@inproceedings{gon-etal-2022-jetsons,
title = "Jetsons at the {F}in{NLP}-2022 {ERAI} Task: {BERT}-{C}hinese for mining high {MPP} posts",
author = "Gon, Alolika and
Zha, Sihan and
Rallabandi, Sai Krishna and
Dakle, Parag Pravin and
Raghavan, Preethi",
editor = "Chen, Chung-Chi and
Huang, Hen-Hsen and
Takamura, Hiroya and
Chen, Hsin-Hsi",
booktitle = "Proceedings of the Fourth Workshop on Financial Technology and Natural Language Processing (FinNLP)",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates (Hybrid)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.finnlp-1.19",
doi = "10.18653/v1/2022.finnlp-1.19",
pages = "141--146",
abstract = "In this paper, we discuss the various approaches by the \textit{Jetsons} team for the {``}Pairwise Comparison{''} sub-task of the ERAI shared task to compare financial opinions for profitability and loss. Our BERT-Chinese model considers a pair of opinions and predicts the one with a higher maximum potential profit (MPP) with 62.07{\%} accuracy. We analyze the performance of our approaches on both the MPP and maximal loss (ML) problems and deeply dive into why BERT-Chinese outperforms other models.",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="gon-etal-2022-jetsons">
<titleInfo>
<title>Jetsons at the FinNLP-2022 ERAI Task: BERT-Chinese for mining high MPP posts</title>
</titleInfo>
<name type="personal">
<namePart type="given">Alolika</namePart>
<namePart type="family">Gon</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Sihan</namePart>
<namePart type="family">Zha</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Sai</namePart>
<namePart type="given">Krishna</namePart>
<namePart type="family">Rallabandi</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Parag</namePart>
<namePart type="given">Pravin</namePart>
<namePart type="family">Dakle</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Preethi</namePart>
<namePart type="family">Raghavan</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2022-12</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the Fourth Workshop on Financial Technology and Natural Language Processing (FinNLP)</title>
</titleInfo>
<name type="personal">
<namePart type="given">Chung-Chi</namePart>
<namePart type="family">Chen</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Hen-Hsen</namePart>
<namePart type="family">Huang</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Hiroya</namePart>
<namePart type="family">Takamura</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Hsin-Hsi</namePart>
<namePart type="family">Chen</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Abu Dhabi, United Arab Emirates (Hybrid)</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>In this paper, we discuss the various approaches by the Jetsons team for the “Pairwise Comparison” sub-task of the ERAI shared task to compare financial opinions for profitability and loss. Our BERT-Chinese model considers a pair of opinions and predicts the one with a higher maximum potential profit (MPP) with 62.07% accuracy. We analyze the performance of our approaches on both the MPP and maximal loss (ML) problems and deeply dive into why BERT-Chinese outperforms other models.</abstract>
<identifier type="citekey">gon-etal-2022-jetsons</identifier>
<identifier type="doi">10.18653/v1/2022.finnlp-1.19</identifier>
<location>
<url>https://aclanthology.org/2022.finnlp-1.19</url>
</location>
<part>
<date>2022-12</date>
<extent unit="page">
<start>141</start>
<end>146</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Jetsons at the FinNLP-2022 ERAI Task: BERT-Chinese for mining high MPP posts
%A Gon, Alolika
%A Zha, Sihan
%A Rallabandi, Sai Krishna
%A Dakle, Parag Pravin
%A Raghavan, Preethi
%Y Chen, Chung-Chi
%Y Huang, Hen-Hsen
%Y Takamura, Hiroya
%Y Chen, Hsin-Hsi
%S Proceedings of the Fourth Workshop on Financial Technology and Natural Language Processing (FinNLP)
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates (Hybrid)
%F gon-etal-2022-jetsons
%X In this paper, we discuss the various approaches by the Jetsons team for the “Pairwise Comparison” sub-task of the ERAI shared task to compare financial opinions for profitability and loss. Our BERT-Chinese model considers a pair of opinions and predicts the one with a higher maximum potential profit (MPP) with 62.07% accuracy. We analyze the performance of our approaches on both the MPP and maximal loss (ML) problems and deeply dive into why BERT-Chinese outperforms other models.
%R 10.18653/v1/2022.finnlp-1.19
%U https://aclanthology.org/2022.finnlp-1.19
%U https://doi.org/10.18653/v1/2022.finnlp-1.19
%P 141-146
Markdown (Informal)
[Jetsons at the FinNLP-2022 ERAI Task: BERT-Chinese for mining high MPP posts](https://aclanthology.org/2022.finnlp-1.19) (Gon et al., FinNLP 2022)
ACL