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cs.CL2023
Unifying Token and Span Level Supervisions for Few-Shot Sequence Labeling
Zifeng Cheng, Qingyu Zhou, Zhiwei Jiang +3
Few-shot sequence labeling aims to identify novel classes based on only a few labeled samples. Existing methods solve the data scarcity problem mainly by designing token-level or s…
cs.CL2023
TQ-Net: Mixed Contrastive Representation Learning For Heterogeneous Test Questions
He Zhu, Xihua Li, Xuemin Zhao +2
Recently, more and more people study online for the convenience of access to massive learning materials (e.g. test questions/notes), thus accurately understanding learning material…
cs.CL2022
DialogUSR: Complex Dialogue Utterance Splitting and Reformulation for Multiple Intent Detection
Haoran Meng, Zheng Xin, Tianyu Liu +6
While interacting with chatbots, users may elicit multiple intents in a single dialogue utterance. Instead of training a dedicated multi-intent detection model, we propose DialogUS…