| Challenge: | Neural models for morphological reinflection tasks have proved to be extremely accurate given ample labeled data, yet labele d data may be slow and costly to obtain. |
| Approach: | They exploit orthographic and semantic regularities in morphological systems to exploit the orthographic regularities on their own to achieve respectable accuracy. |
| Outcome: | The bootstrapping method outperforms hallucination-based methods for morphological reinflection tasks. |
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(Un)solving Morphological Inflection: Lemma Overlap Artificially Inflates Models’ Performance (2022.acl-short)
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| Challenge: | Inflection tasks have gained a lot of traction in recent years, mostly via SIGMORPHON's shared-tasks. |
| Approach: | They propose to use split-by-lemma to challenge the generalization capacity of morphological inflection models by employing harder train-test splits. |
| Outcome: | The proposed method is based on a split-by-lemma method that challenges the generalization capacity of the models. |
Morphology Without Borders: Clause-Level Morphology (2022.tacl-1)
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| Challenge: | Morphological tasks use large multi-lingual datasets that organize words into inflection tables . lack of a clear linguistic and operational definition of what is a word impairs universality of tasks . |
| Approach: | They propose to view morphology as a clause-level phenomenon, rather than word-level . they propose to use a dataset for clause- level morphological tasks in 4 different languages . |
| Outcome: | The proposed dataset for clause-level morphology covers 4 typologically different languages: English, German, Turkish, and Hebrew. |
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. |
Exploring Linguistic Probes for Morphological Inflection (2023.emnlp-main)
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| Challenge: | morphological inflection models typically employ language-independent data splitting algorithms. |
| Approach: | They propose language-specific probes to test aspects of morphological generalization . they use three morphology-distinct languages to test their generalization abilities . |
| Outcome: | The proposed language-specific probes are used to test morphological generalization abilities on three distinct languages. |
A Computational Model for the Linguistic Notion of Morphological Paradigm (C18-1)
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| Challenge: | In supervised learning of morphological patterns, the strategy of generalizing inflectional tables into more abstract paradigms has been proposed as an efficient method to deduce the inflection of unseen word forms. |
| Approach: | They propose to generalize inflectional tables into more abstract paradigms by aligning the longest common subsequence found in an inflection table with the longest lexeme. |
| Outcome: | The proposed method matches linguist intuitions about what an inflectional paradigm is and can reconstruct missing inflections and generalize and group the witnessed patterns into a model of more abstract paradigmatic behavior of lexemes. |
Pushing the Limits of Low-Resource Morphological Inflection (D19-1)
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| 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 . |
Bootstrapping Techniques for Polysynthetic Morphological Analysis (2020.acl-main)
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| Challenge: | Polysynthetic languages have exceptionally large and sparse vocabularies due to the number of morpheme slots and combinations in a word. |
| Approach: | They propose linguistically-informed approaches for bootstrapping a neural morphological analyzer . they use a finite state transducer to train an encoder-decoder model . |
| Outcome: | The proposed method improves on a polysynthetic language's model by "hallucinating" missing linguistic structure and resampling from a Zipf distribution to simulate a more natural distribution of morphemes. |
IGT2P: From Interlinear Glossed Texts to Paradigms (2020.emnlp-main)
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| Challenge: | Existing systems for learning morphology have limited their use to languages with publicly available structured data, such as online dictionaries like Wiktionary. |
| Approach: | They propose a task that generates entire morphological paradigms from IGT input and a language expert cleaning noisy IGT data. |
| Outcome: | The proposed task speeds up the process and generates entire morphological paradigm tables from IGT input. |
Expanding Abbreviations in a Strongly Inflected Language: Are Morphosyntactic Tags Sufficient? (L18-1)
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| Challenge: | In this paper, the problem of recovery of morphological information lost in abbreviated forms is addressed . correct inflected form of expanded abbrevation can be deduced from context words . |
| Approach: | They propose a deep bidirectional LSTM network with tag embedding to predict abbreviated words . they train on 10 million words from the Polish Sejm Corpus and achieve 74.2% prediction accuracy . |
| Outcome: | The proposed model achieves 74.2% accuracy on a smaller but more general corpus of Polish words. |
Morphological Inflection with Phonological Features (2023.acl-short)
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| Challenge: | Recent advances in morphological tasks can be difficult to solve when little training data is available or when generalizing to previously unseen lemmas. |
| Approach: | They propose two methods to manipulate phonemic data to include phonological features instead of characters. |
| Outcome: | The proposed methods yield comparable results to baseline models, with minor improvements in some languages. |