@inproceedings{kim-etal-2018-snu,
title = "{SNU}{\_}{IDS} at {S}em{E}val-2018 Task 12: Sentence Encoder with Contextualized Vectors for Argument Reasoning Comprehension",
author = "Kim, Taeuk and
Choi, Jihun and
Lee, Sang-goo",
editor = "Apidianaki, Marianna and
Mohammad, Saif M. and
May, Jonathan and
Shutova, Ekaterina and
Bethard, Steven and
Carpuat, Marine",
booktitle = "Proceedings of the 12th International Workshop on Semantic Evaluation",
month = jun,
year = "2018",
address = "New Orleans, Louisiana",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/S18-1182",
doi = "10.18653/v1/S18-1182",
pages = "1083--1088",
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.",
}
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%0 Conference Proceedings
%T SNU_IDS at SemEval-2018 Task 12: Sentence Encoder with Contextualized Vectors for Argument Reasoning Comprehension
%A Kim, Taeuk
%A Choi, Jihun
%A Lee, Sang-goo
%Y Apidianaki, Marianna
%Y Mohammad, Saif M.
%Y May, Jonathan
%Y Shutova, Ekaterina
%Y Bethard, Steven
%Y Carpuat, Marine
%S Proceedings of the 12th International Workshop on Semantic Evaluation
%D 2018
%8 June
%I Association for Computational Linguistics
%C New Orleans, Louisiana
%F kim-etal-2018-snu
%X 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.
%R 10.18653/v1/S18-1182
%U https://aclanthology.org/S18-1182
%U https://doi.org/10.18653/v1/S18-1182
%P 1083-1088
Markdown (Informal)
[SNU_IDS at SemEval-2018 Task 12: Sentence Encoder with Contextualized Vectors for Argument Reasoning Comprehension](https://aclanthology.org/S18-1182) (Kim et al., SemEval 2018)
ACL