The Experts below are selected from a list of 3063 Experts worldwide ranked by ideXlab platform
Manaal Faruqui - One of the best experts on this subject based on the ideXlab platform.
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CoNLL-SIGMORPHON 2017 Shared Task: Universal Morphological Reinflection in 52 Languages
arXiv: Computation and Language, 2017Co-Authors: Ryan Cotterell, Manaal Faruqui, Christo Kirov, John Sylak-glassman, Géraldine Walther, Ekaterina Vylomova, Patrick Xia, Sandra Kübler, David Yarowsky, Jason EisnerAbstract:The CoNLL-SIGMORPHON 2017 shared task on supervised morphological generation required systems to be trained and tested in each of 52 typologically diverse languages. In sub-task 1, submitted systems were asked to predict a specific Inflected Form of a given lemma. In sub-task 2, systems were given a lemma and some of its specific Inflected Forms, and asked to complete the inflectional paradigm by predicting all of the remaining Inflected Forms. Both sub-tasks included high, medium, and low-resource conditions. Sub-task 1 received 24 system submissions, while sub-task 2 received 3 system submissions. Following the success of neural sequence-to-sequence models in the SIGMORPHON 2016 shared task, all but one of the submissions included a neural component. The results show that high perFormance can be achieved with small training datasets, so long as models have appropriate inductive bias or make use of additional unlabeled data or synthetic data. However, different biasing and data augmentation resulted in disjoint sets of Inflected Forms being predicted correctly, suggesting that there is room for future improvement.
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HLT-NAACL - Morphological Inflection Generation Using Character Sequence to Sequence Learning
Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2016Co-Authors: Manaal Faruqui, Yulia Tsvetkov, Graham Neubig, Chris DyerAbstract:Morphological inflection generation is the task of generating the Inflected Form of a given lemma corresponding to a particular linguistic transFormation. We model the problem of inflection generation as a character sequence to sequence learning problem and present a variant of the neural encoder-decoder model for solving it. Our model is language independent and can be trained in both supervised and semi-supervised settings. We evaluate our system on seven datasets of morphologically rich languages and achieve either better or comparable results to existing state-of-the-art models of inflection generation.
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Morphological Inflection Generation Using Character Sequence to Sequence Learning
arXiv: Computation and Language, 2015Co-Authors: Manaal Faruqui, Yulia Tsvetkov, Graham Neubig, Chris DyerAbstract:Morphological inflection generation is the task of generating the Inflected Form of a given lemma corresponding to a particular linguistic transFormation. We model the problem of inflection generation as a character sequence to sequence learning problem and present a variant of the neural encoder-decoder model for solving it. Our model is language independent and can be trained in both supervised and semi-supervised settings. We evaluate our system on seven datasets of morphologically rich languages and achieve either better or comparable results to existing state-of-the-art models of inflection generation.
Chris Dyer - One of the best experts on this subject based on the ideXlab platform.
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HLT-NAACL - Morphological Inflection Generation Using Character Sequence to Sequence Learning
Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2016Co-Authors: Manaal Faruqui, Yulia Tsvetkov, Graham Neubig, Chris DyerAbstract:Morphological inflection generation is the task of generating the Inflected Form of a given lemma corresponding to a particular linguistic transFormation. We model the problem of inflection generation as a character sequence to sequence learning problem and present a variant of the neural encoder-decoder model for solving it. Our model is language independent and can be trained in both supervised and semi-supervised settings. We evaluate our system on seven datasets of morphologically rich languages and achieve either better or comparable results to existing state-of-the-art models of inflection generation.
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Morphological Inflection Generation Using Character Sequence to Sequence Learning
arXiv: Computation and Language, 2015Co-Authors: Manaal Faruqui, Yulia Tsvetkov, Graham Neubig, Chris DyerAbstract:Morphological inflection generation is the task of generating the Inflected Form of a given lemma corresponding to a particular linguistic transFormation. We model the problem of inflection generation as a character sequence to sequence learning problem and present a variant of the neural encoder-decoder model for solving it. Our model is language independent and can be trained in both supervised and semi-supervised settings. We evaluate our system on seven datasets of morphologically rich languages and achieve either better or comparable results to existing state-of-the-art models of inflection generation.
Simon Clematide - One of the best experts on this subject based on the ideXlab platform.
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Align and Copy: UZH at SIGMORPHON 2017 Shared Task for Morphological Reinflection
arXiv: Computation and Language, 2017Co-Authors: Peter Makarov, Tatiana Ruzsics, Simon ClematideAbstract:This paper presents the submissions by the University of Zurich to the SIGMORPHON 2017 shared task on morphological reinflection. The task is to predict the Inflected Form given a lemma and a set of morpho-syntactic features. We focus on neural network approaches that can tackle the task in a limited-resource setting. As the transduction of the lemma into the Inflected Form is dominated by copying over lemma characters, we propose two recurrent neural network architectures with hard monotonic attention that are strong at copying and, yet, substantially different in how they achieve this. The first approach is an encoder-decoder model with a copy mechanism. The second approach is a neural state-transition system over a set of explicit edit actions, including a designated COPY action. We experiment with character alignment and find that naive, greedy alignment consistently produces strong results for some languages. Our best system combination is the overall winner of the SIGMORPHON 2017 Shared Task 1 without external resources. At a setting with 100 training samples, both our approaches, as ensembles of models, outperForm the next best competitor.
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CoNLL Shared Task (1) - Align and Copy: UZH at SIGMORPHON 2017 Shared Task for Morphological Reinflection
Proceedings of the CoNLL SIGMORPHON 2017 Shared Task: Universal Morphological Reinflection, 2017Co-Authors: Peter Makarov, Tatiana Ruzsics, Simon ClematideAbstract:This paper presents the submissions by the University of Zurich to the SIGMORPHON 2017 shared task on morphological reinflection. The task is to predict the Inflected Form given a lemma and a set of morpho-syntactic features. We focus on neural network approaches that can tackle the task in a limited-resource setting. As the transduction of the lemma into the Inflected Form is dominated by copying over lemma characters, we propose two recurrent neural network architectures with hard monotonic attention that are strong at copying and, yet, substantially different in how they achieve this. The first approach is an encoder-decoder model with a copy mechanism. The second approach is a neural state-transition system over a set of explicit edit actions, including a designated COPY action. We experiment with character alignment and find that naive, greedy alignment consistently produces strong results for some languages. Our best system combination is the overall winner of the SIGMORPHON 2017 Shared Task 1 without external resources. At a setting with 100 training samples, both our approaches, as ensembles of models, outperForm the next best competitor.
Peter Makarov - One of the best experts on this subject based on the ideXlab platform.
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Align and Copy: UZH at SIGMORPHON 2017 Shared Task for Morphological Reinflection
arXiv: Computation and Language, 2017Co-Authors: Peter Makarov, Tatiana Ruzsics, Simon ClematideAbstract:This paper presents the submissions by the University of Zurich to the SIGMORPHON 2017 shared task on morphological reinflection. The task is to predict the Inflected Form given a lemma and a set of morpho-syntactic features. We focus on neural network approaches that can tackle the task in a limited-resource setting. As the transduction of the lemma into the Inflected Form is dominated by copying over lemma characters, we propose two recurrent neural network architectures with hard monotonic attention that are strong at copying and, yet, substantially different in how they achieve this. The first approach is an encoder-decoder model with a copy mechanism. The second approach is a neural state-transition system over a set of explicit edit actions, including a designated COPY action. We experiment with character alignment and find that naive, greedy alignment consistently produces strong results for some languages. Our best system combination is the overall winner of the SIGMORPHON 2017 Shared Task 1 without external resources. At a setting with 100 training samples, both our approaches, as ensembles of models, outperForm the next best competitor.
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CoNLL Shared Task (1) - Align and Copy: UZH at SIGMORPHON 2017 Shared Task for Morphological Reinflection
Proceedings of the CoNLL SIGMORPHON 2017 Shared Task: Universal Morphological Reinflection, 2017Co-Authors: Peter Makarov, Tatiana Ruzsics, Simon ClematideAbstract:This paper presents the submissions by the University of Zurich to the SIGMORPHON 2017 shared task on morphological reinflection. The task is to predict the Inflected Form given a lemma and a set of morpho-syntactic features. We focus on neural network approaches that can tackle the task in a limited-resource setting. As the transduction of the lemma into the Inflected Form is dominated by copying over lemma characters, we propose two recurrent neural network architectures with hard monotonic attention that are strong at copying and, yet, substantially different in how they achieve this. The first approach is an encoder-decoder model with a copy mechanism. The second approach is a neural state-transition system over a set of explicit edit actions, including a designated COPY action. We experiment with character alignment and find that naive, greedy alignment consistently produces strong results for some languages. Our best system combination is the overall winner of the SIGMORPHON 2017 Shared Task 1 without external resources. At a setting with 100 training samples, both our approaches, as ensembles of models, outperForm the next best competitor.
Graham Neubig - One of the best experts on this subject based on the ideXlab platform.
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HLT-NAACL - Morphological Inflection Generation Using Character Sequence to Sequence Learning
Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2016Co-Authors: Manaal Faruqui, Yulia Tsvetkov, Graham Neubig, Chris DyerAbstract:Morphological inflection generation is the task of generating the Inflected Form of a given lemma corresponding to a particular linguistic transFormation. We model the problem of inflection generation as a character sequence to sequence learning problem and present a variant of the neural encoder-decoder model for solving it. Our model is language independent and can be trained in both supervised and semi-supervised settings. We evaluate our system on seven datasets of morphologically rich languages and achieve either better or comparable results to existing state-of-the-art models of inflection generation.
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Morphological Inflection Generation Using Character Sequence to Sequence Learning
arXiv: Computation and Language, 2015Co-Authors: Manaal Faruqui, Yulia Tsvetkov, Graham Neubig, Chris DyerAbstract:Morphological inflection generation is the task of generating the Inflected Form of a given lemma corresponding to a particular linguistic transFormation. We model the problem of inflection generation as a character sequence to sequence learning problem and present a variant of the neural encoder-decoder model for solving it. Our model is language independent and can be trained in both supervised and semi-supervised settings. We evaluate our system on seven datasets of morphologically rich languages and achieve either better or comparable results to existing state-of-the-art models of inflection generation.