| Challenge: | Recent advances in morphological inflection generation have limited resources . antonisa and colleagues present a battery of improvements to improve performance under low-resource conditions . |
| Approach: | They propose a two-step attention architecture for the inflection decoder that uses two-segments attention and a multi-single-syllabic attention architecture. |
| Outcome: | The proposed model outperforms the state-of-the-art in low-resource languages by 15 percentage points . the proposed model also shows that it can be used to model monolingual data hallucinations . |
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| Challenge: | Rapid progress in natural language processing (NLP) has largely been driven by training transformer models on massive amounts of unlabeled data, but such large datasets are scarce for many of the world's languages. |
| Approach: | They propose to train encoder-decoder transformers for 19 languages and 13 auxiliary objectives on massive amounts of unlabeled data. |
| Outcome: | The proposed tasks outperform standard CMLM in character-level tasks when available data is limited. |
Getting More Data for Low-resource Morphological Inflection: Language Models and Data Augmentation (2020.lrec-1)
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| Challenge: | Morphological inflection is the process that generates the word form given its lexeme and morphological properties. |
| Approach: | They propose to use language models and data augmentation to improve morphological inflection without annotating more data. |
| Outcome: | The proposed model improves by 1.5% with the langauge model and by 9% with the data augmentation. |
Interpretability for Morphological Inflection: from Character-level Predictions to Subword-level Rules (2021.eacl-main)
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| Challenge: | Neural models for morphological inflection have recently attained very high results, but their interpretation remains challenging. |
| Approach: | They propose a linguistically-motivated variant to the encoder-decoder model with attention that incorporates a character-level cross-attention mechanism and a self-attention module over substrings of the input. |
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Morphological Processing of Low-Resource Languages: Where We Are and What’s Next (2022.findings-acl)
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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. |
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| Outcome: | The proposed models perform reasonably, but there is room for improvement. |
Tackling the Low-resource Challenge for Canonical Segmentation (2020.emnlp-main)
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| Challenge: | morphological segmentation is a task of dividing words into their constituting morphemes . we compare two new approaches for the task when training data is limited . |
| Approach: | They propose to use an LSTM pointer-generator and a sequence-to-sequence model to perform canonical segmentation when training data is limited. |
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Morphological Inflection: A Reality Check (2023.acl-long)
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| Challenge: | Morphological inflection is a popular task in sub-word NLP with practical and cognitive applications. |
| Approach: | They propose new methods to analyze data sets and evaluate their generalization abilities to better reflect likely use-cases. |
| Outcome: | The proposed methods improve generalizability and reliability of results and improve generalization abilities. |
Neural Transition-based String Transduction for Limited-Resource Setting in Morphology (C18-1)
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| Challenge: | Morphological string transduction involves mapping one word form into another, possibly given a feature specification for the mapping. |
| Approach: | They propose a neural transition-based model that uses a simple set of edit actions for morphological transduction tasks such as reinflection and reinflation. |
| Outcome: | The proposed model outperforms state-of-the-art systems on low and medium training-set sizes and is competitive in the high-resource setting. |
Low-resource Neural Machine Translation with Cross-modal Alignment (2022.emnlp-main)
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| Challenge: | Existing neural machine translation techniques rely on large monolingual corpus, which is costly for some low-resource languages. |
| Approach: | They propose a cross-modal contrastive learning method to learn a shared space for all languages by additional visual modality. |
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Neural Transductive Learning and Beyond: Morphological Generation in the Minimal-Resource Setting (D18-1)
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| Challenge: | Existing lexicons have limited coverage for learning morphological inflection patterns from labeled data. |
| Approach: | They propose two new methods to solve paradigm completion, the morphological task of generating missing forms, given a partial paradigm. |
| Outcome: | The proposed methods outperform the previous state-of-the-art by 9.71% absolute accuracy on a 52-language benchmark dataset. |
An Investigation of Noise in Morphological Inflection (2023.findings-acl)
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| Challenge: | Neural morphological inflection systems can be used for languages with very little supervised data, but are often less likely to have clean, goldstandard data. |
| Approach: | They propose an error taxonomy and annotation pipeline for inflection training data and propose a character-level masked language modeling (CMLM) pretraining objective. |
| Outcome: | The proposed pipeline is based on error taxonomy and annotation pipelines for unsupervised morphological paradigm completion. |