Analysis of Punctuation Prediction Models for Automated Transcript Generation in MOOC Videos

Garg, Bhrigu; Anika, Ogemdi Chinwendu

doi:10.1109/mite.2018.8747063

Cited by 2 publications

(4 citation statements)

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“…This includes those that incorporate acoustic information (B. Garg and Anika, 2018;Moro and Szaszak, 2017;Szaszák and Tündik, 2019;Nanchen and Garner, 2019;Moró and Szaszák, 2017;Klejch et al, 2016Klejch et al, , 2017 and those that apply attention on top (Tilk and Alumäe, 2016;Oktem et al, 2017;Kim, 2019;Juin et al, 2017).…”

Section: Related Workmentioning

confidence: 99%

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Efficient Automatic Punctuation Restoration Using Bidirectional Transformers with Robust Inference

Courtland¹,

Faulkner²,

McElvain³

2020

Proceedings of the 17th International Conference on Spoken Language Translation

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Though people rarely speak in complete sentences, punctuation confers many benefits to the readers of transcribed speech. Unfortunately, most ASR systems do not produce punctuated output. To address this, we propose a solution for automatic punctuation that is both cost efficient and easy to train. Our solution benefits from the recent trend in fine-tuning transformer-based language models. We also modify the typical framing of this task by predicting punctuation for sequences rather than individual tokens, which makes for more efficient training and inference. Finally, we find that aggregating predictions across multiple context windows improves accuracy even further. Our best model achieves a new state of the art on benchmark data (TED Talks) with a combined F1 of 83.9, representing a 48.7% relative improvement (15.3 absolute) over the previous state of the art.

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Section: Related Workmentioning

confidence: 99%

“…DNNs (Yi et al, 2017;Che et al, 2016) or CNNs (B. Garg and Anika, 2018;Che et al, 2016;Żelasko et al, 2018)), which have less predictive power. The handful of approaches that make use of Transformer architectures are not bidirectional Nguyen et al, 2019;Vāravs and Salimbajevs, 2018;Wang et al, 2018).…”

Section: Related Workmentioning

confidence: 99%

Efficient Automatic Punctuation Restoration Using Bidirectional Transformers with Robust Inference

Courtland¹,

Faulkner²,

McElvain³

2020

Proceedings of the 17th International Conference on Spoken Language Translation

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show abstract

“…This includes those that incorporate acoustic information (B. Garg and Anika, 2018;Moro and Szaszak, 2017;Szaszák and Tündik, 2019;Nanchen and Garner, 2019;Moró and Szaszák, 2017;Klejch et al, 2016Klejch et al, , 2017 and those that apply attention on top Oktem et al, 2017;Kim, 2019;Juin et al, 2017).…”

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Proceedings of the 17th International Conference on Spoken Language Translation

Federico¹,

Waibel²,

Knight³

et al. 2020

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The conference chairs and organizers would like to express their gratitude to everyone who contributed and supported IWSLT. Our IWSLT-20 program exceeds all our expectations in quality and breath, particularly when considering the challenges during a pandemic under lock-downs and health and travel restrictions. We thank the challenge track chairs, organizers, and participants, the program chairs and committee members, as well as all the authors that went the extra mile to submit system and research papers to IWSLT, and make this year's conference our most vibrant than ever. We also wish to express our sincere gratitude to ACL for hosting our conference and for arranging the logistics and infrastructure that allow us to hold IWSLT 2020 as a virtual online conference.

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Analysis of Punctuation Prediction Models for Automated Transcript Generation in MOOC Videos

Cited by 2 publications

References 0 publications

Efficient Automatic Punctuation Restoration Using Bidirectional Transformers with Robust Inference

Efficient Automatic Punctuation Restoration Using Bidirectional Transformers with Robust Inference

Proceedings of the 17th International Conference on Spoken Language Translation

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