Cross-lingual Intermediate Fine-tuning improves Dialogue State Tracking

28 September 2021

Papers citing "Cross-lingual Intermediate Fine-tuning improves Dialogue State Tracking"

4 / 4 papers shown

Title
MULTI3NLU++: A Multilingual, Multi-Intent, Multi-Domain Dataset for Natural Language Understanding in Task-Oriented Dialogue Nikita Moghe E. Razumovskaia Liane Guillou Ivan Vulić Anna Korhonen Alexandra Birch 19 13 0 20 Dec 2022
Improving In-Context Few-Shot Learning via Self-Supervised Training Mingda Chen Jingfei Du Ramakanth Pasunuru Todor Mihaylov Srini Iyer Ves Stoyanov Zornitsa Kozareva SSL AI4MH 24 63 0 03 May 2022
XQA-DST: Multi-Domain and Multi-Lingual Dialogue State Tracking Han Zhou Ignacio Iacobacci Pasquale Minervini 15 2 0 12 Apr 2022
Pre-trained Models for Natural Language Processing: A Survey Xipeng Qiu Tianxiang Sun Yige Xu Yunfan Shao Ning Dai Xuanjing Huang LM&MA VLM 235 1,444 0 18 Mar 2020