# Mitigating Noisy Inputs for Question Answering

arXiv: https://arxiv.org/abs/1908.02914 · HTML: https://mlsanity.com/paper/1908.02914/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Aug 2019.

## Built on by (most cited first, 6 of 6)

- [NoiseQA: Challenge Set Evaluation for User-Centric Question Answering](https://mlsanity.com/paper/2102.08345/) (2021)
- [Disfl-QA: A Benchmark Dataset for Understanding Disfluencies in Question Answering](https://mlsanity.com/paper/2106.04016/) (2021)
- [Make Every Example Count: On the Stability and Utility of Self-Influence for Learning from Noisy NLP Datasets](https://mlsanity.com/paper/2302.13959/) (2023)
- [Improving the Robustness of Summarization Models by Detecting and Removing Input Noise](https://mlsanity.com/paper/2212.09928/) (2022)
- [READIN: A Chinese Multi-Task Benchmark with Realistic and Diverse Input Noises](https://mlsanity.com/paper/2302.07324/) (2023)
- [Evaluating Robustness of Dialogue Summarization Models in the Presence of Naturally Occurring Variations](https://mlsanity.com/paper/2311.08705/) (2023)

## Abstract (arXiv)

Natural language processing systems are often downstream of unreliable inputs: machine translation, optical character recognition, or speech recognition. For instance, virtual assistants can only answer your questions after understanding your speech. We investigate and mitigate the effects of noise from Automatic Speech Recognition systems on two factoid Question Answering (QA) tasks. Integrating confidences into the model and forced decoding of unknown words are empirically shown to improve the accuracy of downstream neural QA systems. We create and train models on a synthetic corpus of over 500,000 noisy sentences and evaluate on two human corpora from Quizbowl and Jeopardy! competitions.