1 code implementation • SemEval (NAACL) 2022 • Xinyu Lu, Mengjie Ren, Yaojie Lu, Hongyu Lin
ISCAS participated in both sub-tasks in SemEval-2022 Task 10: Structured Sentiment competition.
1 code implementation • 24 Apr 2024 • Zhuoqun Li, Hongyu Lin, Tianshu Wang, Boxi Cao, Yaojie Lu, Weixiang Zhou, Hao Wang, Zhenyu Zeng, Le Sun, Xianpei Han
Linking a claim to grounded references is a critical ability to fulfill human demands for authentic and reliable information.
1 code implementation • 25 Mar 2024 • Jiawei Chen, Hongyu Lin, Xianpei Han, Yaojie Lu, Shanshan Jiang, Bin Dong, Le Sun
Then a superposition instance retriever is applied to retrieve corresponding instances of these superposition concepts from large-scale text corpus.
1 code implementation • 14 Mar 2024 • Zhuoqun Li, Hongyu Lin, Yaojie Lu, Hao Xiang, Xianpei Han, Le Sun
Declarative knowledge and procedural knowledge are two key parts in meta-cognitive theory, and these two hold significant importance in pre-training and inference of LLMs.
no code implementations • 6 Mar 2024 • Xin Men, Mingyu Xu, Qingyu Zhang, Bingning Wang, Hongyu Lin, Yaojie Lu, Xianpei Han, WeiPeng Chen
As Large Language Models (LLMs) continue to advance in performance, their size has escalated significantly, with current LLMs containing billions or even trillions of parameters.
no code implementations • 27 Feb 2024 • Xinyu Lu, Bowen Yu, Yaojie Lu, Hongyu Lin, Haiyang Yu, Le Sun, Xianpei Han, Yongbin Li
The alignment problem in Large Language Models (LLMs) involves adapting them to the broad spectrum of human values.
no code implementations • 23 Feb 2024 • Qiaoyu Tang, Jiawei Chen, Bowen Yu, Yaojie Lu, Cheng Fu, Haiyang Yu, Hongyu Lin, Fei Huang, Ben He, Xianpei Han, Le Sun, Yongbin Li
The rise of large language models (LLMs) has transformed the role of information retrieval (IR) systems in the way to humans accessing information.
1 code implementation • 23 Feb 2024 • Xin Zheng, Qiming Zhu, Hongyu Lin, Yaojie Lu, Xianpei Han, Le Sun
In this paper, we seek to examine the capacity of present-day LLMs to comprehend and execute algorithms outlined in natural language.
no code implementations • 22 Feb 2024 • Ning Bian, Xianpei Han, Hongyu Lin, Yaojie Lu, Ben He, Le Sun
Building machines with commonsense has been a longstanding challenge in NLP due to the reporting bias of commonsense rules and the exposure bias of rule-based commonsense reasoning.
no code implementations • 22 Nov 2023 • Xinyan Guan, Yanjiang Liu, Hongyu Lin, Yaojie Lu, Ben He, Xianpei Han, Le Sun
Incorporating factual knowledge in knowledge graph is regarded as a promising approach for mitigating the hallucination of large language models (LLMs).
2 code implementations • 18 May 2023 • Jiawei Chen, Yaojie Lu, Hongyu Lin, Jie Lou, Wei Jia, Dai Dai, Hua Wu, Boxi Cao, Xianpei Han, Le Sun
M}$, and a new entity extractor can be implicitly constructed by applying new instruction and demonstrations to PLMs, i. e., $\mathcal{ (\lambda .
1 code implementation • 12 May 2023 • Jialong Tang, Hongyu Lin, Zhuoqun Li, Yaojie Lu, Xianpei Han, Le Sun
Event schema provides a conceptual, structural and formal language to represent events and model the world event knowledge.
no code implementations • 8 May 2023 • Ning Bian, Hongyu Lin, Peilin Liu, Yaojie Lu, Chunkang Zhang, Ben He, Xianpei Han, Le Sun
LLMs, as AI agents, can observe external information, which shapes their cognition and behaviors.
no code implementations • 29 Mar 2023 • Ning Bian, Xianpei Han, Le Sun, Hongyu Lin, Yaojie Lu, Ben He, Shanshan Jiang, Bin Dong
(4) Can ChatGPT effectively leverage commonsense for answering questions?
no code implementations • 9 Jan 2023 • Jie Lou, Yaojie Lu, Dai Dai, Wei Jia, Hongyu Lin, Xianpei Han, Le Sun, Hua Wu
Based on this paradigm, we propose to universally model various IE tasks with Unified Semantic Matching (USM) framework, which introduces three unified token linking operations to model the abilities of structuring and conceptualizing.
1 code implementation • ACL 2022 • Yaojie Lu, Qing Liu, Dai Dai, Xinyan Xiao, Hongyu Lin, Xianpei Han, Le Sun, Hua Wu
Information extraction suffers from its varying targets, heterogeneous structures, and demand-specific schemas.
Ranked #4 on Aspect-Based Sentiment Analysis (ABSA) on ASTE (using extra training data)
no code implementations • 15 Mar 2022 • Jialong Tang, Hongyu Lin, Meng Liao, Yaojie Lu, Xianpei Han, Le Sun, Weijian Xie, Jin Xu
In this paper, we propose a new \textbf{scene-wise} paradigm for procedural text understanding, which jointly tracks states of all entities in a scene-by-scene manner.
1 code implementation • ACL 2021 • Yaojie Lu, Hongyu Lin, Jin Xu, Xianpei Han, Jialong Tang, Annan Li, Le Sun, Meng Liao, Shaoyi Chen
Event extraction is challenging due to the complex structure of event records and the semantic gap between text and event.
Ranked #3 on Event Extraction on ACE2005
1 code implementation • ACL 2021 • Jialong Tang, Hongyu Lin, Meng Liao, Yaojie Lu, Xianpei Han, Le Sun, Weijian Xie, Jin Xu
Current event-centric knowledge graphs highly rely on explicit connectives to mine relations between events.
1 code implementation • Findings of the Association for Computational Linguistics 2020 • Jialong Tang, Yaojie Lu, Hongyu Lin, Xianpei Han, Le Sun, Xinyan Xiao, Hua Wu
One of the biggest bottlenecks in building accurate, high coverage neural open IE systems is the need for large labelled corpora.
1 code implementation • SEMEVAL 2020 • Yaojie Lu, Annan Li, Hongyu Lin, Xianpei Han, Le Sun
ISCAS participated in two subtasks of SemEval 2020 Task 5: detecting counterfactual statements and detecting antecedent and consequence.
1 code implementation • 17 Sep 2020 • Yaojie Lu, Hongyu Lin, Jialong Tang, Xianpei Han, Le Sun
Traditional event coreference systems usually rely on pipeline framework and hand-crafted features, which often face error propagation problem and have poor generalization ability.
no code implementations • EMNLP 2020 • Hongyu Lin, Yaojie Lu, Jialong Tang, Xianpei Han, Le Sun, Zhicheng Wei, Nicholas Jing Yuan
Specifically, we erase name regularity, mention coverage and context diversity respectively from the benchmarks, in order to explore their impact on the generalization ability of models.
no code implementations • IJCNLP 2019 • Jiali Zeng, Yang Liu, Jinsong Su, Yubin Ge, Yaojie Lu, Yongjing Yin, Jiebo Luo
Previous studies on the domain adaptation for neural machine translation (NMT) mainly focus on the one-pass transferring out-of-domain translation knowledge to in-domain NMT model.
no code implementations • IJCNLP 2019 • Hongyu Lin, Yaojie Lu, Xianpei Han, Le Sun, Bin Dong, Shanshan Jiang
Current region-based NER models only rely on fully-annotated training data to learn effective region encoder, which often face the training data bottleneck.
1 code implementation • ACL 2019 • Yaojie Lu, Hongyu Lin, Xianpei Han, Le Sun
Event detection systems rely on discrimination knowledge to distinguish ambiguous trigger words and generalization knowledge to detect unseen/sparse trigger words.
1 code implementation • ACL 2019 • Hongyu Lin, Yaojie Lu, Xianpei Han, Le Sun
In supervised event detection, most of the mislabeling occurs between a small number of confusing type pairs, including trigger-NIL pairs and sibling sub-types of the same coarse type.
1 code implementation • ACL 2019 • Hongyu Lin, Yaojie Lu, Xianpei Han, Le Sun
In this paper, we propose to resolve this problem by modeling and leveraging the head-driven phrase structures of entity mentions, i. e., although a mention can nest other mentions, they will not share the same head word.
Ranked #7 on Nested Mention Recognition on ACE 2005
1 code implementation • ACL 2018 • Hongyu Lin, Yaojie Lu, Xianpei Han, Le Sun
This paper focuses on detection tasks in information extraction, where positive instances are sparsely distributed and models are usually evaluated using F-measure on positive classes.
1 code implementation • ACL 2018 • Hongyu Lin, Yaojie Lu, Xianpei Han, Le Sun
Neural network based models commonly regard event detection as a word-wise classification task, which suffer from the mismatch problem between words and event triggers, especially in languages without natural word delimiters such as Chinese.
no code implementations • 16 Jan 2018 • Jinsong Su, Shan Wu, Deyi Xiong, Yaojie Lu, Xianpei Han, Biao Zhang
Partially inspired by successful applications of variational recurrent neural networks, we propose a novel variational recurrent neural machine translation (VRNMT) model in this paper.