---
title: English Translation of Thirukural
url: https://www.ml-quant.com/papers/repec/bcp-journl-v-8-y-2024-i-3s-p-5936-5949/
site: ML-Quant (https://www.ml-quant.com)
updated: 2026-09-26
license: Summaries CC BY 4.0; links go to the original sources
index: https://www.ml-quant.com/llms.txt
identifier: RePEc:bcp:journl:v:8:y:2024:i:3s:p:5936-5949
source_url: https://econpapers.repec.org/scripts/redir.pf?u=https%3A%2F%2Fwww.rsisinternational.org%2Fjournals%2Fijriss%2FDigital-Library%2Fvolume-8-issue-3s%2F5936-5949.pdf%3Bh%3Drepec%3Abcp%3Ajournl%3Av%3A8%3Ay%3A2024%3Ai%3A3s%3Ap%3A5936-5949
featured: 2025-02-05
citations: unknown
topic: Other
---


# English Translation of Thirukural

The paper compares the accuracy of Microsoft Translation and Human Translation in translating Thirukural, an ancient Tamil text, into English.

- Source: https://econpapers.repec.org/scripts/redir.pf?u=https%3A%2F%2Fwww.rsisinternational.org%2Fjournals%2Fijriss%2FDigital-Library%2Fvolume-8-issue-3s%2F5936-5949.pdf%3Bh%3Drepec%3Abcp%3Ajournl%3Av%3A8%3Ay%3A2024%3Ai%3A3s%3Ap%3A5936-5949
- Identifier: RePEc:bcp:journl:v:8:y:2024:i:3s:p:5936-5949
- Released: 2024-03-11
- First featured: Quant Letter No. 84 (2025-02-05): https://www.ml-quant.com/issues/2025-02-05/
- Citations (Semantic Scholar): not tracked
- Published in: not yet
- Topic: Other

## Related

- [Depth Anything V2](https://www.ml-quant.com/papers/arxiv/2406.09414/): Depth Anything V2 is a new model for monocular depth estimation, using synthetic and large-scale pseudo-labeled real images for faster, more accurate results and setting a new evaluation benchmark.
- [MMLU-Pro: A More Robust and Challenging Multi-Task Language Understanding Benchmark](https://www.ml-quant.com/papers/arxiv/2406.01574/): MMLU-Pro, an improved dataset, expands the Massive Multitask Language Understanding benchmark by adding tougher questions and more choices, serving as a better benchmark to monitor progress in the field.
- [Qwen2.5-Coder Technical Report](https://www.ml-quant.com/papers/arxiv/2409.12186/): The report unveils the Qwen2.5-Coder series, an improvement from its predecessor, showcasing remarkable code generation abilities and achieving top-tier performance in various code-related tasks.
- [Ego-Exo4D: Understanding Skilled Human Activity from First- and Third-Person Perspectives](https://www.ml-quant.com/papers/arxiv/2311.18259/): Understanding Human Activity: The paper presents Ego-Exo4D, a large-scale video dataset and benchmark challenge featuring human activities from various perspectives, aimed at improving first-person video understanding.
- [Real-time Photorealistic Dynamic Scene Representation and Rendering with 4D Gaussian Splatting](https://www.ml-quant.com/papers/arxiv/2310.10642/): The 4DGS model is introduced, capable of reconstructing dynamic 3D scenes from 2D images and generating diverse views over time, providing real-time rendering efficiency.
- [Continuous 3D Perception Model with Persistent State](https://www.ml-quant.com/papers/arxiv/2501.12387/): The paper presents CUT3R, a unified framework that uses a recurrent model to generate metric-scale pointmaps from a stream of images, enabling dense scene reconstruction that updates with new images.
