Unsupervised Morphological Paradigm Completion

Jin, Huiming; Cai, Liwei; Peng, Yihui; Xia, Chen; McCarthy, Arya D.; Kann, Katharina

doi:10.18653/v1/2020.acl-main.598

Cited by 18 publications

(34 citation statements)

References 35 publications

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“…Finally, introduced neural graphical models which completed paradigms based on principal parts. The unsupervised version of the paradigm completion task (Jin et al, 2020) has been the subject of a recent shared task (Kann et al, 2020b), with the conclusion that it is exremely challenging for current state-of-the-art systems. Here, we propose to, instead of generating paradigms from raw text, generate them from IGT, a resource available for many under-studied languages.…”

Section: Related Workmentioning

confidence: 99%

IGT2P: From Interlinear Glossed Texts to Paradigms

Moeller

Liu

Yang³

et al. 2020

Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

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An intermediate step in the linguistic analysis of an under-documented language is to find and organize inflected forms that are attested in natural speech. From this data, linguists generate unseen inflected word forms in order to test hypotheses about the language's inflectional patterns and to complete inflectional paradigm tables. To get the data linguists spend many hours manually creating interlinear glossed texts (IGTs). We introduce a new task that speeds this process and automatically generates new morphological resources for natural language processing systems: IGTto-paradigms (IGT2P). IGT2P generates entire morphological paradigms from IGT input. We show that existing morphological reinflection models can solve the task with 21% to 64% accuracy, depending on the language. We further find that (i) having a language expert spend only a few hours cleaning the noisy IGT data improves performance by as much as 21 percentage points, and (ii) POS tags, which are generally considered a necessary part of NLP morphological reinflection input, have no effect on the accuracy of the models considered here.

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Section: Related Workmentioning

confidence: 99%

IGT2P: From Interlinear Glossed Texts to Paradigms

Moeller

Liu

Yang³

et al. 2020

Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

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“…The objective is to generate the complete paradigms for all lemmas. Our systems for this task consist of a combination of the official baseline system (Jin et al, 2020) and our systems for Task 0. The baseline system finds inflected forms in the text, decides on the number of inflected forms per lemma, and produces pseudo training files for morphological inflection.…”

Section: Lemma Featuresmentioning

confidence: 99%

“…Our systems for Task 2 consist of a combination of the official baseline system (Jin et al, 2020) and our inflection systems for Task 0. The system is given raw text and a source file with lemmas, and generates the complete paradigm of each lemma.…”

Section: Task 2: Model Descriptionmentioning

confidence: 99%

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The NYU-CUBoulder Systems for SIGMORPHON 2020 Task 0 and Task 2

Singer¹,

Kann²

2020

Proceedings of the 17th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology

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We describe the NYU-CUBoulder systems for the SIGMORPHON 2020 Task 0 on typologically diverse morphological inflection and Task 2 on unsupervised morphological paradigm completion. The former consists of generating morphological inflections from a lemma and a set of morphosyntactic features describing the target form. The latter requires generating entire paradigms for a set of given lemmas from raw text alone. We model morphological inflection as a sequenceto-sequence problem, where the input is the sequence of the lemma's characters with morphological tags, and the output is the sequence of the inflected form's characters. First, we apply a transformer model to the task. Second, as inflected forms share most characters with the lemma, we further propose a pointer-generator transformer model to allow easy copying of input characters. Our best performing system for Task 0 is placed 6th out of 23 systems. We further use our inflection systems as subcomponents of approaches for Task 2. Our best performing system for Task 2 is the 2nd best out of 7 submissions.

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“…This is an intriguing research area that could give us the chance of recovering dead languages that have only limited written resources. Several researchers have attempted to solve this task, such as Goldsmith et al (2017), Jin et al (2020), and Erdmann et al (2020).…”

Section: Introductionmentioning

confidence: 99%

KU-CST at the SIGMORPHON 2020 Task 2 on Unsupervised Morphological Paradigm Completion

Agirrezabal¹,

Wedekind²

2020

Proceedings of the 17th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology

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We present a model for the unsupervised discovery of morphological paradigms. The goal of this model is to induce morphological paradigms from the bible (raw text) and a list of lemmas. We have created a model that splits each lemma in a stem and a suffix, and then we try to create a plausible suffix list by considering lemma pairs. Our model was not able to outperform the official baseline, and there is still room for improvement, but we believe that the ideas presented here are worth considering.

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Unsupervised Morphological Paradigm Completion

Cited by 18 publications

References 35 publications

IGT2P: From Interlinear Glossed Texts to Paradigms

IGT2P: From Interlinear Glossed Texts to Paradigms

The NYU-CUBoulder Systems for SIGMORPHON 2020 Task 0 and Task 2

KU-CST at the SIGMORPHON 2020 Task 2 on Unsupervised Morphological Paradigm Completion

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