Search Results for author: Hongyu Lin

Found 38 papers, 22 papers with code

CATAMARAN: A Cross-lingual Long Text Abstractive Summarization Dataset

no code implementations LREC 2022 Zheng Chen, Hongyu Lin

Cross-lingual summarization, which produces the summary in one language from a given source document in another language, could be extremely helpful for humans to obtain information across the world.

Abstractive Text Summarization Cross-Lingual Abstractive Summarization

Learning In-context Learning for Named Entity Recognition

1 code implementation18 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 .

few-shot-ner Few-shot NER +3

DLUE: Benchmarking Document Language Understanding

no code implementations16 May 2023 Ruoxi Xu, Hongyu Lin, Xinyan Guan, Xianpei Han, Yingfei Sun, Le Sun

Understanding documents is central to many real-world tasks but remains a challenging topic.

Benchmarking Document Classification

Retentive or Forgetful? Diving into the Knowledge Memorizing Mechanism of Language Models

no code implementations16 May 2023 Boxi Cao, Qiaoyu Tang, Hongyu Lin, Xianpei Han, Jiawei Chen, Tianshu Wang, Le Sun

Memory is one of the most essential cognitive functions serving as a repository of world knowledge and episodes of activities.

Harvesting Event Schemas from Large Language Models

1 code implementation12 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.

A Drop of Ink Makes a Million Think: The Spread of False Information in Large Language Models

no code implementations8 May 2023 Ning Bian, Peilin Liu, Xianpei Han, Hongyu Lin, Yaojie Lu, Ben He, Le Sun

Large language models (LLMs) have gained increasing prominence in artificial intelligence, making a profound impact on society and various industries like business and science.

The Life Cycle of Knowledge in Big Language Models: A Survey

1 code implementation14 Mar 2023 Boxi Cao, Hongyu Lin, Xianpei Han, Le Sun

Knowledge plays a critical role in artificial intelligence.

Universal Information Extraction as Unified Semantic Matching

no code implementations9 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.

Bridging the Gap between Reality and Ideality of Entity Matching: A Revisiting and Benchmark Re-Construction

no code implementations12 May 2022 Tianshu Wang, Hongyu Lin, Cheng Fu, Xianpei Han, Le Sun, Feiyu Xiong, Hui Chen, Minlong Lu, Xiuwen Zhu

Experimental results demonstrate that the assumptions made in the previous benchmark construction process are not coincidental with the open environment, which conceal the main challenges of the task and therefore significantly overestimate the current progress of entity matching.

Entity Resolution

Few-shot Named Entity Recognition with Self-describing Networks

1 code implementation ACL 2022 Jiawei Chen, Qing Liu, Hongyu Lin, Xianpei Han, Le Sun

In this paper, we propose a self-describing mechanism for few-shot NER, which can effectively leverage illustrative instances and precisely transfer knowledge from external resources by describing both entity types and mentions using a universal concept set.

Few-shot NER Named Entity Recognition

Can Prompt Probe Pretrained Language Models? Understanding the Invisible Risks from a Causal View

1 code implementation ACL 2022 Boxi Cao, Hongyu Lin, Xianpei Han, Fangchao Liu, Le Sun

Prompt-based probing has been widely used in evaluating the abilities of pretrained language models (PLMs).

Pre-training to Match for Unified Low-shot Relation Extraction

1 code implementation ACL 2022 Fangchao Liu, Hongyu Lin, Xianpei Han, Boxi Cao, Le Sun

Low-shot relation extraction~(RE) aims to recognize novel relations with very few or even no samples, which is critical in real scenario application.

Meta-Learning Relation Extraction

Procedural Text Understanding via Scene-Wise Evolution

no code implementations15 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.

Procedural Text Understanding

Fine-grained Entity Typing via Label Reasoning

no code implementations EMNLP 2021 Qing Liu, Hongyu Lin, Xinyan Xiao, Xianpei Han, Le Sun, Hua Wu

Conventional entity typing approaches are based on independent classification paradigms, which make them difficult to recognize inter-dependent, long-tailed and fine-grained entity types.

Entity Typing

Honey or Poison? Solving the Trigger Curse in Few-shot Event Detection via Causal Intervention

1 code implementation EMNLP 2021 Jiawei Chen, Hongyu Lin, Xianpei Han, Le Sun

In this paper, we identify and solve the trigger curse problem in few-shot event detection (FSED) from a causal view.

Event Detection

Denoising Distantly Supervised Named Entity Recognition via a Hypergeometric Probabilistic Model

1 code implementation17 Jun 2021 Wenkai Zhang, Hongyu Lin, Xianpei Han, Le Sun, Huidan Liu, Zhicheng Wei, Nicholas Jing Yuan

Specifically, during neural network training, we naturally model the noise samples in each batch following a hypergeometric distribution parameterized by the noise-rate.

Denoising named-entity-recognition +2

Element Intervention for Open Relation Extraction

no code implementations ACL 2021 Fangchao Liu, Lingyong Yan, Hongyu Lin, Xianpei Han, Le Sun

Open relation extraction aims to cluster relation instances referring to the same underlying relation, which is a critical step for general relation extraction.

Relation Extraction

Knowledgeable or Educated Guess? Revisiting Language Models as Knowledge Bases

1 code implementation ACL 2021 Boxi Cao, Hongyu Lin, Xianpei Han, Le Sun, Lingyong Yan, Meng Liao, Tong Xue, Jin Xu

Previous literatures show that pre-trained masked language models (MLMs) such as BERT can achieve competitive factual knowledge extraction performance on some datasets, indicating that MLMs can potentially be a reliable knowledge source.

End-to-End Neural Event Coreference Resolution

1 code implementation17 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.

coreference-resolution Coreference Resolution +2

ISCAS at SemEval-2020 Task 5: Pre-trained Transformers for Counterfactual Statement Modeling

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.

Question Answering

A Rigorous Study on Named Entity Recognition: Can Fine-tuning Pretrained Model Lead to the Promised Land?

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.

named-entity-recognition Named Entity Recognition +1

Gazetteer-Enhanced Attentive Neural Networks for Named Entity Recognition

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.

named-entity-recognition Named Entity Recognition +1

Distilling Discrimination and Generalization Knowledge for Event Detection via Delta-Representation Learning

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.

Event Detection Representation Learning

Cost-sensitive Regularization for Label Confusion-aware Event Detection

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.

Event Detection Vocal Bursts Type Prediction

Sequence-to-Nuggets: Nested Entity Mention Detection via Anchor-Region Networks

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.

NER Nested Mention Recognition +1

Adaptive Scaling for Sparse Detection in Information Extraction

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.

Nugget Proposal Networks for Chinese Event Detection

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.

Event Detection General Classification

Reasoning with Heterogeneous Knowledge for Commonsense Machine Comprehension

no code implementations EMNLP 2017 Hongyu Lin, Le Sun, Xianpei Han

Then we propose a multi-knowledge reasoning model, which selects inference rules for a specific reasoning context using attention mechanism, and reasons by summarizing all valid inference rules.

Natural Language Understanding Reading Comprehension

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