Papers with task identification

2 papers
An Ensemble-of-Experts Framework for Rehearsal-free Continual Relation Extraction (2024.findings-acl)

Copied to clipboard

Challenge: Existing methods for continual relation extraction (CRE) are rehearsal-based and need to store samples and thus may encounter privacy and security issues.
Approach: They propose an Ensemble-of-Experts framework for rehearsal-free continual relation extraction that discriminates between experts and augments analogous relations across tasks.
Outcome: The proposed method outperforms existing rehearsal-free methods and is even better than existing methods.
Lin: Unsupervised Extraction of Tasks from Textual Communication (2020.coling-main)

Copied to clipboard

Challenge: Identifying tasks from emails and chats is a hallmark of collaborative communication . state-of-the-art approaches for task identification rely on large annotated datasets .
Approach: They propose an unsupervised approach to identifying tasks that leverages dependency parsing and VerbNet.
Outcome: The proposed approach yields comparable or more accurate results than supervised models on unseen domains.

What is GenGO?

GenGO is an NLP powered publication search system. It currenctly indexes 30k+ papers from ACL Anthology, and implements multi-aspect summarization, semantic search, and more!

Information

About
Limitations