@inproceedings{jiang-sun-2018-csreader,
title = "{CSR}eader at {S}em{E}val-2018 Task 11: Multiple Choice Question Answering as Textual Entailment",
author = "Jiang, Zhengping and
Sun, Qi",
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/S18-1176",
doi = "10.18653/v1/S18-1176",
pages = "1053--1057",
abstract = "In this document we present an end-to-end machine reading comprehension system that solves multiple choice questions with a textual entailment perspective. Since some of the knowledge required is not explicitly mentioned in the text, we try to exploit commonsense knowledge by using pretrained word embeddings during contextual embeddings and by dynamically generating a weighted representation of related script knowledge. In the model two kinds of prediction structure are ensembled, and the final accuracy of our system is 10 percent higher than the naiive baseline.",
}
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<abstract>In this document we present an end-to-end machine reading comprehension system that solves multiple choice questions with a textual entailment perspective. Since some of the knowledge required is not explicitly mentioned in the text, we try to exploit commonsense knowledge by using pretrained word embeddings during contextual embeddings and by dynamically generating a weighted representation of related script knowledge. In the model two kinds of prediction structure are ensembled, and the final accuracy of our system is 10 percent higher than the naiive baseline.</abstract>
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%0 Conference Proceedings
%T CSReader at SemEval-2018 Task 11: Multiple Choice Question Answering as Textual Entailment
%A Jiang, Zhengping
%A Sun, Qi
%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 jiang-sun-2018-csreader
%X In this document we present an end-to-end machine reading comprehension system that solves multiple choice questions with a textual entailment perspective. Since some of the knowledge required is not explicitly mentioned in the text, we try to exploit commonsense knowledge by using pretrained word embeddings during contextual embeddings and by dynamically generating a weighted representation of related script knowledge. In the model two kinds of prediction structure are ensembled, and the final accuracy of our system is 10 percent higher than the naiive baseline.
%R 10.18653/v1/S18-1176
%U https://aclanthology.org/S18-1176
%U https://doi.org/10.18653/v1/S18-1176
%P 1053-1057
Markdown (Informal)
[CSReader at SemEval-2018 Task 11: Multiple Choice Question Answering as Textual Entailment](https://aclanthology.org/S18-1176) (Jiang & Sun, SemEval 2018)
ACL