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The SIGMORPHON 2016 Shared Task - Morphological Reinflection
TLDR
The 2016 SIGMORPHON Shared Task was devoted to the problem of morphological reinflection. Expand
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Joint Lemmatization and Morphological Tagging with Lemming
TLDR
We present LEMMING, a modular loglinear model that jointly models lemmatization and tagging and supports the integration of arbitrary global features. Expand
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Gender Bias in Contextualized Word Embeddings
TLDR
We quantify, analyze and mitigate gender bias exhibited in ELMo’s contextualized word vectors. Expand
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Labeled Morphological Segmentation with Semi-Markov Models
TLDR
We introduce a new hierarchy of morphotactic tagsets and CHIPMUNK, a discriminative morphological segmentation system that explicitly models morphotactics. Expand
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Weighting Finite-State Transductions With Neural Context
TLDR
We propose a hybrid FST-LSTM architecture for string-to-string transduction tasks that combines classical finite-state approaches to transduction and newer neural approaches. Expand
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UniMorph 2.0: Universal Morphology
TLDR
The Universal Morphology UniMorph project is a collaborative effort to improve how NLP handles complex morphology across the world's languages. Expand
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Morphological Smoothing and Extrapolation of Word Embeddings
TLDR
We present a Gaussian graphical model that allows us to extrapolate continuous representations for words not observed in the training corpus, as well as smoothing the representations provided for the observed words. Expand
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Generalizing Procrustes Analysis for Better Bilingual Dictionary Induction
TLDR
We show that projecting the two languages onto a third, latent space, rather than directly onto each other, while equivalent in terms of expressivity, makes it easier to learn approximate alignments. Expand
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CoNLL-SIGMORPHON 2017 Shared Task: Universal Morphological Reinflection in 52 Languages
TLDR
The CoNLL-SIGMORPHON 2017 shared task on supervised morphological generation required systems to be trained and tested in each of 52 typologically diverse languages. Expand
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Cross-lingual Character-Level Neural Morphological Tagging
TLDR
We train character-level recurrent neural taggers to predict morphological taggings for high-resource and low-resource languages together. Expand
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