arXiv subjects

Shruti Tyagi

Publications and source records attributed to Shruti Tyagi.

3 recordsLinked to original sources

arXiv2026

A Systematic Review of NLP for Ghanaian Languages: Datasets, Models, and a Research Roadmap

Natural Language Processing (NLP) for Ghana's 73 living indigenous languages remains deeply fragmented, under-resourced, and heavily skewed toward a single language. We present the first systematic review of the Ghanaian NLP landscape, screening 17,000+ publications across four academic databases to critically synthesize 36 core studies spanning datasets, model architectures, and evaluation paradigms. Our analysis exposes a severe resource imbalance: Twi-centric NLP has grown modestly, driven largely by religious-text alignment and crowdsourcing, while the remaining 70+ languages remain almost entirely unaddressed, and dataset releases, model checkpoints, and evaluation practices remain inconsistent and rarely shared across the field. We translate these findings into a prioritized roadmap targeting Ghana's acute regional constraints, dialectal variation, non-standardized orthographies, and the absence of shared infrastructure, offering a replicable template for systematic review and research prioritization in other low-resource language settings.

cs.CL↗

arXiv2026

PLanet: Formalizing and Analyzing Assignment Procedures in the Design of Experiments

Experimental designs reflect assumptions about variable relationships that determine what causal queries researchers can answer through the experiment. Accounting for and communicating these assumptions is essential for drawing valid, generalizable conclusions from scientific experiments. Unfortunately, existing experimental design tools elide these details, expecting researchers to reason about design decisions and assumptions on their own. To surface assumptions and enable design exploration, we introduce a grammar of composable operators for constructing experimental assignment procedures grounded in matrix algebra. The PLanet DSL implements this grammar and compiles PLanet programs into constraint satisfaction problems over matrices. Together, PLanet's composable grammar and matrix representation enable a static analysis to determine which causal queries are testable under different assumptions. In an expressivity evaluation, PLanet was the most expressive of existing DSLs. Critical reflections with the authors of these DSLs revealed that PLanet makes design choices explicit without requiring procedural specification. Think-aloud studies showed that PLanet facilitated design exploration and surfaced assumptions researchers may otherwise overlook.

cs.HC↗

arXiv2015

Classifier-Based Text Simplification for Improved Machine Translation

Machine Translation is one of the research fields of Computational Linguistics. The objective of many MT Researchers is to develop an MT System that produce good quality and high accuracy output translations and which also covers maximum language pairs. As internet and Globalization is increasing day by day, we need a way that improves the quality of translation. For this reason, we have developed a Classifier based Text Simplification Model for English-Hindi Machine Translation Systems. We have used support vector machines and Naïve Bayes Classifier to develop this model. We have also evaluated the performance of these classifiers.

cs.CL↗