97 research outputs found
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
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
Marrying Universal Dependencies and Universal Morphology
The Universal Dependencies (UD) and Universal Morphology (UniMorph) projects
each present schemata for annotating the morphosyntactic details of language.
Each project also provides corpora of annotated text in many languages - UD at
the token level and UniMorph at the type level. As each corpus is built by
different annotators, language-specific decisions hinder the goal of universal
schemata. With compatibility of tags, each project's annotations could be used
to validate the other's. Additionally, the availability of both type- and
token-level resources would be a boon to tasks such as parsing and homograph
disambiguation. To ease this interoperability, we present a deterministic
mapping from Universal Dependencies v2 features into the UniMorph schema. We
validate our approach by lookup in the UniMorph corpora and find a
macro-average of 64.13% recall. We also note incompatibilities due to paucity
of data on either side. Finally, we present a critical evaluation of the
foundations, strengths, and weaknesses of the two annotation projects.Comment: UDW1
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