BehancePR: A Punctuation Restoration Dataset for Livestreaming Video Transcript
Viet Lai, Amir Pouran Ben Veyseh, Franck Dernoncourt, Thien Nguyen
Abstract
Given the increasing number of livestreaming videos, automatic speech recognition and post-processing for livestreaming video transcripts are crucial for efficient data management as well as knowledge mining. A key step in this process is punctuation restoration which restores fundamental text structures such as phrase and sentence boundaries from the video transcripts. This work presents a new human-annotated corpus, called BehancePR, for punctuation restoration in livestreaming video transcripts. Our experiments on BehancePR demonstrate the challenges of punctuation restoration for this domain. Furthermore, we show that popular natural language processing toolkits like Stanford Stanza, Spacy, and Trankit underperform on detecting sentence boundary on non-punctuated transcripts of livestreaming videos. The dataset is publicly accessible at http://github.com/nlp-uoregon/behancepr.
BibTeX
@inproceedings{lai-etal-2022-behancepr,
title = "{B}ehance{PR}: A Punctuation Restoration Dataset for Livestreaming Video Transcript",
author = "Lai, Viet and
Pouran Ben Veyseh, Amir and
Dernoncourt, Franck and
Nguyen, Thien",
editor = "Carpuat, Marine and
de Marneffe, Marie-Catherine and
Meza Ruiz, Ivan Vladimir",
booktitle = "Findings of the Association for Computational Linguistics: NAACL 2022",
month = jul,
year = "2022",
address = "Seattle, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.findings-naacl.149/",
doi = "10.18653/v1/2022.findings-naacl.149",
pages = "1943--1951"
}