InFiNITE (∞): Indian Financial Narrative Inference Tasks & Evaluations

ACL ARR 2025 July Submission434 Authors

Abstract: This paper introduces Indian Financial Narrative Inference Tasks and Evaluations (InFiNITE), a comprehensive framework for analyzing India's financial narratives through three novel inference tasks. Firstly, we present multi-modal earnings call analysis by integrating transcripts, presentation visuals, and market indicators via the Multi-Modal Indian Earnings Calls (MiMIC) dataset, enabling holistic prediction of post-call stock movements. Secondly, our Budget-Assisted Sectoral Impact Ranking (BASIR) dataset aids in systematically decoding government fiscal narratives by classifying budget excerpts into 81 economic sectors and evaluating their post-announcement equity performance. Thirdly, we introduce Bharat IPO Rating (BIR) datasets to redefine Initial Public Offering (IPO) evaluation through prospectus analysis, classifying potential investments into four recommendation categories (Apply, May Apply, Neutral, Avoid). By unifying textual, visual, and quantitative modalities across corporate, governmental, and public investment domains, InFiNITE addresses critical gaps in Indian financial narrative analysis. The open source data sets of the framework, including earnings calls, union budgets, and IPO prospectuses, establish benchmark resources specific to India for computational economic research under permissive licenses. For investors, InFiNITE enables data-driven identification of capital allocation opportunities and IPO risks, while policymakers gain structured insights to assess Indian fiscal communication impacts. By releasing these datasets publicly, we aim to facilitate research in computational economics and financial text analysis, particularly for the Indian market.

Paper Type: Long

Research Area: Resources and Evaluation

Research Area Keywords: corpus creation, benchmarking, language resources, NLP datasets, evaluation

Contribution Types: Data resources, Data analysis

Languages Studied: English

Previous URL: https://openreview.net/forum?id=XcM7lSsHVx

Reassignment Request Area Chair: Yes, I want a different area chair for our submission

Reassignment Request Reviewers: Yes, I want a different set of reviewers

Justification For Not Keeping Action Editor Or Reviewers: Disproportionate Score Reduction Without Adequate Justification: The Area Chair implemented a substantial score reduction from 2.83 to 2.0 without providing commensurate substantive feedback to justify such a significant downgrade. This dramatic adjustment appears inconsistent with the constructive nature of the revisions we undertook in direct response to reviewer concerns. Reviewer Non-Engagement During Critical Rebuttal Period: One reviewer failed to participate in the rebuttal process entirely, preventing meaningful scholarly dialogue and the opportunity to address potential misunderstandings or provide clarifications that could have influenced the final assessment. Misalignment Between Criticism Severity and Resource Constraints: Several criticisms appeared disproportionately stringent given the inherent resource limitations in computational linguistics research, particularly for specialized domains like Indian financial markets. The expectations seemed to exceed reasonable standards for work conducted under typical academic constraints. Better Venue Alignment for Regional Contributions: While EMNLP's prestigious A* ranking attracts high-quality submissions, our work's focus on Indian financial markets and South Asian linguistic phenomena aligns more naturally with AACL-IJCNLP's Asia-Pacific scope. This specialized regional focus represents a significant contribution to underrepresented markets in computational finance, warranting evaluation by reviewers with appropriate domain expertise and cultural context. Request for Fresh Perspective: We respectfully request new reviewers who can evaluate our contributions within the appropriate regional and methodological context, ensuring fair assessment of our work's significance to the Asia-Pacific computational linguistics community. The average reviewer confidence score of 3.33 raises significant concerns about the thoroughness of the evaluation process. Notably, two out of three reviewers explicitly stated "Confidence: 3". This acknowledgment of incomplete review undermines the validity of their assessments and suggests that critical technical aspects of our work may not have received adequate scrutiny. Fresh reviewers with demonstrated expertise in computational finance and commitment to thorough technical evaluation would ensure our work receives the comprehensive assessment it deserves.

A1 Limitations Section: This paper has a limitations section.

A2 Potential Risks: Yes

A2 Elaboration: Appendix G

B Use Or Create Scientific Artifacts: Yes

B1 Cite Creators Of Artifacts: N/A

B1 Elaboration: We created the datasets being used in the paper.

B2 Discuss The License For Artifacts: Yes

B2 Elaboration: Appendix G

B3 Artifact Use Consistent With Intended Use: Yes

B3 Elaboration: Appendix G

B4 Data Contains Personally Identifying Info Or Offensive Content: Yes

B4 Elaboration: 3.3.2

B5 Documentation Of Artifacts: Yes

B5 Elaboration: Appendix A

B6 Statistics For Data: Yes

B6 Elaboration: Table-2, Section: 3.1.2, 3.2.2, 3.3.2

C Computational Experiments: Yes

C1 Model Size And Budget: Yes

C1 Elaboration: Appendix C.3, Appendix E.4

C2 Experimental Setup And Hyperparameters: Yes

C2 Elaboration: Section 4, Appendix C.3, Appendix E.4

C3 Descriptive Statistics: No

C3 Elaboration: It was a single run

C4 Parameters For Packages: Yes

C4 Elaboration: Section 4

D Human Subjects Including Annotators: Yes

D1 Instructions Given To Participants: No

D1 Elaboration: It was a simple verification exercise

D2 Recruitment And Payment: No

D2 Elaboration: It was a basic verification exercise for one of our dataset (BASIR)

D3 Data Consent: N/A

D4 Ethics Review Board Approval: N/A

D5 Characteristics Of Annotators: N/A

E Ai Assistants In Research Or Writing: Yes

E1 Information About Use Of Ai Assistants: Yes

E1 Elaboration: Appendix F

Author Submission Checklist: yes

Submission Number: 434