69 citations · 236 across the 40 of their papers we have counts for
28 papers · 1 filter
Retrieval-style In-Context Learning for Few-shot Hierarchical Text Classification
Huiyao Chen, Yu Zhao, Zulong Chen +4
Hierarchical text classification (HTC) is an important task with broad applications, while few-shot HTC has gained increasing interest recently. While in-context learning (ICL) wit…
On the Hallucination in Simultaneous Machine Translation
Meizhi Zhong, Kehai Chen, Zhengshan Xue +3
It is widely known that hallucination is a critical issue in Simultaneous Machine Translation (SiMT) due to the absence of source-side information. While many efforts have been mad…
Chinese Sequence Labeling with Semi-Supervised Boundary-Aware Language Model Pre-training
Longhui Zhang, Dingkun Long, Meishan Zhang +3
Chinese sequence labeling tasks are heavily reliant on accurate word boundary demarcation. Although current pre-trained language models (PLMs) have achieved substantial gains on th…
In-Context Learning for Few-Shot Nested Named Entity Recognition
Meishan Zhang, Bin Wang, Hao Fei +1
In nested Named entity recognition (NER), entities are nested with each other, and thus requiring more data annotations to address. This leads to the development of few-shot nested…
How Well Do Large Language Models Understand Syntax? An Evaluation by Asking Natural Language Questions
Houquan Zhou, Yang Hou, Zhenghua Li +4
While recent advancements in large language models (LLMs) bring us closer to achieving artificial general intelligence, the question persists: Do LLMs truly understand language, or…
Context Consistency between Training and Testing in Simultaneous Machine Translation
Meizhi Zhong, Lemao Liu, Kehai Chen +2
Simultaneous Machine Translation (SiMT) aims to yield a real-time partial translation with a monotonically growing the source-side context. However, there is a counterintuitive phe…