Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) 2018
DOI: 10.18653/v1/p18-1247
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Neural Factor Graph Models for Cross-lingual Morphological Tagging

Abstract: Morphological analysis involves predicting the syntactic traits of a word (e.g. {POS: Noun, Case: Acc, Gender: Fem}). Previous work in morphological tagging improves performance for low-resource languages (LRLs) through cross-lingual training with a high-resource language (HRL) from the same family, but is limited by the strict-often false-assumption that tag sets exactly overlap between the HRL and LRL. In this paper we propose a method for cross-lingual morphological tagging that aims to improve information … Show more

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Cited by 23 publications
(49 citation statements)
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“…In line with Malaviya et al (2018), we formulate morphological analysis as a feature-wise sequence prediction task, where we predict the fine-grained labels (e.g N, NOM, ...) for the corresponding coarse-grained features F ={POS,Case,...} as shown in Figure 1. However, we only model the transition dependencies between the labels of a feature.…”
Section: Our Methodsmentioning
confidence: 99%
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“…In line with Malaviya et al (2018), we formulate morphological analysis as a feature-wise sequence prediction task, where we predict the fine-grained labels (e.g N, NOM, ...) for the corresponding coarse-grained features F ={POS,Case,...} as shown in Figure 1. However, we only model the transition dependencies between the labels of a feature.…”
Section: Our Methodsmentioning
confidence: 99%
“…We conduct the following experiments: We compare our multi-lingual transfer approach with the baselines Malaviya et al (2018) and Cotterell and Heigold (2017) under the same experimental settings. Next, we compare our approach with the shared task baseline (McCarthy et al, 2019).…”
Section: Methodsmentioning
confidence: 99%
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“…Future directions for Task 2 include exploring cross-lingual analysis-in stride with both Task 1 and Malaviya et al (2018)-and leveraging these analyses in downstream tasks.…”
Section: Future Directionsmentioning
confidence: 99%
“…We employ a simple LSTM-based tagger to recover the morphology of a sentence (Heigold et al, 2017;Cotterell and Heigold, 2017). We also experimented with the neural conditional random field of Malaviya et al (2018), but Heigold et al (2017) gave slightly better tagging scores on average and is faster to train. Given a sequence of n words w = w 1 , .…”
Section: Morphological Tagger: P(m | W)mentioning
confidence: 99%