Abstract
This paper describes our system for the SemEval-2018 Task 12: Argument Reasoning Comprehension Task. We utilize skip-thought vectors, sentence-level distributional vectors inspired by the popular word embeddings and the skip-gram model. We encode preprocessed sentences from the dataset into vectors, then perform a binary supervised classification of the warrant that justifies the use of the reason as support for the claim. We explore a few variations of the model, reaching 54.1% accuracy on the test set, which placed us 16th out of 22 teams participating in the task.- Anthology ID:
- S18-1192
- Volume:
- Proceedings of the 12th International Workshop on Semantic Evaluation
- Month:
- June
- Year:
- 2018
- Address:
- New Orleans, Louisiana
- Venues:
- SemEval | *SEM
- SIGs:
- SIGLEX | SIGSEM
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1133–1136
- Language:
- URL:
- https://aclanthology.org/S18-1192
- DOI:
- 10.18653/v1/S18-1192
- Cite (ACL):
- Ana Brassard, Tin Kuculo, Filip Boltužić, and Jan Šnajder. 2018. TakeLab at SemEval-2018 Task12: Argument Reasoning Comprehension with Skip-Thought Vectors. In Proceedings of the 12th International Workshop on Semantic Evaluation, pages 1133–1136, New Orleans, Louisiana. Association for Computational Linguistics.
- Cite (Informal):
- TakeLab at SemEval-2018 Task12: Argument Reasoning Comprehension with Skip-Thought Vectors (Brassard et al., SemEval-*SEM 2018)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/S18-1192.pdf