FinArg-1: Fine-grained Argument Understandingin Financial Analysis
Chung-Chi Chen, AIRC, AIST, Japan
Hen-Hsen Huang, Institute of Information Science, Academia Sinica, Taiwan
Hsin-Hsi Chen, Department of Computer Science and Information Engineering, National Taiwan University
Hiroya Takamura, AIRC, AIST, Japan
Yu-Lieh Huang, Department of Quantitative Finance, National Tsing Hua University
Alaa Alhamzeh, Department of Computer Sciences, INSA Lyon

NLPFinOverview of FinArg Shared Task Series
NLPFinLanguage
FinArg-1

FinArg-2
FinArg-3

EnglishChineseEnglishChineseEnglishChineseSourceAnalyst Report
Earning Conference Call
Social Media
Analyst ReportSocial Media
Analyst ReportSocial Media

Task
Argument-based Sentiment Detection
Identifying Attack and Support Argumentative Relations in Social Media Discussion Thread
Premise's Influence Period Assessment
Claim's Validity Period Assessment
High Forecasting Skill Report Retrieval
High Forecasting Skill Opinion Retrieval•Each Round: Two Tasks and Two Languages•

FinArg-1 and FinArg-2 Extract Argument Features••

FinArg-1: General (Argument Mining)•FinArg-2: Temporal (Tailor-made for Financial Data)FinArg-3 Retrieval and Recommendation•Participants can use the features extracted in FinNum or FinArg

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FinArg-1
NLPFin

Task

SubtaskArgument Classification

Argument-based Sentiment Detection

Premise Sentiment Analysis
Claim Sentiment Analysis

Identifying Attack and Support Argumentative Relations in Social Media Discussion Thread-3

Task Design
NLPFin

Task 1: Argument-based Sentiment Analysis (Research Report & Earnings Conference Call)(1) Argument classification: classify the given sentence into claim or premise(2) Premise sentiment analysis: classify the given premise into positive, neutral, or negative(3) Claim sentiment analysis: classify the given claim into bullish, neutral, or bearish
Task 2: Identifying Attack and Support Argumentative Relations in Social Media Discussion
Threads (Social Media)• Identify the argumentative relations (attack, support, or irrelevant) between two given social
media posts.

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Example – Task 1
NLPFinArgument-based Sentiment Analysis (Research Report & Earnings Conference Call)
Negative Premise

Looking forward to 2020, although the outlook for foreign currency lending due to COVID-19 is not good,

Positivethe momentum of Taiwan dollar lending is still stable, driven by 5G, green energy, the return ofPremise

Taiwanese businessmen and the government's forward-looking plan. We revise up CITIC Bank's 2020
lending growth to mid-single digits.

BullishClaim

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Example – Task 2
NLPFinIdentifying Attack and Support Argumentative Relations in Social Media Discussion Threads(Social Media)

6Schedule
NLPFin7

Schedule
NLPFin2023
Jan 31: Chinese Training and Development Set ReleaseMarch 31: English Training and Development Set ReleaseJun 30: Test Set Release
July 10: Task Registration Due
July 15: Participants' Results Submission Due
August 1: Evaluation Result Release & Draft Task Overview Paper Release
September 1: Participants' Papers Submission Due
November 1: Camera-ready Paper
December: NTCIR-17 Conference in NII, Tokyo, Japan

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Thank you!
Q&A

Natural Language Processing in Finance Team
http://nlpfin.com/