@inproceedings{kaneko-etal-2018-tmu,
title = "{TMU} System for {SLAM}-2018",
author = "Kaneko, Masahiro and
Kajiwara, Tomoyuki and
Komachi, Mamoru",
editor = "Tetreault, Joel and
Burstein, Jill and
Kochmar, Ekaterina and
Leacock, Claudia and
Yannakoudakis, Helen",
booktitle = "Proceedings of the Thirteenth Workshop on Innovative Use of {NLP} for Building Educational Applications",
month = jun,
year = "2018",
address = "New Orleans, Louisiana",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W18-0544",
doi = "10.18653/v1/W18-0544",
pages = "365--369",
abstract = "We introduce the TMU systems for the second language acquisition modeling shared task 2018 (Settles et al., 2018). To model learner error patterns, it is necessary to maintain a considerable amount of information regarding the type of exercises learners have been learning in the past and the manner in which they answered them. Tracking an enormous learner{'}s learning history and their correct and mistaken answers is essential to predict the learner{'}s future mistakes. Therefore, we propose a model which tracks the learner{'}s learning history efficiently. Our systems ranked fourth in the English and Spanish subtasks, and fifth in the French subtask.",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="kaneko-etal-2018-tmu">
<titleInfo>
<title>TMU System for SLAM-2018</title>
</titleInfo>
<name type="personal">
<namePart type="given">Masahiro</namePart>
<namePart type="family">Kaneko</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Tomoyuki</namePart>
<namePart type="family">Kajiwara</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Mamoru</namePart>
<namePart type="family">Komachi</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 Thirteenth Workshop on Innovative Use of NLP for Building Educational Applications</title>
</titleInfo>
<name type="personal">
<namePart type="given">Joel</namePart>
<namePart type="family">Tetreault</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Jill</namePart>
<namePart type="family">Burstein</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Ekaterina</namePart>
<namePart type="family">Kochmar</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Claudia</namePart>
<namePart type="family">Leacock</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Helen</namePart>
<namePart type="family">Yannakoudakis</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>We introduce the TMU systems for the second language acquisition modeling shared task 2018 (Settles et al., 2018). To model learner error patterns, it is necessary to maintain a considerable amount of information regarding the type of exercises learners have been learning in the past and the manner in which they answered them. Tracking an enormous learner’s learning history and their correct and mistaken answers is essential to predict the learner’s future mistakes. Therefore, we propose a model which tracks the learner’s learning history efficiently. Our systems ranked fourth in the English and Spanish subtasks, and fifth in the French subtask.</abstract>
<identifier type="citekey">kaneko-etal-2018-tmu</identifier>
<identifier type="doi">10.18653/v1/W18-0544</identifier>
<location>
<url>https://aclanthology.org/W18-0544</url>
</location>
<part>
<date>2018-06</date>
<extent unit="page">
<start>365</start>
<end>369</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T TMU System for SLAM-2018
%A Kaneko, Masahiro
%A Kajiwara, Tomoyuki
%A Komachi, Mamoru
%Y Tetreault, Joel
%Y Burstein, Jill
%Y Kochmar, Ekaterina
%Y Leacock, Claudia
%Y Yannakoudakis, Helen
%S Proceedings of the Thirteenth Workshop on Innovative Use of NLP for Building Educational Applications
%D 2018
%8 June
%I Association for Computational Linguistics
%C New Orleans, Louisiana
%F kaneko-etal-2018-tmu
%X We introduce the TMU systems for the second language acquisition modeling shared task 2018 (Settles et al., 2018). To model learner error patterns, it is necessary to maintain a considerable amount of information regarding the type of exercises learners have been learning in the past and the manner in which they answered them. Tracking an enormous learner’s learning history and their correct and mistaken answers is essential to predict the learner’s future mistakes. Therefore, we propose a model which tracks the learner’s learning history efficiently. Our systems ranked fourth in the English and Spanish subtasks, and fifth in the French subtask.
%R 10.18653/v1/W18-0544
%U https://aclanthology.org/W18-0544
%U https://doi.org/10.18653/v1/W18-0544
%P 365-369
Markdown (Informal)
[TMU System for SLAM-2018](https://aclanthology.org/W18-0544) (Kaneko et al., BEA 2018)
ACL
- Masahiro Kaneko, Tomoyuki Kajiwara, and Mamoru Komachi. 2018. TMU System for SLAM-2018. In Proceedings of the Thirteenth Workshop on Innovative Use of NLP for Building Educational Applications, pages 365–369, New Orleans, Louisiana. Association for Computational Linguistics.