Vectorizing Classical Tamil: Representation Learning for Verse-Commentary Pairs
We construct a corpus of 1,262 verse--commentary (urai) pairs from five Classical Tamil source sections, ranging from technical grammatical prose to modern paraphrase, and ask what information representation learning can recover. We train recurrent and Transformer encoders, a Siamese-style pair-matching network, an mBART-style encoder--decoder, and a decoder-only language model. Each analysis is interpreted against an appropriate control on the same data. TF-IDF provides a strong no-training lexical retrieval baseline, alongside representation analyses and generation controls for the learned models. A fixed string containing the 25 most frequent commentary words scores higher on generatio...
| Authors | Amrit Gopinath, Sangeetha Sivanesan |
|---|---|
| Primary category | cs.CL |
| Published | 4 September 2026 |
| Last updated at source | 7 September 2026 |
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