Learning from Multiple Noisy Augmented Data Sets for Better
Cross-Lingual Spoken Language Understanding

Learning from Multiple Noisy Augmented Data Sets for Better Cross-Lingual Spoken Language Understanding

3 September 2021

Zhiyong Wu

Papers citing "Learning from Multiple Noisy Augmented Data Sets for Better Cross-Lingual Spoken Language Understanding"

6 / 6 papers shown

Title
UniPSDA: Unsupervised Pseudo Semantic Data Augmentation for Zero-Shot Cross-Lingual Natural Language Understanding Dongyang Li Taolin Zhang Jiali Deng Longtao Huang Chengyu Wang Xiaofeng He Hui Xue 34 1 0 24 Jun 2024
Optimal Transport Posterior Alignment for Cross-lingual Semantic Parsing Tom Sherborne Tom Hosking Mirella Lapata OT 24 4 0 09 Jul 2023
PSSAT: A Perturbed Semantic Structure Awareness Transferring Method for Perturbation-Robust Slot Filling Guanting Dong Daichi Guo Liwen Wang Xuefeng Li Zechen Wang ... Hao Lei Xinyue Cui Yi Huang Junlan Feng Weiran Xu 21 12 0 24 Aug 2022
Call Larisa Ivanovna: Code-Switching Fools Multilingual NLU Models Alexey Birshert Ekaterina Artemova 40 2 0 29 Sep 2021
Unsupervised Cross-lingual Adaptation for Sequence Tagging and Beyond Xin Li Lidong Bing Wenxuan Zhang Zheng Li Wai Lam 50 25 0 23 Oct 2020
Data Augmentation using Pre-trained Transformer Models Varun Kumar Ashutosh Choudhary Eunah Cho VLM 216 315 0 04 Mar 2020