ODSQA: Open-Domain Spoken Question Answering Dataset

Lee, Chia‐Hsuan; Wang, Shang-Ming; Chang, Huan‐Cheng; Lee, Hung-yi

doi:10.1109/slt.2018.8639505

Cited by 31 publications

(21 citation statements)

References 38 publications

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“…The authors further propose subword unit sequence embedding based mitigation strategies. This work was further extended to the ODSQA dataset (Lee et al, 2018a), where the question is also given in speech et al, 2020), where the authors explore Spoken Conversational Question Answering (Spoken-CoQA). They used both speech and transcript in their feature vector embedding.…”

Section: Related Workmentioning

confidence: 99%

SD-QA: Spoken Dialectal Question Answering for the Real World

Faisal¹,

Keshava²,

Alam³

et al. 2021

Findings of the Association for Computational Linguistics: EMNLP 2021

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Question answering (QA) systems are now available through numerous commercial applications for a wide variety of domains, serving millions of users that interact with them via speech interfaces. However, current benchmarks in QA research do not account for the errors that speech recognition models might introduce, nor do they consider the language variations (dialects) of the users. To address this gap, we augment an existing QA dataset to construct a multi-dialect, spoken QA benchmark on five languages (Arabic, Bengali, English, Kiswahili, Korean) with more than 68k audio prompts in 24 dialects from 255 speakers. We provide baseline results showcasing the real-world performance of QA systems and analyze the effect of language variety and other sensitive speaker attributes on downstream performance. Last, we study the fairness of the ASR and QA models with respect to the underlying user populations. 1

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Section: Related Workmentioning

confidence: 99%

SD-QA: Spoken Dialectal Question Answering for the Real World

Faisal¹,

Keshava²,

Alam³

et al. 2021

Findings of the Association for Computational Linguistics: EMNLP 2021

View full text Add to dashboard Cite

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“…Recently, there has been a significant increase in the construction of extractive MRC datasets with formal written texts such as SQuAD [2], CNN/Daily Mail [1], CBT [28], NewsQA [29], TriviaQA [31], WIKIHOP [32], DRCD [37], and CMRC2018 [38]. There are also datasets of which reading texts are spoken language, such as ODSQA [33] and Spoken SQuAD [34] and conversation-based datasets [30], [35]. • In contrast to extractive MRC, abstractive MRC requires computers to generate answers or synthetic summaries because answers to such questions in abstractive MRC are usually not spans in the reading text.…”

Section: A Mrc Datasetsmentioning

confidence: 99%

Enhancing Lexical-Based Approach With External Knowledge for Vietnamese Multiple-Choice Machine Reading Comprehension

et al. 2020

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Although Vietnamese is the 17 th most popular native-speaker language a in the world, there are not many research studies on Vietnamese machine reading comprehension (MRC), the task of understanding a text and answering questions about it. One of the reasons is because of the lack of high-quality benchmark datasets for this task. In this work, we construct a dataset which consists of 2,783 pairs of multiple-choice questions and answers based on 417 Vietnamese texts which are commonly used for teaching reading comprehension for elementary school pupils. In addition, we propose a lexicalbased MRC method that utilizes semantic similarity measures and external knowledge sources to analyze questions and extract answers from the given text. We compare the performance of the proposed model with several baseline lexical-based and neural network-based models. Our proposed method achieves 61.81% by accuracy, which is 5.51% higher than the best baseline model. We also measure human performance on our dataset and find that there is a big gap between machine-model and human performances. This indicates that significant progress can be made on this task. The dataset is freely available on our website b for research purposes.

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“…Intentional noise has been added to machine translation data [9,10]. Alternate methods for collecting large scale audio data include Generative Adversarial Networks [11] and manual recording [12].…”

Section: Spoken Question Answering Datasetsmentioning

confidence: 99%

Mitigating Noisy Inputs for Question Answering

et al. 2019

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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. 1

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ODSQA: Open-Domain Spoken Question Answering Dataset

Cited by 31 publications

References 38 publications

SD-QA: Spoken Dialectal Question Answering for the Real World

SD-QA: Spoken Dialectal Question Answering for the Real World

Enhancing Lexical-Based Approach With External Knowledge for Vietnamese Multiple-Choice Machine Reading Comprehension

Mitigating Noisy Inputs for Question Answering

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