8,850 research outputs found
Filling Knowledge Gaps in a Broad-Coverage Machine Translation System
Knowledge-based machine translation (KBMT) techniques yield high quality in
domains with detailed semantic models, limited vocabulary, and controlled input
grammar. Scaling up along these dimensions means acquiring large knowledge
resources. It also means behaving reasonably when definitive knowledge is not
yet available. This paper describes how we can fill various KBMT knowledge
gaps, often using robust statistical techniques. We describe quantitative and
qualitative results from JAPANGLOSS, a broad-coverage Japanese-English MT
system.Comment: 7 pages, Compressed and uuencoded postscript. To appear: IJCAI-9
Translation inference through multi-lingual word embedding similarity
This paper describes our contribution to the Shared Task on
Translation Inference across Dictionaries (TIAD-2019). In our approach,
we construct a multi-lingual word embedding space by projecting new
languages in the feature space of a language for which a pretrained embedding model exists. We use the similarity of the word embeddings to
predict candidate translations. Even if our projection methodology is
rather simplistic, our system outperforms the other participating systems with respect to the F1 measure for the language pairs which we
predicted
Model Transfer for Tagging Low-resource Languages using a Bilingual Dictionary
Cross-lingual model transfer is a compelling and popular method for
predicting annotations in a low-resource language, whereby parallel corpora
provide a bridge to a high-resource language and its associated annotated
corpora. However, parallel data is not readily available for many languages,
limiting the applicability of these approaches. We address these drawbacks in
our framework which takes advantage of cross-lingual word embeddings trained
solely on a high coverage bilingual dictionary. We propose a novel neural
network model for joint training from both sources of data based on
cross-lingual word embeddings, and show substantial empirical improvements over
baseline techniques. We also propose several active learning heuristics, which
result in improvements over competitive benchmark methods.Comment: 5 pages with 2 pages reference. Accepted to appear in ACL 201
Recent Progress in Image Deblurring
This paper comprehensively reviews the recent development of image
deblurring, including non-blind/blind, spatially invariant/variant deblurring
techniques. Indeed, these techniques share the same objective of inferring a
latent sharp image from one or several corresponding blurry images, while the
blind deblurring techniques are also required to derive an accurate blur
kernel. Considering the critical role of image restoration in modern imaging
systems to provide high-quality images under complex environments such as
motion, undesirable lighting conditions, and imperfect system components, image
deblurring has attracted growing attention in recent years. From the viewpoint
of how to handle the ill-posedness which is a crucial issue in deblurring
tasks, existing methods can be grouped into five categories: Bayesian inference
framework, variational methods, sparse representation-based methods,
homography-based modeling, and region-based methods. In spite of achieving a
certain level of development, image deblurring, especially the blind case, is
limited in its success by complex application conditions which make the blur
kernel hard to obtain and be spatially variant. We provide a holistic
understanding and deep insight into image deblurring in this review. An
analysis of the empirical evidence for representative methods, practical
issues, as well as a discussion of promising future directions are also
presented.Comment: 53 pages, 17 figure
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