Punctuation marks in automatically generated texts such as translated or transcribed ones may be displaced erroneously for several reasons. Detecting the end of a sentence and placing an appropriate punctuation mark improves the quality of such texts not only by preserving the original meaning but also by enhancing their readability.
The goal of the shared task is to build models for identifying the end of a sentence by detecting an appropriate position for putting an appropriate punctuation mark. Specifically, we offer the following subtasks:
- Subtask 1 (fully unpunctuated sentences-full stop detection): Given the textual content of an utterance where the full stops are fully removed, correctly detect the end of sentences by placing a full stop in appropriate positions.
- Subtask 2 (fully unpunctuated sentences- full punctuation marks): Given the textual content of an utterance where all punctuation marks are fully removed, correctly predict all punctuation marks.
Participants may choose to attend in one or both of the subtasks.
For further information, visit the official website.
Organizers
- Don Tuggener, ZHAW, InIT (tuge@zhaw.ch)
- Ahmad Aghaebrahimian, ZHAW, InIT (agha@zhaw.ch)