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20162023
most citedLERT: A Linguistically-motivated Pre-trained Language Model

26 citations · 115 across the 25 of their papers we have counts for

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49 papers · 1 filter

cs.CL2023

MetricPrompt: Prompting Model as a Relevance Metric for Few-shot Text Classification

Hongyuan Dong, Weinan Zhang, Wanxiang Che

Prompting methods have shown impressive performance in a variety of text mining tasks and applications, especially few-shot ones. Despite the promising prospects, the performance o…

cs.CL202226 cited

LERT: A Linguistically-motivated Pre-trained Language Model

Yiming Cui, Wanxiang Che, Shijin Wang +1

Pre-trained Language Model (PLM) has become a representative foundation model in the natural language processing field. Most PLMs are trained with linguistic-agnostic pre-training…

cs.CL2022

GL-CLeF: A Global-Local Contrastive Learning Framework for Cross-lingual Spoken Language Understanding

Libo Qin, Qiguang Chen, Tianbao Xie +4

Due to high data demands of current methods, attention to zero-shot cross-lingual spoken language understanding (SLU) has grown, as such approaches greatly reduce human annotation…

cs.CL20221 cited

Improving Pre-trained Language Models with Syntactic Dependency Prediction Task for Chinese Semantic Error Recognition

Bo Sun, Baoxin Wang, Wanxiang Che +3

Existing Chinese text error detection mainly focuses on spelling and simple grammatical errors. These errors have been studied extensively and are relatively simple for humans. On…

cs.CL20227 cited

UniSAr: A Unified Structure-Aware Autoregressive Language Model for Text-to-SQL

Longxu Dou, Yan Gao, Mingyang Pan +4

Existing text-to-SQL semantic parsers are typically designed for particular settings such as handling queries that span multiple tables, domains or turns which makes them ineffecti…

cs.CL2022

Inverse is Better! Fast and Accurate Prompt for Few-shot Slot Tagging

Yutai Hou, Cheng Chen, Xianzhen Luo +2

Prompting methods recently achieve impressive success in few-shot learning. These methods modify input samples with prompt sentence pieces, and decode label tokens to map samples t…