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        <title>Exploring Neural Architectures And Techniques For Typologically Diverse Morphological Inflection</title>
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    <abstract>Morphological inflection in low resource languages is critical to augment existing corpora in Low Resource Languages, which can help develop several applications in these languages with very good social impact. We describe our attention-based encoder-decoder approach that we implement using LSTMs and Transformers as the base units. We also describe the ancillary techniques that we experimented with, such as hallucination, language vector injection, sparsemax loss and adversarial language network alongside our approach to select the related language(s) for training. We present the results we generated on the constrained as well as unconstrained SIGMORPHON 2020 dataset (CITATION). One of the primary goals of our paper was to study the contribution varied components described above towards the performance of our system and perform an analysis on the same.</abstract>
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    <identifier type="doi">10.18653/v1/2020.sigmorphon-1.14</identifier>
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%0 Conference Proceedings
%T Exploring Neural Architectures And Techniques For Typologically Diverse Morphological Inflection
%A Jayarao, Pratik
%A Pillay, Siddhanth
%A Thombre, Pranav
%A Chaudhary, Aditi
%S Proceedings of the 17th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology
%D 2020
%8 jul
%I Association for Computational Linguistics
%C Online
%F jayarao-etal-2020-exploring
%X Morphological inflection in low resource languages is critical to augment existing corpora in Low Resource Languages, which can help develop several applications in these languages with very good social impact. We describe our attention-based encoder-decoder approach that we implement using LSTMs and Transformers as the base units. We also describe the ancillary techniques that we experimented with, such as hallucination, language vector injection, sparsemax loss and adversarial language network alongside our approach to select the related language(s) for training. We present the results we generated on the constrained as well as unconstrained SIGMORPHON 2020 dataset (CITATION). One of the primary goals of our paper was to study the contribution varied components described above towards the performance of our system and perform an analysis on the same.
%R 10.18653/v1/2020.sigmorphon-1.14
%U https://aclanthology.org/2020.sigmorphon-1.14
%U https://doi.org/10.18653/v1/2020.sigmorphon-1.14
%P 128-136

Markdown (Informal)

[Exploring Neural Architectures And Techniques For Typologically Diverse Morphological Inflection](https://aclanthology.org/2020.sigmorphon-1.14) (Jayarao et al., SIGMORPHON 2020)

• Exploring Neural Architectures And Techniques For Typologically Diverse Morphological Inflection (Jayarao et al., SIGMORPHON 2020)

ACL

• Pratik Jayarao, Siddhanth Pillay, Pranav Thombre, and Aditi Chaudhary. 2020. Exploring Neural Architectures And Techniques For Typologically Diverse Morphological Inflection. In Proceedings of the 17th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology, pages 128–136, Online. Association for Computational Linguistics.