@inproceedings{sharma-etal-2021-lrg,
title = "{LRG} at {S}em{E}val-2021 Task 4: Improving Reading Comprehension with Abstract Words using Augmentation, Linguistic Features and Voting",
author = "Sharma, Abheesht and
Pandey, Harshit and
Chhablani, Gunjan and
Bhartia, Yash and
Dash, Tirtharaj",
editor = "Palmer, Alexis and
Schneider, Nathan and
Schluter, Natalie and
Emerson, Guy and
Herbelot, Aurelie and
Zhu, Xiaodan",
booktitle = "Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.semeval-1.21",
doi = "10.18653/v1/2021.semeval-1.21",
pages = "189--198",
abstract = "We present our approaches and methods for SemEval-2021 Task-4 Reading Comprehension of Abstract Meaning. Given a question with a fill-in-the-blank, and a corresponding context, the task is to predict the most suitable word from a list of 5 options. There are three subtasks: Imperceptibility, Non-Specificity and Intersection. We use encoders of transformers-based models pretrained on the MLM task to build our Fill-in-the-blank (FitB) models. Moreover, to model imperceptibility, we define certain linguistic features, and to model non-specificity, we leverage information from hypernyms and hyponyms provided by a lexical database. Specifically, for non-specificity, we try out augmentation techniques, and other statistical techniques. We also propose variants, namely Chunk Voting and Max Context, to take care of input length restrictions for BERT, etc. Additionally, we perform a thorough ablation study, and use Integrated Gradients to explain our predictions on a few samples. Our models achieve accuracies of 75.31{\%} and 77.84{\%}, on the test sets for subtask-I and subtask-II, respectively. For subtask-III, we achieve accuracies of 65.64{\%} and 64.27{\%}.",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="sharma-etal-2021-lrg">
<titleInfo>
<title>LRG at SemEval-2021 Task 4: Improving Reading Comprehension with Abstract Words using Augmentation, Linguistic Features and Voting</title>
</titleInfo>
<name type="personal">
<namePart type="given">Abheesht</namePart>
<namePart type="family">Sharma</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Harshit</namePart>
<namePart type="family">Pandey</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Gunjan</namePart>
<namePart type="family">Chhablani</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Yash</namePart>
<namePart type="family">Bhartia</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Tirtharaj</namePart>
<namePart type="family">Dash</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2021-08</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)</title>
</titleInfo>
<name type="personal">
<namePart type="given">Alexis</namePart>
<namePart type="family">Palmer</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Nathan</namePart>
<namePart type="family">Schneider</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Natalie</namePart>
<namePart type="family">Schluter</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Guy</namePart>
<namePart type="family">Emerson</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Aurelie</namePart>
<namePart type="family">Herbelot</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Xiaodan</namePart>
<namePart type="family">Zhu</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Online</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>We present our approaches and methods for SemEval-2021 Task-4 Reading Comprehension of Abstract Meaning. Given a question with a fill-in-the-blank, and a corresponding context, the task is to predict the most suitable word from a list of 5 options. There are three subtasks: Imperceptibility, Non-Specificity and Intersection. We use encoders of transformers-based models pretrained on the MLM task to build our Fill-in-the-blank (FitB) models. Moreover, to model imperceptibility, we define certain linguistic features, and to model non-specificity, we leverage information from hypernyms and hyponyms provided by a lexical database. Specifically, for non-specificity, we try out augmentation techniques, and other statistical techniques. We also propose variants, namely Chunk Voting and Max Context, to take care of input length restrictions for BERT, etc. Additionally, we perform a thorough ablation study, and use Integrated Gradients to explain our predictions on a few samples. Our models achieve accuracies of 75.31% and 77.84%, on the test sets for subtask-I and subtask-II, respectively. For subtask-III, we achieve accuracies of 65.64% and 64.27%.</abstract>
<identifier type="citekey">sharma-etal-2021-lrg</identifier>
<identifier type="doi">10.18653/v1/2021.semeval-1.21</identifier>
<location>
<url>https://aclanthology.org/2021.semeval-1.21</url>
</location>
<part>
<date>2021-08</date>
<extent unit="page">
<start>189</start>
<end>198</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T LRG at SemEval-2021 Task 4: Improving Reading Comprehension with Abstract Words using Augmentation, Linguistic Features and Voting
%A Sharma, Abheesht
%A Pandey, Harshit
%A Chhablani, Gunjan
%A Bhartia, Yash
%A Dash, Tirtharaj
%Y Palmer, Alexis
%Y Schneider, Nathan
%Y Schluter, Natalie
%Y Emerson, Guy
%Y Herbelot, Aurelie
%Y Zhu, Xiaodan
%S Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)
%D 2021
%8 August
%I Association for Computational Linguistics
%C Online
%F sharma-etal-2021-lrg
%X We present our approaches and methods for SemEval-2021 Task-4 Reading Comprehension of Abstract Meaning. Given a question with a fill-in-the-blank, and a corresponding context, the task is to predict the most suitable word from a list of 5 options. There are three subtasks: Imperceptibility, Non-Specificity and Intersection. We use encoders of transformers-based models pretrained on the MLM task to build our Fill-in-the-blank (FitB) models. Moreover, to model imperceptibility, we define certain linguistic features, and to model non-specificity, we leverage information from hypernyms and hyponyms provided by a lexical database. Specifically, for non-specificity, we try out augmentation techniques, and other statistical techniques. We also propose variants, namely Chunk Voting and Max Context, to take care of input length restrictions for BERT, etc. Additionally, we perform a thorough ablation study, and use Integrated Gradients to explain our predictions on a few samples. Our models achieve accuracies of 75.31% and 77.84%, on the test sets for subtask-I and subtask-II, respectively. For subtask-III, we achieve accuracies of 65.64% and 64.27%.
%R 10.18653/v1/2021.semeval-1.21
%U https://aclanthology.org/2021.semeval-1.21
%U https://doi.org/10.18653/v1/2021.semeval-1.21
%P 189-198
Markdown (Informal)
[LRG at SemEval-2021 Task 4: Improving Reading Comprehension with Abstract Words using Augmentation, Linguistic Features and Voting](https://aclanthology.org/2021.semeval-1.21) (Sharma et al., SemEval 2021)
ACL