Qingyu Tan | DeepAI

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∙ 06/16/2023

Class-Adaptive Self-Training for Relation Extraction with Incompletely Annotated Training Data

Relation extraction (RE) aims to extract relations from sentences and do...

0 Qingyu Tan, et al. ∙

∙ 06/15/2023

Towards Benchmarking and Improving the Temporal Reasoning Capability of Large Language Models

Reasoning about time is of fundamental importance. Many facts are time-d...

0 Qingyu Tan, et al. ∙

∙ 05/24/2023

Unlocking Temporal Question Answering for Large Language Models Using Code Execution

Large language models (LLMs) have made significant progress in natural l...

0 Xingxuan Li, et al. ∙

∙ 05/25/2022

Revisiting DocRED – Addressing the Overlooked False Negative Problem in Relation Extraction

The DocRED dataset is one of the most popular and widely used benchmarks...

0 Qingyu Tan, et al. ∙

∙ 03/21/2022

Document-Level Relation Extraction with Adaptive Focal Loss and Knowledge Distillation

Document-level Relation Extraction (DocRE) is a more challenging task co...

0 Qingyu Tan, et al. ∙

∙ 06/06/2021

On the Effectiveness of Adapter-based Tuning for Pretrained Language Model Adaptation

Adapter-based tuning has recently arisen as an alternative to fine-tunin...

0 Ruidan He, et al. ∙

∙ 09/24/2020

Feature Adaptation of Pre-Trained Language Models across Languages and Domains for Text Classification

Adapting pre-trained language models (PrLMs) (e.g., BERT) to new domains...

0 Hai Ye, et al. ∙

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