Yankai Lin
17 papers in the PaperMetrix corpus
Papers by this author
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Disentangle-based Continual Graph Representation Learning
2020 · arXiv (Cornell University)
Graph embedding (GE) methods embed nodes (and/or edges) in graph into a low-dimensional semantic space, and have shown its effectiveness in modeling multi-relational data. However, existing GE models are not practical in real-world applications since …
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Learning from Context or Names? An Empirical Study on Neural Relation Extraction
2020
Neural models have achieved remarkable success on relation extraction (RE) benchmarks. However, there is no clear understanding which type of information affects existing RE models to make decisions and how to further improve the performance …
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MAVEN: A Massive General Domain Event Detection Dataset
2020
Xiaozhi Wang, Ziqi Wang, Xu Han, Wangyi Jiang, Rong Han, Zhiyuan Liu, Juanzi Li, Peng Li, Yankai Lin, Jie Zhou. Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). 2020.
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MAVEN-ERE: A Unified Large-scale Dataset for Event Coreference, Temporal, Causal, and Subevent Relation Extraction
2022 · arXiv (Cornell University)
The diverse relationships among real-world events, including coreference, temporal, causal, and subevent relations, are fundamental to understanding natural languages. However, two drawbacks of existing datasets limit event relation extraction (ERE) tasks: (1) Small scale. Due …
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Plug-and-Play Knowledge Injection for Pre-trained Language Models
2023 · arXiv (Cornell University)
Injecting external knowledge can improve the performance of pre-trained language models (PLMs) on various downstream NLP tasks. However, massive retraining is required to deploy new knowledge injection methods or knowledge bases for downstream tasks. In …
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Exploring Mode Connectivity for Pre-trained Language Models
2022
Recent years have witnessed the prevalent application of pre-trained language models (PLMs) in NLP. From the perspective of parameter space, PLMs provide generic initialization, starting from which high-performance minima could be found. Although plenty of …
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Enhancing Open-Domain Task-Solving Capability of LLMs via Autonomous Tool Integration from GitHub
2023 · arXiv (Cornell University)
Large Language Models (LLMs) excel in traditional natural language processing tasks but struggle with problems that require complex domain-specific calculations or simulations. While equipping LLMs with external tools to build LLM-based agents can enhance their …
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UniMem: Towards a Unified View of Long-Context Large Language Models
2024 · arXiv (Cornell University)
Long-context processing is a critical ability that constrains the applicability of large language models (LLMs). Although there exist various methods devoted to enhancing the long-context processing ability of LLMs, they are developed in an isolated …
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ICLEval: Evaluating In-Context Learning Ability of Large Language Models
2024 · arXiv (Cornell University)
In-Context Learning (ICL) is a critical capability of Large Language Models (LLMs) as it empowers them to comprehend and reason across interconnected inputs. Evaluating the ICL ability of LLMs can enhance their utilization and deepen …
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YuLan: An Open-source Large Language Model
2024 · arXiv (Cornell University)
Large language models (LLMs) have become the foundation of many applications, leveraging their extensive capabilities in processing and understanding natural language. While many open-source LLMs have been released with technical reports, the lack of training …
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Modeling Relation Paths for Representation Learning of Knowledge Bases
2015
Representation learning of knowledge bases aims to embed both entities and relations into a low-dimensional space. Most existing methods only consider direct relations in representation learning. We argue that multiple-step relation paths also contain rich …
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Neural Relation Extraction with Selective Attention over Instances
2016
Distant supervised relation extraction has been widely used to find novel relational facts from text. However, distant supervision inevitably accompanies with the wrong labelling problem, and these noisy data will substantially hurt the performance of …
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Neural Relation Extraction with Multi-lingual Attention
2017
Relation extraction has been widely used for finding unknown relational facts from the plain text. Most existing methods focus on exploiting mono-lingual data for relation extraction, ignoring massive information from the texts in various languages. …
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Denoising Distantly Supervised Open-Domain Question Answering
2018
Distantly supervised open-domain question answering (DS-QA) aims to find answers in collections of unlabeled text. Existing DS-QA models usually retrieve related paragraphs from a large-scale corpus and apply reading comprehension technique to extract answers from …
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DocRED: A Large-Scale Document-Level Relation Extraction Dataset
2019
Yuan Yao, Deming Ye, Peng Li, Xu Han, Yankai Lin, Zhenghao Liu, Zhiyuan Liu, Lixin Huang, Jie Zhou, Maosong Sun. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. 2019.
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Incorporating Relation Paths in Neural Relation Extraction
2017
Distantly supervised relation extraction has been widely used to find novel relational facts from plain text. To predict the relation between a pair of two target entities, existing methods solely rely on those direct sentences …