most citedFine-grained Contrastive Learning for Definition Generation

2 citations · 2 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CL2023

Multi-level Contrastive Learning for Script-based Character Understanding

Dawei Li, Hengyuan Zhang, Yanran Li +1

In this work, we tackle the scenario of understanding characters in scripts, which aims to learn the characters' personalities and identities from their utterances. We begin by ana…

cs.CL2023

Assisting Language Learners: Automated Trans-Lingual Definition Generation via Contrastive Prompt Learning

Hengyuan Zhang, Dawei Li, Yanran Li +3

The standard definition generation task requires to automatically produce mono-lingual definitions (e.g., English definitions for English words), but ignores that the generated def…

cs.CL2022★ 2 cited

Fine-grained Contrastive Learning for Definition Generation

Hengyuan Zhang, Dawei Li, Shiping Yang +1

Recently, pre-trained transformer-based models have achieved great success in the task of definition generation (DG). However, previous encoder-decoder models lack effective repres…

cs.CL2022

BLCU-ICALL at SemEval-2022 Task 1: Cross-Attention Multitasking Framework for Definition Modeling

Cunliang Kong, Yujie Wang, Ruining Chong +4

This paper describes the BLCU-ICALL system used in the SemEval-2022 Task 1 Comparing Dictionaries and Word Embeddings, the Definition Modeling subtrack, achieving 1st on Italian, 2…

cs.CL2022

Multitasking Framework for Unsupervised Simple Definition Generation

Cunliang Kong, Yun Chen, Hengyuan Zhang +2

The definition generation task can help language learners by providing explanations for unfamiliar words. This task has attracted much attention in recent years. We propose a novel…