@inproceedings{zou-etal-2022-uoa,
title = "{UOA} at the {F}in{NLP}-2022 {ERAI} Task: Leveraging the Class Label Description for Financial Opinion Mining",
author = "Zou, Jinan and
Cao, Haiyao and
Liu, Yanxi and
Liu, Lingqiao and
Abbasnejad, Ehsan and
Shi, Javen Qinfeng",
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-1.15",
doi = "10.18653/v1/2022.finnlp-1.15",
pages = "122--126",
abstract = "Evaluating the Rationales of Amateur Investors (ERAI) is a task about mining expert-like viewpoints from social media. This paper summarizes our solutions to the ERAI shared task, which is co-located with the FinNLP workshop at EMNLP 2022. There are 2 sub-tasks in ERAI. Sub-task 1 is a pair-wised comparison task, where we propose a BERT-based pre-trained model projecting opinion pairs in a common space for classification. Sub-task 2 is an unsupervised learning task ranking the opinions{'} maximal potential profit (MPP) and maximal loss (ML), where our model leverages the regression method and multi-layer perceptron to rank the MPP and ML values. The proposed approaches achieve competitive accuracy of 54.02{\%} on ML Accuracy and 51.72{\%} on MPP Accuracy for pairwise tasks, also 12.35{\%} and -9.39{\%} regression unsupervised ranking task for MPP and ML.",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="zou-etal-2022-uoa">
<titleInfo>
<title>UOA at the FinNLP-2022 ERAI Task: Leveraging the Class Label Description for Financial Opinion Mining</title>
</titleInfo>
<name type="personal">
<namePart type="given">Jinan</namePart>
<namePart type="family">Zou</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Haiyao</namePart>
<namePart type="family">Cao</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Yanxi</namePart>
<namePart type="family">Liu</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Lingqiao</namePart>
<namePart type="family">Liu</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Ehsan</namePart>
<namePart type="family">Abbasnejad</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Javen</namePart>
<namePart type="given">Qinfeng</namePart>
<namePart type="family">Shi</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>Evaluating the Rationales of Amateur Investors (ERAI) is a task about mining expert-like viewpoints from social media. This paper summarizes our solutions to the ERAI shared task, which is co-located with the FinNLP workshop at EMNLP 2022. There are 2 sub-tasks in ERAI. Sub-task 1 is a pair-wised comparison task, where we propose a BERT-based pre-trained model projecting opinion pairs in a common space for classification. Sub-task 2 is an unsupervised learning task ranking the opinions’ maximal potential profit (MPP) and maximal loss (ML), where our model leverages the regression method and multi-layer perceptron to rank the MPP and ML values. The proposed approaches achieve competitive accuracy of 54.02% on ML Accuracy and 51.72% on MPP Accuracy for pairwise tasks, also 12.35% and -9.39% regression unsupervised ranking task for MPP and ML.</abstract>
<identifier type="citekey">zou-etal-2022-uoa</identifier>
<identifier type="doi">10.18653/v1/2022.finnlp-1.15</identifier>
<location>
<url>https://aclanthology.org/2022.finnlp-1.15</url>
</location>
<part>
<date>2022-12</date>
<extent unit="page">
<start>122</start>
<end>126</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T UOA at the FinNLP-2022 ERAI Task: Leveraging the Class Label Description for Financial Opinion Mining
%A Zou, Jinan
%A Cao, Haiyao
%A Liu, Yanxi
%A Liu, Lingqiao
%A Abbasnejad, Ehsan
%A Shi, Javen Qinfeng
%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 zou-etal-2022-uoa
%X Evaluating the Rationales of Amateur Investors (ERAI) is a task about mining expert-like viewpoints from social media. This paper summarizes our solutions to the ERAI shared task, which is co-located with the FinNLP workshop at EMNLP 2022. There are 2 sub-tasks in ERAI. Sub-task 1 is a pair-wised comparison task, where we propose a BERT-based pre-trained model projecting opinion pairs in a common space for classification. Sub-task 2 is an unsupervised learning task ranking the opinions’ maximal potential profit (MPP) and maximal loss (ML), where our model leverages the regression method and multi-layer perceptron to rank the MPP and ML values. The proposed approaches achieve competitive accuracy of 54.02% on ML Accuracy and 51.72% on MPP Accuracy for pairwise tasks, also 12.35% and -9.39% regression unsupervised ranking task for MPP and ML.
%R 10.18653/v1/2022.finnlp-1.15
%U https://aclanthology.org/2022.finnlp-1.15
%U https://doi.org/10.18653/v1/2022.finnlp-1.15
%P 122-126
Markdown (Informal)
[UOA at the FinNLP-2022 ERAI Task: Leveraging the Class Label Description for Financial Opinion Mining](https://aclanthology.org/2022.finnlp-1.15) (Zou et al., FinNLP 2022)
ACL