Overview of the VLSP 2023 -- ComOM Shared Task: A Data Challenge for Comparative Opinion Mining from Vietnamese Product Reviews
This is an incremental task focused on advancing NLP techniques for comparative opinion mining in Vietnamese product reviews.
The paper introduces the Comparative Opinion Mining (ComOM) shared task at VLSP 2023, which aims to develop models for extracting comparative quintuples from Vietnamese product reviews, resulting in a dataset of 120 documents with 2,468 comparisons in 1,798 sentences.
This paper presents a comprehensive overview of the Comparative Opinion Mining from Vietnamese Product Reviews shared task (ComOM), held as part of the 10$^{th}$ International Workshop on Vietnamese Language and Speech Processing (VLSP 2023). The primary objective of this shared task is to advance the field of natural language processing by developing techniques that proficiently extract comparative opinions from Vietnamese product reviews. Participants are challenged to propose models that adeptly extract a comparative "quintuple" from a comparative sentence, encompassing Subject, Object, Aspect, Predicate, and Comparison Type Label. We construct a human-annotated dataset comprising $120$ documents, encompassing $7427$ non-comparative sentences and $2468$ comparisons within $1798$ sentences. Participating models undergo evaluation and ranking based on the Exact match macro-averaged quintuple F1 score.