Mitigating Noisy Inputs for Question Answering

By Denis Peskov, Joe Barrow, Pedro Rodriguez, Graham Neubig, Jordan L. Boyd-Graber. 2019

16 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Natural Language Processing

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Citations in the corpus

TyDi QA: A Benchmark for Information-Seeking Question Answering in Typologically Diverse Languages (2020, background): “Question answering can also be interpreted as an exercise in verifying the knowledge of experts by finding the answer to trivia questions that are carefully crafted by someone who already knows the answer such that exactly one answer is correct such as TriviaQA and Quizbowl/Jeopoardy! questions (Fer”
Can You Unpack That? Learning to Rewrite Questions-in-Context (2019, background): “When the computer cannot understand (rewrite) a question because of complicated context, missing world knowledge, or upstream errors (Peskov et al., 2019) in the course of a conversation, it should be able to ask its interlocutor,”
Disfl-QA: A Benchmark Dataset for Understanding Disfluencies in Question Answering (2021, methodology): “However, since collecting audio is challenging, another line of work involves testing the robustness of NLP models to ASR errors in transcribed texts containing synthetic noise using TTS → ASR technique (Peskov et al., 2019; Peng et al., 2020; Liu et al., 2020; Ravichander et al., 2021).”