Papers with finite-state transducer
BabyFST - Towards a Finite-State Based Computational Model of Ancient Babylonian (2020.lrec-1)
Copied to clipboard
| Challenge: | morphological analyzer for Akkadian is not yet available for the extinct language . we present a general finite-state based model for Babylonian that can achieve a coverage of 97.3% and a recall of 93.7% on token level. |
| Approach: | They propose a general finite-state based morphological model for Babylonian that can achieve a coverage of 97.3% and recall up to 93.7% on lemmatization and POS-tagging tasks. |
| Outcome: | The proposed model can achieve coverage and recall of 97.3% on lemmatization and POS-tagging tasks on token level from a transcribed input. |
SPOT: Zero-Shot Semantic Parsing Over Property Graphs (2025.findings-acl)
Copied to clipboard
| Challenge: | Knowledge Graphs (KGs) are becoming increasingly popular as a means of storing structured data. |
| Approach: | They propose a method to generate training data for semantic parsing over Property Graphs without human annotations by matching tree patterns to the KG and paraphrasing the query program with an LLM. |
| Outcome: | The proposed method generates training data for parsing over Property Graphs without human annotations on two property graph benchmarks utilizing the Cypher query language. |
Burmese Speech Corpus, Finite-State Text Normalization and Pronunciation Grammars with an Application to Text-to-Speech (2020.lrec-1)
Copied to clipboard
Yin May Oo, Theeraphol Wattanavekin, Chenfang Li, Pasindu De Silva, Supheakmungkol Sarin, Knot Pipatsrisawat, Martin Jansche, Oddur Kjartansson, Alexander Gutkin
| Challenge: | Using crowd-sourced speech corpus and finite-state transducer grammars, we build a text-to-speech system for Burmese, a tonal Southeast Asian language from the Sino-Tibetan family. |
| Approach: | They propose an open-source crowd-sourced multi-speaker speech corpus and finite-state grammars for performing grapheme-to-phoneme conversion for Burmese. |
| Outcome: | The proposed system performs well for Burmese in a low-resource setting. |
FSTs vs ICL: Generalisation in LLMs for an under-resourced language (2025.findings-emnlp)
Copied to clipboard
| Challenge: | LLMs have been widely adopted to tackle many traditional NLP tasks, but their effectiveness remains uncertain in scenarios where pre-trained models have limited prior knowledge of a language. |
| Approach: | They propose a rule-based method using a finite-state transducer and an in-context learning method that provides the model with string transduction examples. |
| Outcome: | The proposed method outperforms FSTs in zero-shot settings while ICL surpasses FLMs. |