| Challenge: | morphologically rich languages require ambiguous analysis to be effective . morphology tools are limited for morphlogical analysis, disambiguation, and annotation . |
| Approach: | They propose to learn to morphologically disambiguate Kinyarwanda verbal forms from a crowd-sourced stemming dataset using feature engineering and a feed-forward neural network based classifier. |
| Outcome: | The proposed method achieves about 89% non-contextualized disambiguation accuracy from a crowd-sourced dataset. |
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Language-Independent Approach for Morphological Disambiguation (2022.coling-1)
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| Challenge: | Existing approaches for predicting complex morphological tags treat each analysis as a tag and apply sequence labeling models to perform tagging. |
| Approach: | They propose a language-independent approach which integrates all words, roots, POS and morpheme tags into vectors and computes the inner products between analyses and the contexts. |
| Outcome: | The proposed approach outperforms existing models on seven different languages while running about 6 and 33 times faster than MarMot and Seq2Seq, respectively. |
Neural Disambiguation of Lemma and Part of Speech in Morphologically Rich Languages (2020.lrec-1)
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| Challenge: | a method for disambiguating the lemma and part of speech of ambiguous words is proposed . a morphological analyser produces multiple analyses for ambiguously words . |
| Approach: | They propose a method for disambiguating the lemma and part of speech of ambiguous words in context . they use a large un-annotated corpus of text and a morphological analyser to train neural networks on the output of the analyser . |
| Outcome: | The proposed method outperforms the state-of-the-art on POS and lemma disambiguation in morphologically rich languages using no manual disambiguations or data annotations. |
KinyaBERT: a Morphology-aware Kinyarwanda Language Model (2022.acl-long)
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| Challenge: | Pre-trained language models such as BERT are sub-optimal at handling morphologically rich languages. |
| Approach: | They propose a two-tier BERT architecture that leverages a morphological analyzer and explicitly represents morphology in a low-resource Kinyarwanda language. |
| Outcome: | The proposed model outperforms baseline models on the low-resource morphologically rich Kinyarwanda language by 2% in F1 score and 4.3% in average score of GLUE benchmark. |
Using Morphological Knowledge in Open-Vocabulary Neural Language Models (N18-1)
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| Challenge: | Existing models that generate words from a fixed vocabulary are linguistically nave . authors present an open-vocabulary language model that incorporates morphological knowledge into a neural framework . |
| Approach: | They propose a model that incorporates morphological knowledge into a neural model by generating words as a sequence of characters, generating full word forms and combining them with a hand-written morphology analyzer. |
| Outcome: | The proposed model outperforms character-based models on Finnish, Turkish, and Russian on three languages. |
Morphology-Aware Meta-Embeddings for Tamil (2021.naacl-srw)
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| Challenge: | In this work, we focus on producing morphologically enhanced word embeddings for Tamil, a highly agglutinative South Indian language with rich morphology that remains low-resource with regards to NLP tasks. |
| Approach: | They present a first-ever word analogy dataset for Tamil using a rules-based segmenter and meta-embedding techniques. |
| Outcome: | The proposed embeddings outperform baselines on the word analogy task by 16% and appear to mitigate a trade-off between semantic and morphological accuracy. |
An Unsupervised Method for Weighting Finite-state Morphological Analyzers (2020.lrec-1)
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| Challenge: | Morphological analysis is one of the tasks that have been studied for years. |
| Approach: | They propose a method for weighting a morphological analyzer built using finite state transducers in order to disambiguate its results. |
| Outcome: | The proposed model weights a word2vec model using untagged corpora and captures the semantic meaning of the words. |
A Pointer Network Architecture for Joint Morphological Segmentation and Tagging (2020.findings-emnlp)
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| Challenge: | Morphological Disambiguation (MD) is a task of decomposing tokens into morphemes . a simple pipeline is used to segment and tagging raw tokens . |
| Approach: | They propose a new pointer network model that combines symbolic knowledge of morphemes with the learning capacity of neural end-to-end modeling. |
| Outcome: | The proposed model outperforms all previous reported results on Hebrew and Turkish . it uses morphological knowledge and the learning capacity of neural end-to-end modeling . |
Confounding Factors in Relating Model Performance to Morphology (2025.emnlp-main)
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| Challenge: | morphological differences between languages are unclear, but are often considered unimportant . confounding factors make it hard to compare results and draw conclusions, authors argue . |
| Approach: | They propose to use token bigram metrics to predict difficulty of causal language modeling . they argue that confounding factors are contributing to the conflicting evidence . |
| Outcome: | The proposed metrics better capture the relation between morphology and tokenization compared to word-based models. |
What do character-level models learn about morphology? The case of dependency parsing (D18-1)
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| Challenge: | morphologically rich languages require character-level input models to learn morphology, but some models are poor at disambiguating some words . authors of this study show that character- level models learn a lot from input input . explicit modeling of morphologies is expensive and expensive, authors say . |
| Approach: | They compare character-level models to an oracle with explicit morphological analysis . they show that explicitly modeling morphology improves their best model . |
| Outcome: | The results show that character-level models learn morphology better than word models . the authors compare character-based models to oracles on 12 languages with morphological typologies . |
Modeling Morphological Typology for Unsupervised Learning of Language Morphology (2020.acl-main)
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| Challenge: | Existing approaches to morphological analysis relied on hand-built rules to identify word-internal structures. |
| Approach: | They propose a language-independent model for fully unsupervised morphological analysis that exploits a universal framework leveraging morphology. |
| Outcome: | The proposed model outperforms existing systems on nine typologically and genetically diverse languages and shows superior performance over leading systems. |