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Winoground: Probing Vision and Language Models for Visio-Linguistic Compositionality ...
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ANLIzing the Adversarial Natural Language Inference Dataset
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In: Proceedings of the Society for Computation in Linguistics (2022)
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Investigating Failures of Automatic Translation in the Case of Unambiguous Gender ...
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On the Relationships Between the Grammatical Genders of Inanimate Nouns and Their Co-Occurring Adjectives and Verbs ...
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On the Relationships Between the Grammatical Genders of Inanimate Nouns and Their Co-Occurring Adjectives and Verbs ...
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Generalising to German Plural Noun Classes, from the Perspective of a Recurrent Neural Network ...
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On the Relationships Between the Grammatical Genders of Inanimate Nouns and Their Co-Occurring Adjectives and Verbs
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In: Transactions of the Association for Computational Linguistics, 9 (2021)
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Masked Language Modeling and the Distributional Hypothesis: Order Word Matters Pre-training for Little ...
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On the Relationships Between the Grammatical Genders of Inanimate Nouns and Their Co-Occurring Adjectives and Verbs ...
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SIGMORPHON 2020 Shared Task 0: Typologically Diverse Morphological Inflection ...
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Vylomova, Ekaterina; White, Jennifer; Salesky, Elizabeth; Mielke, Sabrina J.; Wu, Shijie; Ponti, Edoardo; Maudslay, Rowan Hall; Zmigrod, Ran; Valvoda, Josef; Toldova, Svetlana; Tyers, Francis; Klyachko, Elena; Yegorov, Ilya; Krizhanovsky, Natalia; Czarnowska, Paula; Nikkarinen, Irene; Krizhanovsky, Andrew; Pimentel, Tiago; Hennigen, Lucas Torroba; Kirov, Christo; Nicolai, Garrett; Williams, Adina; Anastasopoulos, Antonios; Cruz, Hilaria; Chodroff, Eleanor; Cotterell, Ryan; Silfverberg, Miikka; Hulden, Mans. - : arXiv, 2020
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Abstract:
A broad goal in natural language processing (NLP) is to develop a system that has the capacity to process any natural language. Most systems, however, are developed using data from just one language such as English. The SIGMORPHON 2020 shared task on morphological reinflection aims to investigate systems' ability to generalize across typologically distinct languages, many of which are low resource. Systems were developed using data from 45 languages and just 5 language families, fine-tuned with data from an additional 45 languages and 10 language families (13 in total), and evaluated on all 90 languages. A total of 22 systems (19 neural) from 10 teams were submitted to the task. All four winning systems were neural (two monolingual transformers and two massively multilingual RNN-based models with gated attention). Most teams demonstrate utility of data hallucination and augmentation, ensembles, and multilingual training for low-resource languages. Non-neural learners and manually designed grammars showed ... : 39 pages, SIGMORPHON ...
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Keyword:
Computation and Language cs.CL; FOS Computer and information sciences
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URL: https://dx.doi.org/10.48550/arxiv.2006.11572 https://arxiv.org/abs/2006.11572
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Measuring the Similarity of Grammatical Gender Systems by Comparing Partitions
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In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) (2020)
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Pareto Probing: Trading Off Accuracy for Complexity
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In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) (2020)
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