| Challenge: | Word problem solving is a challenging and interesting task in NLP. |
| Approach: | They propose to use equations to solve Hindi arithmetic word problems . they propose to also use equation equivalence to evaluate word problem solvers . |
| Outcome: | The proposed dataset is based on 2336 arithmetic word problems in Hindi . it also includes baseline systems and evaluation techniques . |
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| Challenge: | Existing solvers for math word problems often achieve high performance on benchmark datasets . existing models rely on shallow heuristics to achieve high accuracy . |
| Approach: | They restrict their attention to English MWPs taught in grades four and lower . they propose a challenge dataset to test the accuracy of MWp solvers . |
| Outcome: | The proposed model can solve a large fraction of MWPs even with shallow heuristics . the proposed model is much lower on the challenge dataset SVAMP . |
BhashaSutra: A Task-Centric Unified Survey of Indian NLP Datasets, Corpora, and Resources (2026.acl-long)
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| Challenge: | Existing reviews focus on a few high-resource languages or embed Indian languages within broad multilingual settings, limiting coverage of low-resourced and culturally diverse varieties. |
| Approach: | They present a unified survey of Indian NLP resources, covering 200+ datasets, 50+ benchmarks, and 100+ models, tools, and systems across text, speech, multimodal, and culturally grounded tasks. |
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Bhaasha, Bhāṣā, Zaban: A Survey for Low-Resourced Languages in South Asia – Current Stage and Challenges (2025.findings-emnlp)
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| Challenge: | a survey examines the current efforts and challenges of NLP models for South Asian languages . there are more than 650 languages in South Asia, but many have very limited computational resources or are missing from existing models. |
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CWID-hi: A Dataset for Complex Word Identification in Hindi Text (2022.lrec-1)
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| Challenge: | Text simplification is a method for improving the accessibility of text by converting complex sentences into simple sentences. |
| Approach: | They propose to use Hindi knowledge annotators to capture the annotator’s language knowledge to build an automatic complex word classifier using a soft voting approach. |
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IndicFinNLP: Financial Natural Language Processing for Indian Languages (2024.lrec-main)
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| Challenge: | IndicFinNLP is a collection of 9 datasets relating to FinNLP for three Indian languages. |
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| Outcome: | The proposed framework detects exaggerated numerals in financial texts written in Hindi, Bengali, and Telugu. |
A Diverse Corpus for Evaluating and Developing English Math Word Problem Solvers (2020.acl-main)
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| Challenge: | Existing MWP corpora are limited in language patterns and problem types . a new corpus of 2,305 MWps is proposed that is more diverse in terms of lexicon usage . |
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ArMATH: a Dataset for Solving Arabic Math Word Problems (2022.lrec-1)
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| Challenge: | This paper is the first to use deep learning methods to solve Arabic MWPs . it is also the first study to use transfer learning to solve MWp across different languages . |
| Approach: | They contribute to the first large-scale dataset for Arabic Math Word Problems . they use deep learning methods to solve Arabic MWPs and a transfer learning model to promote performance . |
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A Platform for Event Extraction in Hindi (2020.lrec-1)
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| Challenge: | Event Extraction is an important task in the widespread field of NLP, but there is no benchmark setup in Hindi. |
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WARM: A Weakly (+Semi) Supervised Math Word Problem Solver (2022.coling-1)
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| Challenge: | Existing approaches to solving math word problems require full supervision in the form of intermediate equations. |
| Approach: | They propose a weakly supervised model that requires only the final answer as supervision to solve math word problems. |
| Outcome: | The proposed model achieves accuracy gains of 4.5% and 32% over current weakly-supervised methods on standard Math23K and AllArith datasets. |
Hi-GEC: Hindi Grammar Error Correction in Low Resource Scenario (2025.coling-main)
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| Challenge: | Automated Grammatical Error Correction (GEC) is a scarcely explored low-resource language . a recent study focused on English, but it focused on Hindi, which presents unique challenges due to its complex syntax and intricate morphology. |
| Approach: | They propose to use a human-edited dataset to generate Hindi GEC data . they also investigate round trip translation using diverse languages for the technique . |
| Outcome: | The proposed method outperforms other methods in Hindi, showing that it is highly efficient. |