no code implementations • EMNLP 2021 • Tao Zhang, Congying Xia, Philip S. Yu, Zhiwei Liu, Shu Zhao
Cross-domain Named Entity Recognition (NER) transfers the NER knowledge from high-resource domains to the low-resource target domain.
no code implementations • 29 Oct 2024 • Renze Lou, Hanzi Xu, Sijia Wang, Jiangshu Du, Ryo Kamoi, Xiaoxin Lu, Jian Xie, Yuxuan Sun, Yusen Zhang, Jihyun Janice Ahn, Hongchao Fang, Zhuoyang Zou, Wenchao Ma, Xi Li, Kai Zhang, Congying Xia, Lifu Huang, Wenpeng Yin
Numerous studies have assessed the proficiency of AI systems, particularly large language models (LLMs), in facilitating everyday tasks such as email writing, question answering, and creative content generation.
no code implementations • 3 Oct 2024 • Xiangyu Peng, Congying Xia, Xinyi Yang, Caiming Xiong, Chien-Sheng Wu, Chen Xing
We show that ReGenesis achieves superior performance on all in-domain and OOD settings tested compared to existing methods.
no code implementations • 22 Aug 2024 • Can Qin, Congying Xia, Krithika Ramakrishnan, Michael Ryoo, Lifu Tu, Yihao Feng, Manli Shu, Honglu Zhou, Anas Awadalla, Jun Wang, Senthil Purushwalkam, Le Xue, Yingbo Zhou, Huan Wang, Silvio Savarese, Juan Carlos Niebles, Zeyuan Chen, ran Xu, Caiming Xiong
We present xGen-VideoSyn-1, a text-to-video (T2V) generation model capable of producing realistic scenes from textual descriptions.
1 code implementation • 24 Jun 2024 • Jiangshu Du, Yibo Wang, Wenting Zhao, Zhongfen Deng, Shuaiqi Liu, Renze Lou, Henry Peng Zou, Pranav Narayanan Venkit, Nan Zhang, Mukund Srinath, Haoran Ranran Zhang, Vipul Gupta, Yinghui Li, Tao Li, Fei Wang, Qin Liu, Tianlin Liu, Pengzhi Gao, Congying Xia, Chen Xing, Jiayang Cheng, Zhaowei Wang, Ying Su, Raj Sanjay Shah, Ruohao Guo, Jing Gu, Haoran Li, Kangda Wei, ZiHao Wang, Lu Cheng, Surangika Ranathunga, Meng Fang, Jie Fu, Fei Liu, Ruihong Huang, Eduardo Blanco, Yixin Cao, Rui Zhang, Philip S. Yu, Wenpeng Yin
This study focuses on the topic of LLMs assist NLP Researchers, particularly examining the effectiveness of LLM in assisting paper (meta-)reviewing and its recognizability.
1 code implementation • 28 Feb 2024 • Congying Xia, Chen Xing, Jiangshu Du, Xinyi Yang, Yihao Feng, ran Xu, Wenpeng Yin, Caiming Xiong
This paper presents FoFo, a pioneering benchmark for evaluating large language models' (LLMs) ability to follow complex, domain-specific formats, a crucial yet underexamined capability for their application as AI agents.
1 code implementation • 7 Sep 2023 • Erik Nijkamp, Tian Xie, Hiroaki Hayashi, Bo Pang, Congying Xia, Chen Xing, Jesse Vig, Semih Yavuz, Philippe Laban, Ben Krause, Senthil Purushwalkam, Tong Niu, Wojciech Kryściński, Lidiya Murakhovs'ka, Prafulla Kumar Choubey, Alex Fabbri, Ye Liu, Rui Meng, Lifu Tu, Meghana Bhat, Chien-Sheng Wu, Silvio Savarese, Yingbo Zhou, Shafiq Joty, Caiming Xiong
Most open-source LLMs, on the other hand, are limited in their ability to support longer sequence lengths, which is a key requirement for many tasks that require inference over an input context.
no code implementations • 7 Sep 2023 • Jiangshu Du, Congying Xia, Wenpeng Yin, TingTing Liang, Philip S. Yu
In intent detection tasks, leveraging meaningful semantic information from intent labels can be particularly beneficial for few-shot scenarios.
2 code implementations • NeurIPS 2023 • Shentao Yang, Shujian Zhang, Congying Xia, Yihao Feng, Caiming Xiong, Mingyuan Zhou
Aligning language models (LMs) with preferences is an important problem in natural language generation.
1 code implementation • 1 Dec 2022 • Jiangshu Du, Wenpeng Yin, Congying Xia, Philip S. Yu
To deal with the two issues, this work first proposes a contextualized TE model (Context-TE) by appending other k options as the context of the current (P, H) modeling.
1 code implementation • 4 Nov 2022 • Yibo Wang, Congying Xia, Guan Wang, Philip Yu
In order to handle new entities in product titles and address the special language styles problem of product titles in e-commerce domain, we propose our textual entailment model with continuous prompt tuning based hypotheses and fusion embeddings for e-commerce entity typing.
no code implementations • 1 Apr 2022 • TingTing Liang, Yixuan Jiang, Congying Xia, Ziqiang Zhao, Yuyu Yin, Philip S. Yu
Recently, conversational OpenQA is proposed to address these issues with the abundant contextual information in the conversation.
1 code implementation • 13 Oct 2021 • Jiangshu Du, Yingtong Dou, Congying Xia, Limeng Cui, Jing Ma, Philip S. Yu
The COVID-19 pandemic poses a great threat to global public health.
1 code implementation • EMNLP 2021 • Ye Liu, Jian-Guo Zhang, Yao Wan, Congying Xia, Lifang He, Philip S. Yu
To capture the semantic graph structure from raw text, most existing summarization approaches are built on GNNs with a pre-trained model.
2 code implementations • EMNLP 2021 • JianGuo Zhang, Trung Bui, Seunghyun Yoon, Xiang Chen, Zhiwei Liu, Congying Xia, Quan Hung Tran, Walter Chang, Philip Yu
In this work, we focus on a more challenging few-shot intent detection scenario where many intents are fine-grained and semantically similar.
no code implementations • 3 May 2021 • Congying Xia, Caiming Xiong, Philip Yu
PSN consists of two identical subnetworks with the same structure but different weights: an action network and an object network.
2 code implementations • 25 Apr 2021 • Yingtong Dou, Kai Shu, Congying Xia, Philip S. Yu, Lichao Sun
The majority of existing fake news detection algorithms focus on mining news content and/or the surrounding exogenous context for discovering deceptive signals; while the endogenous preference of a user when he/she decides to spread a piece of fake news or not is ignored.
Ranked #1 on
Graph Classification
on UPFD-GOS
1 code implementation • NAACL 2021 • Congying Xia, Wenpeng Yin, Yihao Feng, Philip Yu
Two major challenges exist in this new task: (i) For the learning process, the system should incrementally learn new classes round by round without re-training on the examples of preceding classes; (ii) For the performance, the system should perform well on new classes without much loss on preceding classes.
1 code implementation • COLING 2020 • Zhongfen Deng, Hao Peng, Congying Xia, JianXin Li, Lifang He, Philip S. Yu
Review rating prediction of text reviews is a rapidly growing technology with a wide range of applications in natural language processing.
1 code implementation • Findings of the Association for Computational Linguistics 2020 • Hoang Nguyen, Chenwei Zhang, Congying Xia, Philip S. Yu
Although recent works demonstrate that multi-level matching plays an important role in transferring learned knowledge from seen training classes to novel testing classes, they rely on a static similarity measure and overly fine-grained matching components.
no code implementations • COLING 2020 • Lichao Sun, Congying Xia, Wenpeng Yin, TingTing Liang, Philip S. Yu, Lifang He
Our studies show that mixup is a domain-independent data augmentation technique to pre-trained language models, resulting in significant performance improvement for transformer-based models.
no code implementations • Findings of the Association for Computational Linguistics 2020 • Congying Xia, Caiming Xiong, Philip Yu, Richard Socher
In this paper, we focus on generating training examples for few-shot intents in the realistic imbalanced scenario.
2 code implementations • 2 Aug 2020 • Qian Li, Hao Peng, Jian-Xin Li, Congying Xia, Renyu Yang, Lichao Sun, Philip S. Yu, Lifang He
The last decade has seen a surge of research in this area due to the unprecedented success of deep learning.
no code implementations • 23 May 2020 • Ting-Ting Liang, Congying Xia, Yuyu Yin, Philip S. Yu
This paper proposes a novel neural network, joint training capsule network (JTCN), for the cold start recommendation task.
no code implementations • 4 Apr 2020 • Congying Xia, Chenwei Zhang, Hoang Nguyen, Jiawei Zhang, Philip Yu
In this paper, we formulate a more realistic and difficult problem setup for the intent detection task in natural language understanding, namely Generalized Few-Shot Intent Detection (GFSID).
no code implementations • COLING 2020 • Tao Zhang, Congying Xia, Chun-Ta Lu, Philip Yu
Named entity typing (NET) is a classification task of assigning an entity mention in the context with given semantic types.
2 code implementations • 15 Jan 2020 • Jiawei Zhang, Haopeng Zhang, Congying Xia, Li Sun
We have tested the effectiveness of GRAPH-BERT on several graph benchmark datasets.
Ranked #30 on
Node Classification
on Cora
1 code implementation • ACL 2019 • Congying Xia, Chenwei Zhang, Tao Yang, Yaliang Li, Nan Du, Xian Wu, Wei Fan, Fenglong Ma, Philip Yu
This paper presents a novel framework, MGNER, for Multi-Grained Named Entity Recognition where multiple entities or entity mentions in a sentence could be non-overlapping or totally nested.
Ranked #5 on
Nested Mention Recognition
on ACE 2005
Multi-Grained Named Entity Recognition
named-entity-recognition
+5
no code implementations • 27 Sep 2018 • Congying Xia, Chenwei Zhang, Tao Yang, Yaliang Li, Nan Du, Xian Wu, Wei Fan, Fenglong Ma, Philip S. Yu
In this paper, we focus on a new Named Entity Recognition (NER) task, i. e., the Multi-grained NER task.
no code implementations • 11 Sep 2018 • Lichao Sun, Lifang He, Zhipeng Huang, Bokai Cao, Congying Xia, Xiaokai Wei, Philip S. Yu
Meta-graph is currently the most powerful tool for similarity search on heterogeneous information networks, where a meta-graph is a composition of meta-paths that captures the complex structural information.
4 code implementations • EMNLP 2018 • Congying Xia, Chenwei Zhang, Xiaohui Yan, Yi Chang, Philip S. Yu
User intent detection plays a critical role in question-answering and dialog systems.
no code implementations • 26 Nov 2017 • Jiawei Zhang, Congying Xia, Chenwei Zhang, Limeng Cui, Yanjie Fu, Philip S. Yu
The closeness among users in the networks are defined as the meta proximity scores, which will be fed into DIME to learn the embedding vectors of users in the emerging network.
Social and Information Networks Databases