DUAL: Discrete Spoken Unit Adaptive Learning for Textless Spoken Question Answering

V. Panayotov, G. Chen, D. Povey, and S. Khudanpur, “Librispeech: An asr corpus based on public domain audio books,” in 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) , 2015, pp. 5206–5210

2015

Earlier work this paper cites.

B.-H. Tseng, S. syun Shen, H. yi Lee, and L.-S. Lee, “Towards machine comprehension of spoken content: Initial toefl listening comprehension test by machine,” in INTERSPEECH , 2016

2016

Earlier work this paper cites.

P. Rajpurkar, J. Zhang, K. Lopyrev, and P. Liang, “SQuAD: 100,000+ questions for machine comprehension of text,” in Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing . Austin, Texas: Association for Computational Linguistics, Nov. 2016, pp. 2383–2392. [Online]. Available: https://aclanthology.org/D16-1264

2016

Earlier work this paper cites.

M. McAuliffe, M. Socolof, S. Mihuc, M. Wagner, and M. Sonderegger, “Montreal forced aligner: Trainable text-speech alignment using kaldi,” in INTERSPEECH , 2017

2017

Earlier work this paper cites.

C.-H. Lee, S.-L. Wu, C.-L. Liu, and H. yi Lee, “Spoken squad: A study of mitigating the impact of speech recognition errors on listening comprehension,” in INTERSPEECH , 2018

2018

Earlier work this paper cites.

C.-H. Lee, S.-M. Wang, H.-C. Chang, and H. yi Lee, “Odsqa: Open-domain spoken question answering dataset,” 2018 IEEE Spoken Language Technology Workshop (SLT) , pp. 949–956, 2018

2018

Earlier work this paper cites.

C.-H. Lee, Y.-N. Chen, and H.-Y. Lee, “Mitigating the impact of speech recognition errors on spoken question answering by adversarial domain adaptation,” in ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2019, pp. 7300–7304

2019

Earlier work this paper cites.

J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, “Bert: Pre-training of deep bidirectional transformers for language understanding,” in Proceedings of NAACL-HLT , 2019, pp. 4171–4186

2019

Earlier work this paper cites.

D. Su and P. Fung, “Improving spoken question answering using contextualized word representation,” in ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2020, pp. 8004–8008

2020

Earlier work this paper cites.

Y.-S. Chuang, C.-L. Liu, H. yi Lee, and L.-S. Lee, “Speechbert: An audio-and-text jointly learned language model for end-to-end spoken question answering,” in INTERSPEECH , 2020

2020

Earlier work this paper cites.

I. Papadimitriou and D. Jurafsky, “Learning Music Helps You Read: Using transfer to study linguistic structure in language models,” in Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) . Online: Association for Computational Linguistics, Nov. 2020, pp. 6829–6839. [Online]. Available: https://aclanthology.org/2020.emnlp-main.554

2020

Earlier work this paper cites.

C. You, N. Chen, F. Liu, D. Yang, and Y. Zou, “Towards data distillation for end-to-end spoken conversational question answering,” ArXiv , vol. abs/2010.08923, 2020

Original

2020

Cited alongside, same era.

I. Beltagy, M. E. Peters, and A. Cohan, “Longformer: The long-document transformer,” arXiv preprint arXiv:2004.05150 , 2020

Original

2020

Cited alongside, same era.

A. Baevski, Y. Zhou, A. Mohamed, and M. Auli, “wav2vec 2.0: A framework for self-supervised learning of speech representations,” in Advances in Neural Information Processing Systems , H. Larochelle, M. Ranzato, R. Hadsell, M. F. Balcan, and H. Lin, Eds., vol. 33. Curran Associates, Inc., 2020, pp. 12 449–12 460

2020

Cited alongside, same era.

C. You, N. Chen, and Y. Zou, “Knowledge distillation for improved accuracy in spoken question answering,” in ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2021, pp. 7793–7797

A. Lee, H. Gong, P.-A. Duquenne, H. Schwenk, P.-J. Chen, C. Wang, S. Popuri, J. Pino, J. Gu, and W.-N. Hsu, “Textless speech-to-speech translation on real data,” arXiv preprint arXiv:2112.08352 , 2021

Original

2021

Later among the works it cites.

W.-T. Kao and H.-y. Lee, “Is BERT a cross-disciplinary knowledge learner? a surprising finding of pre-trained models’ transferability,” in Findings of the Association for Computational Linguistics: EMNLP 2021 . Punta Cana, Dominican Republic: Association for Computational Linguistics, Nov. 2021, pp. 2195–2208. [Online]. Available: https://aclanthology.org/2021.findings-emnlp.189

2021

Later among the works it cites.

C.-H. Chiang and H.-y. Lee, “On the transferability of pre-trained language models: A study from artificial datasets,” arXiv preprint arXiv:2109.03537 , 2021

Original

2021

Later among the works it cites.

K. Lu, A. Grover, P. Abbeel, and I. Mordatch, “Pretrained transformers as universal computation engines,” arXiv preprint arXiv:2103.05247 , 2021