A Graph Auto-encoder Model of Derivational Morphology

Hofmann, Valentin; Schütze, Hinrich; Pierrehumbert, Janet B.

doi:10.18653/v1/2020.acl-main.106

Cited by 5 publications

(2 citation statements)

References 32 publications

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“…Graph Neural Networks (GNNs) have been used to address many problems that are inherently graph-like such as traffic networks, social networks, and physical and biological systems (Liu and Zhou, 2020). GNNs achieve impressive performance in many domains, including social networks (Wu et al, 2020) and natural science (Sanchez-Gonzalez et al, 2018) as well as NLP tasks like sentence classification (Huang et al, 2020), question generation (Pan et al, 2020), summarization (Fernandes et al, 2019) and derivational morphology (Hofmann et al, 2020). 7 github.com/robertostling/eflomal…”

Section: Annotation Projectionmentioning

confidence: 99%

Graph Neural Networks for Multiparallel Word Alignment

Imani,

Şenel,

Sabet

et al. 2022

Preprint

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After a period of decrease, interest in word alignments is increasing again for their usefulness in domains such as typological research, cross-lingual annotation projection and machine translation. Generally, alignment algorithms only use bitext and do not make use of the fact that many parallel corpora are multiparallel. Here, we compute high-quality word alignments between multiple language pairs by considering all language pairs together. First, we create a multiparallel word alignment graph, joining all bilingual word alignment pairs in one graph. Next, we use graph neural networks (GNNs) to exploit the graph structure. Our GNN approach (i) utilizes information about the meaning, position and language of the input words, (ii) incorporates information from multiple parallel sentences, (iii) adds and removes edges from the initial alignments, and (iv) yields a prediction model that can generalize beyond the training sentences. We show that community detection provides valuable information for multiparallel word alignment. Our method outperforms previous work on three word alignment datasets and on a downstream task.

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Section: Annotation Projectionmentioning

confidence: 99%

Graph Neural Networks for Multiparallel Word Alignment

Imani,

Şenel,

Sabet

et al. 2022

Preprint

Self Cite

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“…In addition to the morphological analysis and generation tasks introduced above centering mainly on inflection, there are tasks involving derivational morphology as well (Cotterell et al, 2017d;Vylomova et al, 2017;Deutsch et al, 2018;Hofmann et al, 2020b;Hofmann et al, 2020a), though derivation is much less studied than inflection in computational morphology.…”

Section: Other Tasksmentioning

confidence: 99%

Computational Morphology with Neural Network Approaches

Liu¹

2021

Preprint

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Neural network approaches have been applied to computational morphology with great success, improving the performance of most tasks by a large margin and providing new perspectives for modeling. This paper starts with a brief introduction to computational morphology, followed by a review of recent work on computational morphology with neural network approaches, to provide an overview of the area. In the end, we will analyze the advantages and problems of neural network approaches to computational morphology, and point out some directions to be explored by future research and study.

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Predicting the Growth of Morphological Families from Social and Linguistic Factors

Hofmann¹,

Pierrehumbert²,

Schütze³

2020

Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

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We present the first study that examines the evolution of morphological families, i.e., sets of morphologically related words such as "trump", "antitrumpism", and "detrumpify", in social media. We introduce the novel task of Morphological Family Expansion Prediction (MFEP) as predicting the increase in the size of a morphological family. We create a ten-year Reddit corpus as a benchmark for MFEP and evaluate a number of baselines on this benchmark. Our experiments demonstrate very good performance on MFEP.

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A Graph Auto-encoder Model of Derivational Morphology

Cited by 5 publications

References 32 publications

Graph Neural Networks for Multiparallel Word Alignment

Graph Neural Networks for Multiparallel Word Alignment

Computational Morphology with Neural Network Approaches

Predicting the Growth of Morphological Families from Social and Linguistic Factors

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