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AdaTag: Multi-Attribute Value Extraction from Product Profiles with Adaptive Decoding ...
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Adversarial Multitask Learning for Joint Multi-Feature and Multi-Dialect Morphological Modeling ...
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Joint Diacritization, Lemmatization, Normalization, and Fine-Grained Morphological Tagging ...
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Abstract:
Semitic languages can be highly ambiguous, having several interpretations of the same surface forms, and morphologically rich, having many morphemes that realize several morphological features. This is further exacerbated for dialectal content, which is more prone to noise and lacks a standard orthography. The morphological features can be lexicalized, like lemmas and diacritized forms, or non-lexicalized, like gender, number, and part-of-speech tags, among others. Joint modeling of the lexicalized and non-lexicalized features can identify more intricate morphological patterns, which provide better context modeling, and further disambiguate ambiguous lexical choices. However, the different modeling granularity can make joint modeling more difficult. Our approach models the different features jointly, whether lexicalized (on the character-level), where we also model surface form normalization, or non-lexicalized (on the word-level). We use Arabic as a test case, and achieve state-of-the-art results for Modern ...
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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.1910.02267 https://arxiv.org/abs/1910.02267
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Utilizing Character and Word Embeddings for Text Normalization with Sequence-to-Sequence Models ...
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Optimizing Tokenization Choice for Machine Translation across Multiple Target Languages
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In: Prague Bulletin of Mathematical Linguistics , Vol 108, Iss 1, Pp 257-269 (2017) (2017)
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