skip to main content
10.1145/3477495.3531923acmconferencesArticle/Chapter ViewAbstractPublication PagesirConference Proceedingsconference-collections
research-article

A Dual-Expert Framework for Event Argument Extraction

Published: 07 July 2022 Publication History

Abstract

Event argument extraction (EAE) is an important information extraction task, which aims to identify the arguments of an event described in a given text and classify the roles played by them. A key characteristic in realistic EAE data is that the instance numbers of different roles follow an obvious long-tail distribution. However, the training and evaluation paradigms of existing EAE models either prone to neglect the performance on "tail roles'', or change the role instance distribution for model training to an unrealistic uniform distribution. Though some generic methods can alleviate the class imbalance in long-tail datasets, they usually sacrifice the performance of "head classes'' as a trade-off. To address the above issues, we propose to train our model on realistic long-tail EAE datasets, and evaluate the average performance over all roles. Inspired by the Mixture of Experts (MOE), we propose a Routing-Balanced Dual Expert Framework (RBDEF), which divides all roles into "head" and "tail" two scopes and assigns the classifications of head and tail roles to two separate experts. In inference, each encoded instance will be allocated to one of the two experts by a routing mechanism. To reduce routing errors caused by the imbalance of role instances, we design a Balanced Routing Mechanism (BRM), which transfers several head roles to the tail expert to balance the load of routing, and employs a tri-filter routing strategy to reduce the misallocation of the tail expert's instances. To enable an effective learning of tail roles with scarce instances, we devise Target-Specialized Meta Learning (TSML) to train the tail expert. Different from other meta learning algorithms that only search a generic parameter initialization equally applying to infinite tasks, TSML can adaptively adjust its search path to obtain a specialized initialization for the tail expert, thereby expanding the benefits to the learning of tail roles. In experiments, RBDEF significantly outperforms the state-of-the-art EAE models and advanced methods for long-tail data.

Supplementary Material

MP4 File (SIGIR22-fp0717.mp4)
Event argument Extraction (EAE) aims to identify the arguments of an event described in a given text and classify the roles played by them, whose role instance numbers follow a long-tail distribution in realistic datasets. In order to improve the prediction performance of tail roles without sacrificing that of head roles, we propose a Routing Balanced Dual-Expert Framework (RBDEF), which leverages two independent expert modules to predict head and tail roles respectively. To accurately assign each instance to the corresponding expert, we devise a Balanced Routing Mechanism. To enable an effective learning of tail roles with scarce instances, we design a Target-Specialized Meta Learning algorithm to train the tail expert. In experiments, RBDEF significantly outperforms the state-of-the-art EAE models and advanced generic methods for long-tail data.

References

[1]
David Ahn. 2006. The stages of event extraction. In Proceedings of the Workshop on Annotating and Reasoning about Time and Events.
[2]
Farhad Akhbardeh, Cecilia Ovesdotter Alm, Marcos Zampieri, and Travis Desell. 2021. Handling Extreme Class Imbalance in Technical Logbook Datasets. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, ACL/IJCNLP 2021, (Volume 1: Long Papers), Virtual Event, August 1--6, 2021, Chengqing Zong, Fei Xia, Wenjie Li, and Roberto Navigli (Eds.). Association for Computational Linguistics, 4034--4045.
[3]
Sakyajit Bhattacharya, Vaibhav Rajan, and Harsh Shrivastava. 2017. ICU Mortality Prediction: A Classification Algorithm for Imbalanced Datasets. In Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, February 4--9, 2017, San Francisco, California, USA, Satinder P. Singh and Shaul Markovitch (Eds.). AAAI Press, 1288--1294.
[4]
Yee Seng Chan, Joshua Fasching, Haoling Qiu, and Bonan Min. 2019. Rapid Customization for Event Extraction. In Proceedings of the 57th Conference of the Association for Computational Linguistics, ACL 2019, Florence, Italy, July 28 - August 2, 2019, Volume 3: System Demonstrations, Marta R. Costa-jussà and Enrique Alfonseca (Eds.). Association for Computational Linguistics, 31--36.
[5]
Shubham Chatterjee and Laura Dietz. 2021. Entity Retrieval Using Fine-Grained Entity Aspects. In SIGIR '21: The 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, Virtual Event, Canada, July 11--15, 2021, Fernando Diaz, Chirag Shah, Torsten Suel, Pablo Castells, Rosie Jones, and Tetsuya Sakai (Eds.). ACM, 1662--1666.
[6]
Jiayi Chen and Aidong Zhang. 2021. HetMAML: Task-Heterogeneous ModelAgnostic Meta-Learning for Few-Shot Learning Across Modalities. In CIKM '21: The 30th ACM International Conference on Information and Knowledge Management, Virtual Event, Queensland, Australia, November 1 - 5, 2021.
[7]
Yubo Chen, Liheng Xu, Kang Liu, Daojian Zeng, and Jun Zhao. 2015. Event Extraction via Dynamic Multi-Pooling Convolutional Neural Networks. In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing of the Asian Federation of Natural Language Processing, ACL 2015, July 26--31, 2015, Beijing, China, Volume 1: Long Papers. The Association for Computer Linguistics, 167--176.
[8]
Ronan Collobert, Samy Bengio, and Yoshua Bengio. 2001. A Parallel Mixture of SVMs for Very Large Scale Problems. In Advances in Neural Information Processing Systems 14 [Neural Information Processing Systems: Natural and Synthetic, NIPS 2001, December 3--8, 2001, Vancouver, British Columbia, Canada], Thomas G. Dietterich, Suzanna Becker, and Zoubin Ghahramani (Eds.). MIT Press, 633--640.
[9]
Yin Cui, Menglin Jia, Tsung-Yi Lin, Yang Song, and Serge J. Belongie. 2019. ClassBalanced Loss Based on Effective Number of Samples. In IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2019, Long Beach, CA, USA, June 16--20, 2019. Computer Vision Foundation / IEEE, 9268--9277.
[10]
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018).
[11]
Xinya Du and Claire Cardie. 2020. Event Extraction by Answering (Almost) Natural Questions. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, EMNLP 2020, Online, November 16--20, 2020, Bonnie Webber, Trevor Cohn, Yulan He, and Yang Liu (Eds.). Association for Computational Linguistics, 671--683.
[12]
Linhui Feng, Linbo Qiao, Yi Han, Zhigang Kan, Yifu Gao, and Dongsheng Li. 2021. Syntactic Enhanced Projection Network for Few-Shot Chinese Event Extraction. In International Conference on Knowledge Science, Engineering and Management.
[13]
Rui Feng, Jie Yuan, and Chao Zhang. 2020. Probing and fine-tuning reading comprehension models for few-shot event extraction. arXiv preprint arXiv:2010.11325 (2020).
[14]
Chelsea Finn, Pieter Abbeel, and Sergey Levine. 2017. Model-Agnostic MetaLearning for Fast Adaptation of Deep Networks. In Proceedings of the 34th International Conference on Machine Learning, ICML 2017, Sydney, NSW, Australia, 6--11 August 2017 (Proceedings of Machine Learning Research, Vol. 70), Doina Precup and Yee Whye Teh (Eds.). PMLR, 1126--1135.
[15]
Sam Gross, Marc'Aurelio Ranzato, and Arthur Szlam. 2017. Hard Mixtures of Experts for Large Scale Weakly Supervised Vision. In 2017 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, Honolulu, HI, USA, July 21--26, 2017. IEEE Computer Society, 5085--5093.
[16]
Jiatao Gu, Yong Wang, Yun Chen, Victor O. K. Li, and Kyunghyun Cho. 2018. Meta-Learning for Low-Resource Neural Machine Translation. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, Brussels, Belgium, October 31 - November 4, 2018, Ellen Riloff, David Chiang, Julia Hockenmaier, and Jun'ichi Tsujii (Eds.). Association for Computational Linguistics, 3622--3631.
[17]
Sepp Hochreiter and Jürgen Schmidhuber. 1997. Long Short-Term Memory. Neural Computation 9 (1997), 1735--1780.
[18]
Po-Sen Huang, Chenglong Wang, Rishabh Singh, Wen-tau Yih, and Xiaodong He. 2018. Natural Language to Structured Query Generation via Meta-Learning. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers). 732--738.
[19]
Yi Huang, Buse Giledereli, Abdullatif Köksal, Arzucan Özgür, and Elif Ozkirimli. 2021. Balancing Methods for Multi-label Text Classification with Long-Tailed Class Distribution. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, EMNLP 2021, Virtual Event / Punta Cana, Dominican Republic, 7--11 November, 2021, Marie-Francine Moens, Xuanjing Huang, Lucia Specia, and Scott Wen-tau Yih (Eds.). Association for Computational Linguistics, 8153--8161.
[20]
Robert A. Jacobs, Michael I. Jordan, Steven J. Nowlan, and Geoffrey E. Hinton. 1991. Adaptive Mixtures of Local Experts. Neural Comput. 3, 1 (1991), 79--87.
[21]
Shoaib Jameel, Zied Bouraoui, and Steven Schockaert. 2017. MEmbER: MaxMargin Based Embeddings for Entity Retrieval. In Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, Shinjuku, Tokyo, Japan, August 7--11, 2017, Noriko Kando, Tetsuya Sakai, Hideo Joho, Hang Li, Arjen P. de Vries, and Ryen W. White (Eds.). ACM, 783--792.
[22]
Taewon Jeong and Heeyoung Kim. 2020. OOD-MAML: Meta-Learning for FewShot Out-of-Distribution Detection and Classification. In Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020, December 6--12, 2020, virtual.
[23]
Heng Ji and Ralph Grishman. 2008. Refining Event Extraction through CrossDocument Inference. In ACL 2008, Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics, June 15--20, 2008, Columbus, Ohio, USA, Kathleen R. McKeown, Johanna D. Moore, Simone Teufel, James Allan, and Sadaoki Furui (Eds.). The Association for Computer Linguistics, 254--262.
[24]
Zhiyi Jiang, Jianliang Gao, and Xinqi Lv. 2021. MetaP: Meta Pattern Learning for One-Shot Knowledge Graph Completion. In SIGIR '21: The 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, Virtual Event, Canada, July 11--15, 2021, Fernando Diaz, Chirag Shah, Torsten Suel, Pablo Castells, Rosie Jones, and Tetsuya Sakai (Eds.). ACM, 2232--2236.
[25]
Bingyi Kang, Saining Xie, Marcus Rohrbach, Zhicheng Yan, Albert Gordo, Jiashi Feng, and Yannis Kalantidis. 2020. Decoupling Representation and Classifier for Long-Tailed Recognition. In 8th International Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, April 26--30, 2020. OpenReview.net.
[26]
Siavash Khodadadeh, Ladislau Bölöni, and Mubarak Shah. 2019. Unsupervised Meta-Learning for Few-Shot Image Classification. In Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, NeurIPS 2019, December 8--14, 2019, Vancouver, BC, Canada, Hanna M. Wallach, Hugo Larochelle, Alina Beygelzimer, Florence d'Alché-Buc, Emily B. Fox, and Roman Garnett (Eds.). 10132--10142.
[27]
Viet Dac Lai, Minh Van Nguyen, Thien Huu Nguyen, and Franck Dernoncourt. 2021. Graph Learning Regularization and Transfer Learning for Few-Shot Event Detection. In SIGIR '21: The 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, Virtual Event, Canada, July 11--15, 2021, Fernando Diaz, Chirag Shah, Torsten Suel, Pablo Castells, Rosie Jones, and Tetsuya Sakai (Eds.). ACM, 2172--2176.
[28]
Dingcheng Li, Xu Li, Jun Wang, and Ping Li. 2020. Video Recommendation with Multi-gate Mixture of Experts Soft Actor Critic. In Proceedings of the 43rd International ACM SIGIR conference on research and development in Information Retrieval, SIGIR 2020, Virtual Event, China, July 25--30, 2020, Jimmy Huang, Yi Chang, Xueqi Cheng, Jaap Kamps, Vanessa Murdock, Ji-Rong Wen, and Yiqun Liu (Eds.). ACM, 1553--1556.
[29]
Jing Li, Shuo Shang, and Ling Shao. 2020. Metaner: Named entity recognition with meta-learning. In Proceedings of The Web Conference 2020. 429--440.
[30]
Rui Li, Wenlin Zhao, Cheng Yang, and Sen Su. 2021. Treasures Outside Contexts: Improving Event Detection via Global Statistics. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, EMNLP 2021, Virtual Event / Punta Cana, Dominican Republic, 7--11 November, 2021, Marie-Francine Moens, Xuanjing Huang, Lucia Specia, and Scott Wen-tau Yih (Eds.). Association for Computational Linguistics, 2625--2635.
[31]
Xiaoya Li, Xiaofei Sun, Yuxian Meng, Junjun Liang, Fei Wu, and Jiwei Li. 2020. Dice Loss for Data-imbalanced NLP Tasks. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, ACL 2020, Online, July 5--10, 2020, Dan Jurafsky, Joyce Chai, Natalie Schluter, and Joel R. Tetreault (Eds.). Association for Computational Linguistics, 465--476.
[32]
Jinzhi Liao, Xiang Zhao, Xinyi Li, Lingling Zhang, and Jiuyang Tang. 2021. Learning Discriminative Neural Representations for Event Detection. In SIGIR '21: The 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, Virtual Event, Canada, July 11--15, 2021, Fernando Diaz, Chirag Shah, Torsten Suel, Pablo Castells, Rosie Jones, and Tetsuya Sakai (Eds.). ACM, 644--653.
[33]
Jian Liu, Yubo Chen, Kang Liu, Wei Bi, and Xiaojiang Liu. 2020. Event Extraction as Machine Reading Comprehension. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online.
[34]
Yaojie Lu, Hongyu Lin, Jin Xu, Xianpei Han, Jialong Tang, Annan Li, Le Sun, Meng Liao, and Shaoyi Chen. 2021. Text2Event: Controllable Sequence-to-Structure Generation for End-to-end Event Extraction. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, ACL/IJCNLP 2021, (Volume 1: Long Papers), Virtual Event, August 1--6, 2021, Chengqing Zong, Fei Xia, Wenjie Li, and Roberto Navigli (Eds.). Association for Computational Linguistics, 2795-- 2806.
[35]
Jie Ma, Shuai Wang, Rishita Anubhai, Miguel Ballesteros, and Yaser Al-Onaizan. 2020. Resource-Enhanced Neural Model for Event Argument Extraction. In Findings of the Association for Computational Linguistics: EMNLP 2020, Online Event, 16--20 November 2020 (Findings of ACL, Vol. EMNLP 2020), Trevor Cohn, Yulan He, and Yang Liu (Eds.). Association for Computational Linguistics, 3554-- 3559.
[36]
Guoshun Nan, Jiaqi Zeng, Rui Qiao, Zhijiang Guo, and Wei Lu. 2021. Uncovering Main Causalities for Long-tailed Information Extraction. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, EMNLP 2021, Virtual Event / Punta Cana, Dominican Republic, 7--11 November, 2021, MarieFrancine Moens, Xuanjing Huang, Lucia Specia, and Scott Wen-tau Yih (Eds.). Association for Computational Linguistics, 9683--9695.
[37]
Tarun Naren, Yuanda Zhu, and May Dongmei Wang. 2021. COVID-19 diagnosis using model agnostic meta-learning on limited chest X-ray images. In Proceedings of the 12th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics. 1--9.
[38]
Toru Nishino, Ryota Ozaki, Yohei Momoki, Tomoki Taniguchi, Ryuji Kano, Norihisa Nakano, Yuki Tagawa, Motoki Taniguchi, Tomoko Ohkuma, and Keigo Nakamura. 2020. Reinforcement Learning with Imbalanced Dataset for Data-toText Medical Report Generation. In Findings of the Association for Computational Linguistics: EMNLP 2020, Online Event, 16--20 November 2020 (Findings of ACL, Vol. EMNLP 2020), Trevor Cohn, Yulan He, and Yang Liu (Eds.). Association for Computational Linguistics, 2223--2236.
[39]
Aniruddh Raghu, Maithra Raghu, Samy Bengio, and Oriol Vinyals. 2020. Rapid Learning or Feature Reuse? Towards Understanding the Effectiveness of MAML. In International Conference on Learning Representations. https://openreview.net/ forum?id=rkgMkCEtPB
[40]
Babak Shahbaba and Radford M. Neal. 2009. Nonlinear Models Using Dirichlet Process Mixtures. J. Mach. Learn. Res. 10 (2009), 1829--1850.
[41]
Noam Shazeer, Azalia Mirhoseini, Krzysztof Maziarz, Andy Davis, Quoc V. Le, Geoffrey E. Hinton, and Jeff Dean. 2017. Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer. In 5th International Conference on Learning Representations, ICLR 2017, Toulon, France, April 24--26, 2017, Conference Track Proceedings. OpenReview.net.
[42]
Shivashankar Subramanian, Afshin Rahimi, Timothy Baldwin, Trevor Cohn, and Lea Frermann. 2021. Fairness-aware Class Imbalanced Learning. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, EMNLP 2021, Virtual Event / Punta Cana, Dominican Republic, 7--11 November, 2021, MarieFrancine Moens, Xuanjing Huang, Lucia Specia, and Scott Wen-tau Yih (Eds.). Association for Computational Linguistics, 2045--2051.
[43]
Kaihua Tang, Jianqiang Huang, and Hanwang Zhang. 2020. Long-Tailed Classification by Keeping the Good and Removing the Bad Momentum Causal Effect. In Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020, December 6--12, 2020, virtual, Hugo Larochelle, Marc'Aurelio Ranzato, Raia Hadsell, Maria-Florina Balcan, and Hsuan-Tien Lin (Eds.).
[44]
Kaihua Tang, Yulei Niu, Jianqiang Huang, Jiaxin Shi, and Hanwang Zhang. 2020. Unbiased Scene Graph Generation From Biased Training. In 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2020, Seattle, WA, USA, June 13--19, 2020. Computer Vision Foundation / IEEE, 3713--3722.
[45]
Volker Tresp. 2000. Mixtures of Gaussian Processes. In Advances in Neural Information Processing Systems 13, Papers from Neural Information Processing Systems (NIPS) 2000, Denver, CO, USA, Todd K. Leen, Thomas G. Dietterich, and Volker Tresp (Eds.). MIT Press, 654--660.
[46]
Amir Pouran Ben Veyseh, Tuan Ngo Nguyen, and Thien Huu Nguyen. 2020. Graph Transformer Networks with Syntactic and Semantic Structures for Event Argument Extraction. In Findings of the Association for Computational Linguistics: EMNLP 2020, Online Event, 16--20 November 2020 (Findings of ACL, Vol. EMNLP 2020), Trevor Cohn, Yulan He, and Yang Liu (Eds.). Association for Computational Linguistics, 3651--3661.
[47]
Xiaozhi Wang, Shengyu Jia, Xu Han, Zhiyuan Liu, Juanzi Li, Peng Li, and Jie Zhou. 2020. Neural Gibbs Sampling for Joint Event Argument Extraction. In Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing, AACL/IJCNLP 2020, Suzhou, China, December 4--7, 2020, Kam-Fai Wong, Kevin Knight, and Hua Wu (Eds.). Association for Computational Linguistics, 169--180.
[48]
Xiaozhi Wang, Ziqi Wang, Xu Han, Zhiyuan Liu, Juanzi Li, Peng Li, Maosong Sun, Jie Zhou, and Xiang Ren. 2019. HMEAE: Hierarchical modular event argument extraction. In EMNLP-IJCNLP.
[49]
Fei Wu, Xiao-Yuan Jing, Shiguang Shan, Wangmeng Zuo, and Jing-Yu Yang. 2017. Multiset Feature Learning for Highly Imbalanced Data Classification. In Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, February 4--9, 2017, San Francisco, California, USA, Satinder P. Singh and Shaul Markovitch (Eds.). AAAI Press, 1583--1589.
[50]
Xiangyu Xi, Wei Ye, Shikun Zhang, Quanxiu Wang, Huixing Jiang, and Wei Wu. 2021. Capturing Event Argument Interaction via A Bi-Directional Entity-Level Recurrent Decoder. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, ACL/IJCNLP 2021, (Volume 1: Long Papers), Virtual Event, August 1--6, 2021, Chengqing Zong, Fei Xia, Wenjie Li, and Roberto Navigli (Eds.). Association for Computational Linguistics, 210--219.
[51]
Haifeng Xia, Zengmao Wang, Bo Du, Lefei Zhang, Shuai Chen, and Gang Chun. 2019. Leveraging Ratings and Reviews with Gating Mechanism for Recommendation. In Proceedings of the 28th ACM International Conference on Information and Knowledge Management, CIKM 2019, Beijing, China, November 3--7, 2019, Wenwu Zhu, Dacheng Tao, Xueqi Cheng, Peng Cui, Elke A. Rundensteiner, David Carmel, Qi He, and Jeffrey Xu Yu (Eds.). ACM, 1573--1582. https://doi.org/10.1145/3357384.3357919
[52]
Lin Xiao, Xiangliang Zhang, Liping Jing, Chi Huang, and Mingyang Song. 2021. Does Head Label Help for Long-Tailed Multi-Label Text Classification. In ThirtyFifth AAAI Conference on Artificial Intelligence, AAAI 2021, Thirty-Third Conference on Innovative Applications of Artificial Intelligence, IAAI 2021, The Eleventh Symposium on Educational Advances in Artificial Intelligence, EAAI 2021, Virtual Event, February 2--9, 2021. AAAI Press, 14103--14111.
[53]
Hang Yang, Yubo Chen, Kang Liu, and Jun Zhao. 2020. Meta Learning for Event Argument Extraction via Domain-Specific Information Enhanced. In China Conference on Knowledge Graph and Semantic Computing.
[54]
Sen Yang, Dawei Feng, Linbo Qiao, Zhigang Kan, and Dongsheng Li. 2019. Exploring Pre-trained Language Models for Event Extraction and Generation. In Proceedings of the 57th Conference of the Association for Computational Linguistics, ACL 2019, Florence, Italy, July 28- August 2, 2019, Volume 1: Long Papers, Anna Korhonen, David R. Traum, and Lluís Màrquez (Eds.). Association for Computational Linguistics, 5284--5294.
[55]
Ying Zeng, Yansong Feng, Rong Ma, Zheng Wang, Rui Yan, Chongde Shi, and Dongyan Zhao. 2018. Scale Up Event Extraction Learning via Automatic Training Data Generation. In Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence (AAAI-18), Sheila A. McIlraith and Kilian Q. Weinberger (Eds.). AAAI Press, 6045--6052.
[56]
Shaolei Zhang and Yang Feng. 2021. Universal Simultaneous Machine Translation with Mixture-of-Experts Wait-k Policy. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, EMNLP 2021, Virtual Event / Punta Cana, Dominican Republic, 7--11 November, 2021, Marie-Francine Moens, Xuanjing Huang, Lucia Specia, and Scott Wen-tau Yih (Eds.). Association for Computational Linguistics, 7306--7317.
[57]
Boyan Zhou, Quan Cui, Xiu-Shen Wei, and Zhao-Min Chen. 2020. BBN: BilateralBranch Network With Cumulative Learning for Long-Tailed Visual Recognition. In 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2020, Seattle, WA, USA, June 13--19, 2020. Computer Vision Foundation / IEEE, 9716--9725.
[58]
Fan Zhou, Chengtai Cao, Kunpeng Zhang, Goce Trajcevski, Ting Zhong, and Ji Geng. 2019. Meta-gnn: On few-shot node classification in graph meta-learning. In Proceedings of the 28th ACM International Conference on Information and Knowledge Management. 2357--2360.
[59]
Yang Zhou, Yubo Chen, Jun Zhao, Yin Wu, Jiexin Xu, and JinLong Li. 2021. What the Role is vs. What Plays the Role: Semi-Supervised Event Argument Extraction via Dual Question Answering. In Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI 2021). AAAI Press, 14638--14646.
[60]
Ziwei Zhu, Shahin Sefati, Parsa Saadatpanah, and James Caverlee. 2020. Recommendation for New Users and New Items via Randomized Training and Mixture-of-Experts Transformation. In Proceedings of the 43rd International ACM SIGIR conference on research and development in Information Retrieval, SIGIR 2020, Virtual Event, China, July 25--30, 2020, Jimmy Huang, Yi Chang, Xueqi Cheng, Jaap Kamps, Vanessa Murdock, Ji-Rong Wen, and Yiqun Liu (Eds.). ACM, 1121--1130.

Cited By

View all
  • (2024)Token-Event-Role Structure-Based Multi-Channel Document-Level Event ExtractionACM Transactions on Information Systems10.1145/364388542:4(1-27)Online publication date: 22-Mar-2024
  • (2023)Transcription between human-readable synthetic descriptions and machine-executable instructions: an application of the latest pre-training technologyChemical Science10.1039/D3SC02483K14:35(9360-9373)Online publication date: 2023

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences
SIGIR '22: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval
July 2022
3569 pages
ISBN:9781450387323
DOI:10.1145/3477495
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 July 2022

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. event argument extraction
  2. information extraction
  3. meta learning
  4. mixture of experts

Qualifiers

  • Research-article

Funding Sources

Conference

SIGIR '22
Sponsor:

Acceptance Rates

Overall Acceptance Rate 792 of 3,983 submissions, 20%

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)30
  • Downloads (Last 6 weeks)3
Reflects downloads up to 28 Feb 2025

Other Metrics

Citations

Cited By

View all
  • (2024)Token-Event-Role Structure-Based Multi-Channel Document-Level Event ExtractionACM Transactions on Information Systems10.1145/364388542:4(1-27)Online publication date: 22-Mar-2024
  • (2023)Transcription between human-readable synthetic descriptions and machine-executable instructions: an application of the latest pre-training technologyChemical Science10.1039/D3SC02483K14:35(9360-9373)Online publication date: 2023

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media