Chuanqi Tan

Biomedical Question Answering: A Survey of Approaches and Challenges

2021 · arXiv (Cornell University)

Automatic Question Answering (QA) has been successfully applied in various domains such as search engines and chatbots. Biomedical QA (BQA), as an emerging QA task, enables innovative applications to effectively perceive, access and understand complex …

Knowledge Rumination for Pre-trained Language Models

2023 · arXiv (Cornell University)

Previous studies have revealed that vanilla pre-trained language models (PLMs) lack the capacity to handle knowledge-intensive NLP tasks alone; thus, several works have attempted to integrate external knowledge into PLMs. However, despite the promising outcome, …

S-Net: From Answer Extraction to Answer Generation for Machine Reading Comprehension

2017 · arXiv (Cornell University)

In this paper, we present a novel approach to machine reading comprehension for the MS-MARCO dataset. Unlike the SQuAD dataset that aims to answer a question with exact text spans in a passage, the MS-MARCO …

Document-level Relation Extraction as Semantic Segmentation

2021

Document-level relation extraction aims to extract relations among multiple entity pairs from a document. Previously proposed graph-based or transformer-based models utilize the entities independently, regardless of global information among relational triples. This paper approaches the …

KnowPrompt: Knowledge-aware Prompt-tuning with Synergistic Optimization for Relation Extraction

2022 · Proceedings of the ACM Web Conference 2022

Recently, prompt-tuning has achieved promising results for specific few-shot classification tasks. The core idea of prompt-tuning is to insert text pieces (i.e., templates) into the input and transform a classification task into a masked language …