In FinArg-1, we explored three types of financial documents and proposed tasks that combine argument mining and sentiment analysis. In FinArg-2, we aim to introduce "Temporal Inference of Financial Arguments," focusing on the assessment of temporal information, which is a distinct phenomenon in financial opinions. In FinArg-2, we will continue utilizing the same resources as in FinArg-1, including analyst reports, earnings conference calls, and social media data. Furthermore, all annotations will be on the same documents, enabling participants to leverage features from FinArg-1 to enhance their performance.
In FinArg-2, we outline three tasks as follows:
Assessment of the Premise's Influence Period [1]: Models are required to infer the duration of an event's impact on a company's operations.
Detection of Argument Temporal References [2]: Models must identify the temporal reference associated with an argument.
Assessment of the Claim's Validity Period [3]: Models should predict the validity period of a claim. For instance, the opinion "I think $AAPL will reach $200 today" should not be considered valid after three days from its publication time.
[1] Chin-Yi Lin, Chung-Chi Chen, Hen-Hsen Huang, and Hsin-Hsi Chen. 2024. Argument-Based Sentiment Analysis on Forward-Looking Statements. In Findings of the Association for Computational Linguistics: ACL 2024
[2] Alaa Alhamzeh. Financial Argument Quality Assessment in Earnings Conference Calls. In International Conference on Database and Expert Systems Applications, pp. 65-81. 2023.
[3] Chr-Jr Chiu, Chung-Chi Chen, Hen-Hsen Huang, Hsin-Hsi Chen. 2025. Pre-Finetuning with Impact Duration Awareness for Stock Movement Prediction. In Companion Proceedings of the Web Conference 2025 (WWW'25).
FinArg-1 in NTCIR-17 (2023): Fine-grained Argument Understanding in Financial Analysis