@inproceedings{kane-etal-2020-nubia,
title = "{NUBIA}: {N}e{U}ral Based Interchangeability Assessor for Text Generation",
author = "Kane, Hassan and
Kocyigit, Muhammed Yusuf and
Abdalla, Ali and
Ajanoh, Pelkins and
Coulibali, Mohamed",
editor = "Agarwal, Shubham and
Du{\v{s}}ek, Ond{\v{r}}ej and
Gehrmann, Sebastian and
Gkatzia, Dimitra and
Konstas, Ioannis and
Van Miltenburg, Emiel and
Santhanam, Sashank",
booktitle = "Proceedings of the 1st Workshop on Evaluating NLG Evaluation",
month = dec,
year = "2020",
address = "Online (Dublin, Ireland)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.evalnlgeval-1.4",
pages = "28--37",
abstract = "We present NUBIA, a methodology to build automatic evaluation metrics for text generation using only machine learning models as core components. A typical NUBIA model is composed of three modules: a neural feature extractor, an aggregator and a calibrator. We demonstrate an implementation of NUBIA showing competitive performance with stateof-the art metrics used to evaluate machine translation and state-of-the art results for image captions quality evaluation. In addition to strong performance, NUBIA models have the advantage of being modular and improve in synergy with advances in text generation models.",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="kane-etal-2020-nubia">
<titleInfo>
<title>NUBIA: NeUral Based Interchangeability Assessor for Text Generation</title>
</titleInfo>
<name type="personal">
<namePart type="given">Hassan</namePart>
<namePart type="family">Kane</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Muhammed</namePart>
<namePart type="given">Yusuf</namePart>
<namePart type="family">Kocyigit</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Ali</namePart>
<namePart type="family">Abdalla</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Pelkins</namePart>
<namePart type="family">Ajanoh</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Mohamed</namePart>
<namePart type="family">Coulibali</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2020-12</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the 1st Workshop on Evaluating NLG Evaluation</title>
</titleInfo>
<name type="personal">
<namePart type="given">Shubham</namePart>
<namePart type="family">Agarwal</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Ondřej</namePart>
<namePart type="family">Dušek</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Sebastian</namePart>
<namePart type="family">Gehrmann</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Dimitra</namePart>
<namePart type="family">Gkatzia</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Ioannis</namePart>
<namePart type="family">Konstas</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Emiel</namePart>
<namePart type="family">Van Miltenburg</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Sashank</namePart>
<namePart type="family">Santhanam</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Online (Dublin, Ireland)</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>We present NUBIA, a methodology to build automatic evaluation metrics for text generation using only machine learning models as core components. A typical NUBIA model is composed of three modules: a neural feature extractor, an aggregator and a calibrator. We demonstrate an implementation of NUBIA showing competitive performance with stateof-the art metrics used to evaluate machine translation and state-of-the art results for image captions quality evaluation. In addition to strong performance, NUBIA models have the advantage of being modular and improve in synergy with advances in text generation models.</abstract>
<identifier type="citekey">kane-etal-2020-nubia</identifier>
<location>
<url>https://aclanthology.org/2020.evalnlgeval-1.4</url>
</location>
<part>
<date>2020-12</date>
<extent unit="page">
<start>28</start>
<end>37</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T NUBIA: NeUral Based Interchangeability Assessor for Text Generation
%A Kane, Hassan
%A Kocyigit, Muhammed Yusuf
%A Abdalla, Ali
%A Ajanoh, Pelkins
%A Coulibali, Mohamed
%Y Agarwal, Shubham
%Y Dušek, Ondřej
%Y Gehrmann, Sebastian
%Y Gkatzia, Dimitra
%Y Konstas, Ioannis
%Y Van Miltenburg, Emiel
%Y Santhanam, Sashank
%S Proceedings of the 1st Workshop on Evaluating NLG Evaluation
%D 2020
%8 December
%I Association for Computational Linguistics
%C Online (Dublin, Ireland)
%F kane-etal-2020-nubia
%X We present NUBIA, a methodology to build automatic evaluation metrics for text generation using only machine learning models as core components. A typical NUBIA model is composed of three modules: a neural feature extractor, an aggregator and a calibrator. We demonstrate an implementation of NUBIA showing competitive performance with stateof-the art metrics used to evaluate machine translation and state-of-the art results for image captions quality evaluation. In addition to strong performance, NUBIA models have the advantage of being modular and improve in synergy with advances in text generation models.
%U https://aclanthology.org/2020.evalnlgeval-1.4
%P 28-37
Markdown (Informal)
[NUBIA: NeUral Based Interchangeability Assessor for Text Generation](https://aclanthology.org/2020.evalnlgeval-1.4) (Kane et al., EvalNLGEval 2020)
ACL