Papers with segmentation algorithms
Multi-view Subword Regularization (2021.naacl-main)
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| Challenge: | Existing subword regularization methods for multilingual pretrained representations are suboptimal for multi-lingual transfer. |
| Approach: | They propose a method that enforces consistency between standard and probabilistic segmentations. |
| Outcome: | The proposed method improves the effectiveness of cross-lingual transfer by 2.5 points over standard methods. |
Is Word Segmentation Child’s Play in All Languages? (P19-1)
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| Challenge: | Existing word learning strategies for infants are cross-linguistically robust . infants do not know which language(s) will be found in their environment at the beginning of development . |
| Approach: | They propose to use 11 conceptually diverse algorithms to learn word-like units in infants . they propose to employ cross-linguistically robust algorithms that can be used by all infants. |
| Outcome: | The proposed algorithms perform above chance on 8 different languages . the results show that some of the algorithms are cross-linguistically valid . |
Effects of sub-word segmentation on performance of transformer language models (2023.emnlp-main)
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| Challenge: | Language models are a fundamental task in natural language processing, but few studies focus on the effect of sub-word segmentation on the performance of models. |
| Approach: | They compare GPT and BERT models trained with statistical segmentation algorithm BPE to unsupervised morphological segmentation algorithms Morfessor and StateMorph. |
| Outcome: | The proposed model trains for several languages and compares them with two unsupervised morphological segmentation algorithms. |