SNU_IDS at SemEval-2018 Task 12: Sentence Encoder with Contextualized Vectors for Argument Reasoning Comprehension

Taeuk Kim, Jihun Choi, Sang-goo Lee


Abstract
We present a novel neural architecture for the Argument Reasoning Comprehension task of SemEval 2018. It is a simple neural network consisting of three parts, collectively judging whether the logic built on a set of given sentences (a claim, reason, and warrant) is plausible or not. The model utilizes contextualized word vectors pre-trained on large machine translation (MT) datasets as a form of transfer learning, which can help to mitigate the lack of training data. Quantitative analysis shows that simply leveraging LSTMs trained on MT datasets outperforms several baselines and non-transferred models, achieving accuracies of about 70% on the development set and about 60% on the test set.
Anthology ID:
S18-1182
Volume:
Proceedings of the 12th International Workshop on Semantic Evaluation
Month:
June
Year:
2018
Address:
New Orleans, Louisiana
Editors:
Marianna Apidianaki, Saif M. Mohammad, Jonathan May, Ekaterina Shutova, Steven Bethard, Marine Carpuat
Venue:
SemEval
SIG:
SIGLEX
Publisher:
Association for Computational Linguistics
Note:
Pages:
1083–1088
Language:
URL:
https://aclanthology.org/S18-1182
DOI:
10.18653/v1/S18-1182
Bibkey:
Cite (ACL):
Taeuk Kim, Jihun Choi, and Sang-goo Lee. 2018. SNU_IDS at SemEval-2018 Task 12: Sentence Encoder with Contextualized Vectors for Argument Reasoning Comprehension. In Proceedings of the 12th International Workshop on Semantic Evaluation, pages 1083–1088, New Orleans, Louisiana. Association for Computational Linguistics.
Cite (Informal):
SNU_IDS at SemEval-2018 Task 12: Sentence Encoder with Contextualized Vectors for Argument Reasoning Comprehension (Kim et al., SemEval 2018)
Copy Citation:
PDF:
https://aclanthology.org/S18-1182.pdf
Code
 galsang/SemEval2018-task12
Data
SNLI