Papers with Morfessor
Morphology Matters: A Multilingual Language Modeling Analysis (2021.tacl-1)
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| Challenge: | Existing studies on inflectional morphology disagree on whether or not it makes languages harder to model. |
| Approach: | They propose to use a corpus of 145 Bible translations in 92 languages to investigate whether inflectional morphology makes languages harder to model. |
| Outcome: | The proposed model trains with linguistically motivated subword segmentation strategies and reduces the impact of morphology on language modeling. |
A Systematic Study of Leveraging Subword Information for Learning Word Representations (N19-1)
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| Challenge: | Existing word representation models for morphologically rich languages use subword-level information, but their systematic comparative analysis across typologically diverse languages and tasks is still missing. |
| Approach: | They propose a framework for learning subword-informed word representations that allows for easy experimentation with different segmentation and composition components. |
| Outcome: | The proposed framework allows for easy experimentation with different segmentation and composition components, as well as advanced techniques based on position embeddings and self-attention. |
Lexically Grounded Subword Segmentation (2024.emnlp-main)
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| Challenge: | Statistical word segmentation algorithms have remained a thorn in the side of many researchers. |
| Approach: | They propose to use unsupervised morphological analysis with Morfessor as pre-tokenization and an algebraic method for obtaining subword embeddings grounded in a word embeddable space. |
| Outcome: | The proposed methods improve morphological plausibility and Rényi efficiency on part-of-speech tagging and machine translation tasks. |
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. |