@inproceedings{brassard-etal-2018-takelab,
title = "{T}ake{L}ab at {S}em{E}val-2018 Task12: Argument Reasoning Comprehension with Skip-Thought Vectors",
author = "Brassard, Ana and
Kuculo, Tin and
Boltu{\v{z}}i{\'c}, Filip and
{\v{S}}najder, Jan",
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/S18-1192",
doi = "10.18653/v1/S18-1192",
pages = "1133--1136",
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.",
}
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%0 Conference Proceedings
%T TakeLab at SemEval-2018 Task12: Argument Reasoning Comprehension with Skip-Thought Vectors
%A Brassard, Ana
%A Kuculo, Tin
%A Boltužić, Filip
%A Šnajder, Jan
%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 brassard-etal-2018-takelab
%X 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.
%R 10.18653/v1/S18-1192
%U https://aclanthology.org/S18-1192
%U https://doi.org/10.18653/v1/S18-1192
%P 1133-1136
Markdown (Informal)
[TakeLab at SemEval-2018 Task12: Argument Reasoning Comprehension with Skip-Thought Vectors](https://aclanthology.org/S18-1192) (Brassard et al., SemEval 2018)
ACL