@inproceedings{ekanayaka-sumanathilaka-2026-character,
title = "A Character-Level Neural Approach to {S}inhala Sandhi Splitting",
author = "Ekanayaka, Yasas and
Sumanathilaka, Deshan",
editor = "Zhang, Yue and
Xu, Ruifeng and
Purwarianti, Ayu and
Rong, Tong and
Suzuki, Jun",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {IJCNLP}-{AACL} 2026",
month = nov,
year = "2026",
address = "Hengqin, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-aacl.14/",
pages = "257--266",
ISBN = "979-8-89176-444-6",
abstract = "Sinhala Sandhi splitting recovers the constituent words or morphemes hidden inside a phonologically merged surface form. The task is important for Sinhala NLP because Sandhi obscures lexical boundaries, but no prior published work has established a neural benchmark for Sinhala Sandhi splitting. We present a character-level sequence-to-sequence study based on SandhiLex, using native Sinhala Unicode input and evaluating recurrent encoder-decoder models for affixational and more complex lexicalized, derivational, and etymological Sandhi. A bidirectional LSTM encoder with a unidirectional LSTM decoder yields the best results on the hard subset, achieving 68.40{\%} exact-match accuracy and 82.08{\%} character-level accuracy. On the full affixational subset, the same model achieves 94.00{\%} exact-match accuracy. Ablations show that bidirectional encoding is the largest contributor to performance, while native Sinhala script improves exact match accuracy over romanized input. Qualitative analysis indicates that many errors are near misses involving boundary adjacent characters or plausible but incorrect phonological substitutions. These results establish an empirical baseline for Sinhala Sandhi splitting and identify data scale, Sandhi type conditioning, and attention-based decoding as the main directions for future work."
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<abstract>Sinhala Sandhi splitting recovers the constituent words or morphemes hidden inside a phonologically merged surface form. The task is important for Sinhala NLP because Sandhi obscures lexical boundaries, but no prior published work has established a neural benchmark for Sinhala Sandhi splitting. We present a character-level sequence-to-sequence study based on SandhiLex, using native Sinhala Unicode input and evaluating recurrent encoder-decoder models for affixational and more complex lexicalized, derivational, and etymological Sandhi. A bidirectional LSTM encoder with a unidirectional LSTM decoder yields the best results on the hard subset, achieving 68.40% exact-match accuracy and 82.08% character-level accuracy. On the full affixational subset, the same model achieves 94.00% exact-match accuracy. Ablations show that bidirectional encoding is the largest contributor to performance, while native Sinhala script improves exact match accuracy over romanized input. Qualitative analysis indicates that many errors are near misses involving boundary adjacent characters or plausible but incorrect phonological substitutions. These results establish an empirical baseline for Sinhala Sandhi splitting and identify data scale, Sandhi type conditioning, and attention-based decoding as the main directions for future work.</abstract>
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%0 Conference Proceedings
%T A Character-Level Neural Approach to Sinhala Sandhi Splitting
%A Ekanayaka, Yasas
%A Sumanathilaka, Deshan
%Y Zhang, Yue
%Y Xu, Ruifeng
%Y Purwarianti, Ayu
%Y Rong, Tong
%Y Suzuki, Jun
%S Findings of the Association for Computational Linguistics: IJCNLP-AACL 2026
%D 2026
%8 November
%I Association for Computational Linguistics
%C Hengqin, China
%@ 979-8-89176-444-6
%F ekanayaka-sumanathilaka-2026-character
%X Sinhala Sandhi splitting recovers the constituent words or morphemes hidden inside a phonologically merged surface form. The task is important for Sinhala NLP because Sandhi obscures lexical boundaries, but no prior published work has established a neural benchmark for Sinhala Sandhi splitting. We present a character-level sequence-to-sequence study based on SandhiLex, using native Sinhala Unicode input and evaluating recurrent encoder-decoder models for affixational and more complex lexicalized, derivational, and etymological Sandhi. A bidirectional LSTM encoder with a unidirectional LSTM decoder yields the best results on the hard subset, achieving 68.40% exact-match accuracy and 82.08% character-level accuracy. On the full affixational subset, the same model achieves 94.00% exact-match accuracy. Ablations show that bidirectional encoding is the largest contributor to performance, while native Sinhala script improves exact match accuracy over romanized input. Qualitative analysis indicates that many errors are near misses involving boundary adjacent characters or plausible but incorrect phonological substitutions. These results establish an empirical baseline for Sinhala Sandhi splitting and identify data scale, Sandhi type conditioning, and attention-based decoding as the main directions for future work.
%U https://aclanthology.org/2026.findings-aacl.14/
%P 257-266
Markdown (Informal)
[A Character-Level Neural Approach to Sinhala Sandhi Splitting](https://aclanthology.org/2026.findings-aacl.14/) (Ekanayaka & Sumanathilaka, Findings 2026)
ACL