This paper presents the findings of the Lowresource Indic Languages Translation Shared Task organized at the Eleventh Conference on Machine Translation (WMT 2026). We evaluated machine translation systems across ten English–Indic language pairs spanning three major language families of Northeast India, grouped into moderately resourced and extremely low-resource categories. Built upon the expanded INDICNE-CORP 2.0 dataset, this edition introduced a rigorous, bidirectional multidomain benchmark covering the Healthcare, Political, Travel, Sports, and Entertainment domains to assess real-world generalizability. The task attracted 24 participating teams who deployed diverse methodologies, including parameter-efficient fine-tuning (PEFT) of multilingual foundations, retrieval-augmented generation (RAG) using frontier large language models (LLMs), and hybrid neuro-symbolic decoding constraints. The systems were evaluated using a comprehensive suite of lexical metrics (BLEU, METEOR, TER, ChrF++) and semantic metrics (BERTScore, COMET). Our analysis reveals a persistent performance cliff under extreme data scarcity and a pronounced directional asymmetry, demonstrating that while current architectures readily decode Indic representations into English, they struggle to generate morphologically complex Indic target text. By publicly releasing these datasets, evaluation protocols, and competitive baselines, this shared task establishes a foundation for advancing research in translation technologies for underrepresented and indigenous languages.

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.

Applications of Natural Language Processing (NLP) in the finance domain have been very popular of late. For financial NLP, (FinNLP) while various datasets exist for widely spoken languages like English and Chinese, datasets are scarce for low resource languages,particularly for Indian languages. In this paper, we address this challenges by presenting IndicFinNLP – a collection of 9 datasets consisting of three tasks relating to FinNLP for three Indian languages. These tasks are Exaggerated Numeral Detection, Sustainability Classification, and ESG Theme Determination of financial texts in Hindi, Bengali, and Telugu. Moreover, we release the datasets under CC BY-NC-SA 4.0 license for the benefit of the research community.