16,074 research outputs found
Spartan Daily, February 16, 2004
Volume 122, Issue 11https://scholarworks.sjsu.edu/spartandaily/9946/thumbnail.jp
Spartan Daily, February 16, 2004
Volume 122, Issue 11https://scholarworks.sjsu.edu/spartandaily/9946/thumbnail.jp
Spartan Daily, March 17, 2015
Volume 144, Issue 22https://scholarworks.sjsu.edu/spartandaily/2114/thumbnail.jp
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
Offline to Online Conversion
We consider the problem of converting offline estimators into an online
predictor or estimator with small extra regret. Formally this is the problem of
merging a collection of probability measures over strings of length 1,2,3,...
into a single probability measure over infinite sequences. We describe various
approaches and their pros and cons on various examples. As a side-result we
give an elementary non-heuristic purely combinatoric derivation of Turing's
famous estimator. Our main technical contribution is to determine the
computational complexity of online estimators with good guarantees in general.Comment: 20 LaTeX page
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