that multilingual models outperform monolingual models in concept-to-text and that our framework outperforms previous approaches, especially in low resource conditions.
Conversations Are Not Flat: Modeling the Dynamic Information Flow across Dialogue Utterances
Authors: Jinchao Zhang, Zhengcong Fei, Yang Feng, Jie Zhou Zekang Li
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: Nowadays, open-domain dialogue models can generate acceptable responses according to the historical context based on the large-scale pre-trained language models. However, they generally concatenate the dialogue history directly as the model input to predict the response, which we named as the \textitflat pattern and ignores the dynamic information flow across dialogue utterances. In this work, we propose the \textbfDialoFlow model, in which we introduce a dynamic flow mechanism to model the context flow, and design three training objectives to capture the information dynamics across dialogue utterances by addressing the semantic influence brought about by each utterance in large-scale pre-training. Experiments on the multi-reference Reddit Dataset and DailyDialog Dataset demonstrate that our DialoFlow significantly outperforms the DialoGPT on the dialogue generation task. Besides, we propose the \textbfFlow score, an effective automatic metric for evaluating interactive human-bot conversation quality based on the pre-trained DialoFlow, which presents high chatbot-level correlation ($r=0.9$) with human ratings among 11 chatbots. Code and pre-trained models will be public.
Dual Slot Selector via Local Reliability Verification for Dialogue State Tracking
Authors: Kai Shuang, Jijie Li, Zihan Wang Jinyu Guo
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: The goal of dialogue state tracking (DST) is to predict the current dialogue state given all previous dialogue contexts. Existing approaches generally predict the dialogue state at every turn from scratch. However, the overwhelming majority of the slots in each turn should simply inherit the slot values from the previous turn. Therefore, the mechanism of treating slots equally in each turn not only is inefficient but also may lead to additional errors because of the redundant slot value generation. To address this problem, we devise the two-stage DSS-DST which consists of the Dual Slot Selector based on the current turn dialogue, and the Slot Value Generator based on the dialogue history. The Dual Slot Selector determines each slot whether to update slot value or to inherit the slot value from the previous turn from two aspects: (1) if there is a strong relationship between it and the current turn dialogue utterances; (2) if a slot value with high reliability can be obtained for it through the current turn dialogue. The slots selected to be updated are permitted to enter the Slot Value Generator to update values by a hybrid method, while the other slots directly inherit the values from the previous turn. Empirical results show that our method achieves 56.93%, 60.73%, and 58.04% joint accuracy on MultiWOZ 2.0, MultiWOZ 2.1, and MultiWOZ 2.2 datasets respectively and achieves a new state-of-the-art performance with significant improvements.
Transferable Dialogue Systems and User Simulators
Authors: Yinpei Dai, Florian Kreyssig, Bill Byrne Bo-Hsiang Tseng
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: One of the difficulties in training dialogue systems is the lack of training data. We explore the possibility of creating dialogue data through the interaction between a dialogue system and a user simulator. Our goal is to develop a modelling framework that can incorporate new dialogue scenarios through self-play between the two agents. In this framework, we first pre-train the two agents on a collection of source domain dialogues, which equips the agents to converse with each other via natural language. With further fine-tuning on a small amount of target domain data, the agents continue to interact with the aim of improving their behaviors using reinforcement learning with structured reward functions. In experiments on the MultiWOZ dataset, two practical transfer learning problems are investigated: 1) domain adaptation and 2) single-to-multiple domain transfer. We demonstrate that the proposed framework is highly effective in bootstrapping the performance of the two agents in transfer learning. We also show that our method leads to improvements in dialogue system performance on complete datasets.
BoB: BERT Over BERT for Training Persona-based Dialogue Models from Limited Personalized Data
Authors: Yan Wang, Kaiyan Zhang, Wei-Nan Zhang, Ting Liu Haoyu Song
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: Maintaining a consistent persona is essential for dialogue agents. Although tremendous advancements have been brought, the limited-scale of annotated personalized dialogue datasets is still a barrier towards training robust and consistent persona-based dialogue models. This work shows how this challenge can be addressed by disentangling persona-based dialogue generation into two sub-tasks with a novel BERT-over-BERT (BoB) model. Specifically, the model consists of a BERT-based encoder and two BERT-based decoders, where one decoder is for response generation, and another is for consistency understanding. In particular, to learn the ability of consistency understanding from large-scale non-dialogue inference data, we train the second decoder in an unlikelihood manner. Under different limited data settings, both automatic and human evaluations demonstrate that the proposed model outperforms strong baselines in response quality and persona consistency.
GL-GIN: Fast and Accurate Non-Autoregressive Model for Joint Multiple Intent Detection and Slot Filling
Authors: Fuxuan Wei, Tianbao Xie, Xiao Xu, Wanxiang Che, Ting Liu Libo Qin
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: Multi-intent SLU can handle multiple intents in an utterance, which has attracted increasing attention. However, the state-of-the-art joint models heavily rely on autoregressive approaches, resulting in two issues: slow inference speed and information leakage. In this paper, we explore a non-autoregressive model for joint multiple intent detection and slot filling, achieving more fast and accurate. Specifically, we propose a Global-Locally Graph Interaction Network (GL-GIN) where a local slot-aware graph interaction layer is proposed to model slot dependency for alleviating uncoordinated slots problem while a global intent-slot graph interaction layer is introduced to model the interaction between multiple intents and all slots in the utterance. Experimental results on two public datasets show that our framework achieves state-of-the-art performance while being 11.5 times faster.
Accelerating BERT Inference for Sequence Labeling via Early-Exit
Authors: Yunfan Shao, Tianxiang Sun, Hang Yan, Xipeng Qiu, Xuanjing Huang Xiaonan Li
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: Both performance and efficiency are crucial factors for sequence labeling tasks in many real-world scenarios. Although the pre-trained models (PTMs) have significantly improved the performance of various sequence labeling tasks, their computational cost is expensive. To alleviate this problem, we extend the recent successful early-exit mechanism to accelerate the inference of PTMs for sequence labeling tasks. However, existing early-exit mechanisms are specifically designed for sequence-level tasks, rather than sequence labeling. In this paper, we first propose a simple extension of sentence-level early-exit for sequence labeling tasks. To further reduce the computational cost, we also propose a token-level early-exit mechanism that allows partial tokens to exit early at different layers. Considering the local dependency inherent in sequence labeling, we employed a window-based criterion to decide for a token whether or not to exit. The token-level early-exit brings the gap between training and inference, so we introduce an extra self-sampling fine-tuning stage to alleviate it. The extensive experiments on three popular sequence labeling tasks show that our approach can save up to 66%\ensuremath∼75% inference cost with minimal performance degradation. Compared with competitive compressed models such as DistilBERT, our approach can achieve better performance under the same speed-up ratios of 2×, 3×, and 4×.
Modularized Interaction Network for Named Entity Recognition
Authors: Zheng Wang, Siu Cheung Hui, Lejian Liao, Dandan Song, Jing Xu, Guoxiu He, Meihuizi Jia Fei Li
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: Although the existing Named Entity Recognition (NER) models have achieved promising performance, they suffer from certain drawbacks. The sequence labeling-based NER models do not perform well in recognizing long entities as they focus only on word-level information, while the segment-based NER models which focus on processing segment instead of single word are unable to capture the word-level dependencies within the segment. Moreover, as boundary detection and type prediction may cooperate with each other for the NER task, it is also important for the two sub-tasks to mutually reinforce each other by sharing their information. In this paper, we propose a novel Modularized Interaction Network (MIN) model which utilizes both segment-level information and word-level dependencies, and incorporates an interaction mechanism to support information sharing between boundary detection and type prediction to enhance the performance for the NER task. We have conducted extensive experiments based on three NER benchmark datasets. The performance results have shown that the proposed MIN model has outperformed the current state-of-the-art models.
Capturing Event Argument Interaction via A Bi-Directional Entity-Level Recurrent Decoder
Authors: Wei Ye, Shikun Zhang, Quanxiu Wang, Huixing Jiang, Wei Wu Xi Xiangyu
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: Capturing interactions among event arguments is an essential step towards robust event argument extraction (EAE). However, existing efforts in this direction suffer from two limitations: 1) The argument role type information of contextual entities is mainly utilized as training signals, ignoring the potential merits of directly adopting it as semantically rich input features; 2) The argument-level sequential semantics, which implies the overall distribution pattern of argument roles over an event mention, is not well characterized. To tackle the above two bottlenecks, we formalize EAE as a Seq2Seq-like learning problem for the first time, where a sentence with a specific event trigger is mapped to a sequence of event argument roles. A neural architecture with a novel Bi-directional Entity-level Recurrent Decoder (BERD) is proposed to generate argument roles by incorporating contextual entities' argument role predictions, like a word-by-word text generation process, thereby distinguishing implicit argument distribution patterns within an event more accurately.