Mitigating Noisy Inputs for Question Answering

Mitigating Noisy Inputs for Question Answering

8 August 2019

Pedro Rodriguez

Graham Neubig

Jordan L. Boyd-Graber

Papers citing "Mitigating Noisy Inputs for Question Answering"

8 / 8 papers shown

Title
Make Every Example Count: On the Stability and Utility of Self-Influence for Learning from Noisy NLP Datasets Irina Bejan Artem Sokolov Katja Filippova TDI 58 10 0 27 Feb 2023
READIN: A Chinese Multi-Task Benchmark with Realistic and Diverse Input Noises Chenglei Si Zhengyan Zhang Yingfa Chen Xiaozhi Wang Zhiyuan Liu Maosong Sun AAML 48 1 0 14 Feb 2023
Improving the Robustness of Summarization Models by Detecting and Removing Input Noise Kundan Krishna Yao-Min Zhao Jie Jessie Ren Balaji Lakshminarayanan Jiaming Luo Mohammad Saleh Peter J. Liu 39 4 0 20 Dec 2022
Disfl-QA: A Benchmark Dataset for Understanding Disfluencies in Question Answering Aditya Gupta Jiacheng Xu Shyam Upadhyay Diyi Yang Manaal Faruqui 43 33 0 08 Jun 2021
NoiseQA: Challenge Set Evaluation for User-Centric Question Answering Abhilasha Ravichander Siddharth Dalmia Maria Ryskina Florian Metze Eduard H. Hovy A. Black ELM 36 32 0 16 Feb 2021
Contextualized Attention-based Knowledge Transfer for Spoken Conversational Question Answering Chenyu You Nuo Chen Yuexian Zou 24 36 0 21 Oct 2020
Information Seeking in the Spirit of Learning: a Dataset for Conversational Curiosity Pedro Rodriguez Paul A. Crook Seungwhan Moon Zhiguang Wang RALM 39 12 0 01 May 2020
TyDi QA: A Benchmark for Information-Seeking Question Answering in Typologically Diverse Languages J. Clark Eunsol Choi Michael Collins Dan Garrette Tom Kwiatkowski Vitaly Nikolaev J. Palomaki 66 597 0 10 Mar 2020