# Mitigating Noisy Inputs for Question Answering

## TL;DR

This paper explores methods to improve question answering systems' robustness against noisy inputs from sources like speech recognition, using confidence integration and forced decoding, validated on synthetic and real datasets.

## Contribution

It introduces techniques to mitigate noise effects in QA systems, including confidence integration and forced decoding, with empirical validation on large synthetic and real-world datasets.

## Key findings

- Confidence integration improves QA accuracy
- Forced decoding reduces errors from unknown words
- Models trained on synthetic data generalize to human datasets

## Abstract

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.