SemEval 2022 Task 12: Symlink- Linking Mathematical Symbols to their Descriptions
This work addresses the problem of efficient data management and knowledge mining for livestreaming video content creators and analysts, but it is incremental as it focuses on dataset creation and benchmarking.
The authors tackled punctuation restoration in livestreaming video transcripts by creating a new human-annotated corpus called BehancePR, and they found that existing NLP toolkits fail to detect sentence boundaries in this domain, highlighting the need for more robust models.
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 are incapable of detecting sentence boundary on non-punctuated transcripts of livestreaming videos, calling for more research effort to develop robust models for this area.