How Suitable Are Subword Segmentation Strategies for Translating Non-Concatenative Morphology? (2021.findings-emnlp)
Copied to clipboard
| Challenge: | Data-driven subword segmentation is the default strategy for open-vocabulary machine translation but may not be sufficiently generic for learning non-concatenative morphology. |
| Approach: | They propose to test data-driven subword segmentation on non-concatenative morphological phenomena in a controlled, semi-synthetic setting. |
| Outcome: | The proposed model can translate non-concatenative morphological phenomena in a controlled, semi-synthetic setting. |
Similar Papers
When is Char Better Than Subword: A Systematic Study of Segmentation Algorithms for Neural Machine Translation (2021.acl-short)
Copied to clipboard
| Challenge: | Subword segmentation algorithms can produce sub-optimal segmentation when the target language is rich in morphological changes or there is not enough data for learning compact composition rules. |
| Approach: | They compare character-based and subword-based neural machine translation systems . they find character-driven models are better at handling morphological phenomena . |
| Outcome: | The character-based models are better at handling morphological phenomena, generating rare and unknown words, and more suitable for transferring to unseen domains. |
Unlike “Likely”, “Unlike” is Unlikely: BPE-based Segmentation hurts Morphological Derivations in LLMs (2025.coling-main)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) use subword vocabularies to process and generate text. |
| Approach: | They find that Large Language Models (LLMs) perform poorly at handling some types of affixations because subwords are marked as initial- or intra-word . |
| Outcome: | The largest models trained on enough data can mitigate this tendency because initial- and intra-word embeddings are aligned; in-context learning also helps when all examples are selected in a consistent way; but only morphological segmentation can achieve a near-perfect accuracy. |
The Effectiveness of Morphology-aware Segmentation in Low-Resource Neural Machine Translation (2021.eacl-srw)
Copied to clipboard
| Challenge: | Current NMT systems typically operate at the level of subwords, causing problems of vocabulary sparsity. |
| Approach: | They compare subword segmentation methods with morphologically-based methods in a low-resource setting . they find that no consistent and reliable differences emerge between the methods . |
| Outcome: | The proposed methods outperform BPE in a low-resource translation setting. |
A Systematic Study of Leveraging Subword Information for Learning Word Representations (N19-1)
Copied to clipboard
| 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. |
LLMSegm: Surface-level Morphological Segmentation Using Large Language Model (2024.lrec-main)
Copied to clipboard
| Challenge: | Existing approaches to morphological segmentation split word into its morphemes . LLMSegm is applicable in low-data settings and low-resourced languages . |
| Approach: | They propose a novel approach to surface-level morphological segmentation leveraging large language models. |
| Outcome: | The proposed method is applicable in low-data settings and low-resource languages. |
A Morphology-Based Investigation of Positional Encodings (2024.emnlp-main)
Copied to clipboard
| Challenge: | Contemporary deep learning models handle languages with diverse morphology . morphological complexity of languages is closely linked with positional encodings . |
| Approach: | They propose to use positional encodings to integrate morphological complexity into deep learning models. |
| Outcome: | The proposed model improves on 22 languages and 5 downstream tasks. |
Exploring morphology-aware tokenization: A case study on Spanish language modeling (2025.emnlp-main)
Copied to clipboard
| Challenge: | a recent study shows that subword tokenization improves performance of neural language models. |
| Approach: | They propose a linguistically grounded approach to train a tokenizer on morphologically segmented data. |
| Outcome: | The proposed tokenizer improves on a Spanish language model with morphological information. |
BPE vs. Morphological Segmentation: A Case Study on Machine Translation of Four Polysynthetic Languages (2022.findings-acl)
Copied to clipboard
| Challenge: | Morphologically rich polysynthetic languages present a challenge for NLP systems due to data sparsity. |
| Approach: | They propose to use subword segmentation to reduce data sparsity in polysynthetic languages . they compare supervised and unsupervised morphological segmentation methods to Byte-Pair Encodings . |
| Outcome: | The proposed methods outperform BPEs in MT tasks for all language pairs except for Nahuatl . the proposed methods are more efficient than supervised methods, but less sparse in fusional languages. |
TAMS: Translation-Assisted Morphological Segmentation (2024.acl-long)
Copied to clipboard
| Challenge: | Canonical morphological segmentation is a key task in endangered language documentation . training data for canonical segmentation can be difficult, making it difficult to train high quality models. |
| Approach: | They propose a model that leverages translation data to speed up canonical segmentation . they propose to use translation data as an additional signal to leverage the data . |
| Outcome: | The proposed model outperforms baseline models in a super-low resource setting but yields mixed results on training splits with more data. |
Morphological Processing of Low-Resource Languages: Where We Are and What’s Next (2022.findings-acl)
Copied to clipboard
Adam Wiemerslage, Miikka Silfverberg, Changbing Yang, Arya McCarthy, Garrett Nicolai, Eliana Colunga, Katharina Kann
| Challenge: | Existing models for morphological processing are not suitable for low-resource languages, but they are still lacking in the field of computational morphology. |
| Approach: | They propose to bridge two unsupervised models to understand a language’s morphology from raw text alone and propose to use them to improve their models. |
| Outcome: | The proposed models perform reasonably, but there is room for improvement. |