4,456 research outputs found
Paradigm Completion for Derivational Morphology
The generation of complex derived word forms has been an overlooked problem
in NLP; we fill this gap by applying neural sequence-to-sequence models to the
task. We overview the theoretical motivation for a paradigmatic treatment of
derivational morphology, and introduce the task of derivational paradigm
completion as a parallel to inflectional paradigm completion. State-of-the-art
neural models, adapted from the inflection task, are able to learn a range of
derivation patterns, and outperform a non-neural baseline by 16.4%. However,
due to semantic, historical, and lexical considerations involved in
derivational morphology, future work will be needed to achieve performance
parity with inflection-generating systems.Comment: EMNLP 201
Align and Copy: UZH at SIGMORPHON 2017 Shared Task for Morphological Reinflection
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.Comment: To appear in Proceedings of the 15th Annual SIGMORPHON Workshop on
Computational Research in Phonetics, Phonology, and Morphology at CoNLL 201
Multi-space Variational Encoder-Decoders for Semi-supervised Labeled Sequence Transduction
Labeled sequence transduction is a task of transforming one sequence into
another sequence that satisfies desiderata specified by a set of labels. In
this paper we propose multi-space variational encoder-decoders, a new model for
labeled sequence transduction with semi-supervised learning. The generative
model can use neural networks to handle both discrete and continuous latent
variables to exploit various features of data. Experiments show that our model
provides not only a powerful supervised framework but also can effectively take
advantage of the unlabeled data. On the SIGMORPHON morphological inflection
benchmark, our model outperforms single-model state-of-art results by a large
margin for the majority of languages.Comment: Accepted by ACL 201
One-Shot Neural Cross-Lingual Transfer for Paradigm Completion
We present a novel cross-lingual transfer method for paradigm completion, the
task of mapping a lemma to its inflected forms, using a neural encoder-decoder
model, the state of the art for the monolingual task. We use labeled data from
a high-resource language to increase performance on a low-resource language. In
experiments on 21 language pairs from four different language families, we
obtain up to 58% higher accuracy than without transfer and show that even
zero-shot and one-shot learning are possible. We further find that the degree
of language relatedness strongly influences the ability to transfer
morphological knowledge.Comment: Accepted at ACL 201
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