@inproceedings{niven-kao-2018-nlitrans,
title = "{NLIT}rans at {S}em{E}val-2018 Task 12: Transfer of Semantic Knowledge for Argument Comprehension",
author = "Niven, Timothy and
Kao, Hung-Yu",
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/S18-1185",
doi = "10.18653/v1/S18-1185",
pages = "1099--1103",
abstract = "The Argument Reasoning Comprehension Task is a difficult challenge requiring significant language understanding and complex reasoning over world knowledge. We focus on transfer of a sentence encoder to bootstrap more complicated architectures given the small size of the dataset. Our best model uses a pre-trained BiLSTM to encode input sentences, learns task-specific features for the argument and warrants, then performs independent argument-warrant matching. This model achieves mean test set accuracy of 61.31{\%}. Encoder transfer yields a significant gain to our best model over random initialization. Sharing parameters for independent warrant evaluation provides regularization and effectively doubles the size of the dataset. We demonstrate that regularization comes from ignoring statistical correlations between warrant positions. We also report an experiment with our best model that only matches warrants to reasons, ignoring claims. Performance is still competitive, suggesting that our model is not necessarily learning the intended task.",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="niven-kao-2018-nlitrans">
<titleInfo>
<title>NLITrans at SemEval-2018 Task 12: Transfer of Semantic Knowledge for Argument Comprehension</title>
</titleInfo>
<name type="personal">
<namePart type="given">Timothy</namePart>
<namePart type="family">Niven</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Hung-Yu</namePart>
<namePart type="family">Kao</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2018-06</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the 12th International Workshop on Semantic Evaluation</title>
</titleInfo>
<name type="personal">
<namePart type="given">Marianna</namePart>
<namePart type="family">Apidianaki</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Saif</namePart>
<namePart type="given">M</namePart>
<namePart type="family">Mohammad</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Jonathan</namePart>
<namePart type="family">May</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Ekaterina</namePart>
<namePart type="family">Shutova</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Steven</namePart>
<namePart type="family">Bethard</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Marine</namePart>
<namePart type="family">Carpuat</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">New Orleans, Louisiana</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>The Argument Reasoning Comprehension Task is a difficult challenge requiring significant language understanding and complex reasoning over world knowledge. We focus on transfer of a sentence encoder to bootstrap more complicated architectures given the small size of the dataset. Our best model uses a pre-trained BiLSTM to encode input sentences, learns task-specific features for the argument and warrants, then performs independent argument-warrant matching. This model achieves mean test set accuracy of 61.31%. Encoder transfer yields a significant gain to our best model over random initialization. Sharing parameters for independent warrant evaluation provides regularization and effectively doubles the size of the dataset. We demonstrate that regularization comes from ignoring statistical correlations between warrant positions. We also report an experiment with our best model that only matches warrants to reasons, ignoring claims. Performance is still competitive, suggesting that our model is not necessarily learning the intended task.</abstract>
<identifier type="citekey">niven-kao-2018-nlitrans</identifier>
<identifier type="doi">10.18653/v1/S18-1185</identifier>
<location>
<url>https://aclanthology.org/S18-1185</url>
</location>
<part>
<date>2018-06</date>
<extent unit="page">
<start>1099</start>
<end>1103</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T NLITrans at SemEval-2018 Task 12: Transfer of Semantic Knowledge for Argument Comprehension
%A Niven, Timothy
%A Kao, Hung-Yu
%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 niven-kao-2018-nlitrans
%X The Argument Reasoning Comprehension Task is a difficult challenge requiring significant language understanding and complex reasoning over world knowledge. We focus on transfer of a sentence encoder to bootstrap more complicated architectures given the small size of the dataset. Our best model uses a pre-trained BiLSTM to encode input sentences, learns task-specific features for the argument and warrants, then performs independent argument-warrant matching. This model achieves mean test set accuracy of 61.31%. Encoder transfer yields a significant gain to our best model over random initialization. Sharing parameters for independent warrant evaluation provides regularization and effectively doubles the size of the dataset. We demonstrate that regularization comes from ignoring statistical correlations between warrant positions. We also report an experiment with our best model that only matches warrants to reasons, ignoring claims. Performance is still competitive, suggesting that our model is not necessarily learning the intended task.
%R 10.18653/v1/S18-1185
%U https://aclanthology.org/S18-1185
%U https://doi.org/10.18653/v1/S18-1185
%P 1099-1103
Markdown (Informal)
[NLITrans at SemEval-2018 Task 12: Transfer of Semantic Knowledge for Argument Comprehension](https://aclanthology.org/S18-1185) (Niven & Kao, SemEval 2018)
ACL