6,088 research outputs found
Introduction to the special issue on cross-language algorithms and applications
With the increasingly global nature of our everyday interactions, the need for multilingual technologies to support efficient and efective information access and communication cannot be overemphasized. Computational modeling of language has been the focus of
Natural Language Processing, a subdiscipline of Artificial Intelligence. One of the current challenges for this discipline is to design methodologies and algorithms that are cross-language in order to create multilingual technologies rapidly. The goal of this JAIR special
issue on Cross-Language Algorithms and Applications (CLAA) is to present leading research in this area, with emphasis on developing unifying themes that could lead to the development of the science of multi- and cross-lingualism. In this introduction, we provide the reader with the motivation for this special issue and summarize the contributions of the papers that have been included. The selected papers cover a broad range of cross-lingual technologies including machine translation, domain and language adaptation for sentiment
analysis, cross-language lexical resources, dependency parsing, information retrieval and knowledge representation. We anticipate that this special issue will serve as an invaluable resource for researchers interested in topics of cross-lingual natural language processing.Postprint (published version
On Strong Centerpoints
Let be a set of points in and be a
family of geometric objects. We call a point a strong centerpoint of
w.r.t if is contained in all that
contains more than points from , where is a fixed constant. A
strong centerpoint does not exist even when is the family of
halfspaces in the plane. We prove the existence of strong centerpoints with
exact constants for convex polytopes defined by a fixed set of orientations. We
also prove the existence of strong centerpoints for abstract set systems with
bounded intersection
'Part'ly first among equals: Semantic part-based benchmarking for state-of-the-art object recognition systems
An examination of object recognition challenge leaderboards (ILSVRC,
PASCAL-VOC) reveals that the top-performing classifiers typically exhibit small
differences amongst themselves in terms of error rate/mAP. To better
differentiate the top performers, additional criteria are required. Moreover,
the (test) images, on which the performance scores are based, predominantly
contain fully visible objects. Therefore, `harder' test images, mimicking the
challenging conditions (e.g. occlusion) in which humans routinely recognize
objects, need to be utilized for benchmarking. To address the concerns
mentioned above, we make two contributions. First, we systematically vary the
level of local object-part content, global detail and spatial context in images
from PASCAL VOC 2010 to create a new benchmarking dataset dubbed PPSS-12.
Second, we propose an object-part based benchmarking procedure which quantifies
classifiers' robustness to a range of visibility and contextual settings. The
benchmarking procedure relies on a semantic similarity measure that naturally
addresses potential semantic granularity differences between the category
labels in training and test datasets, thus eliminating manual mapping. We use
our procedure on the PPSS-12 dataset to benchmark top-performing classifiers
trained on the ILSVRC-2012 dataset. Our results show that the proposed
benchmarking procedure enables additional differentiation among
state-of-the-art object classifiers in terms of their ability to handle missing
content and insufficient object detail. Given this capability for additional
differentiation, our approach can potentially supplement existing benchmarking
procedures used in object recognition challenge leaderboards.Comment: Extended version of our ACCV-2016 paper. Author formatting modifie
Learning to Translate in Real-time with Neural Machine Translation
Translating in real-time, a.k.a. simultaneous translation, outputs
translation words before the input sentence ends, which is a challenging
problem for conventional machine translation methods. We propose a neural
machine translation (NMT) framework for simultaneous translation in which an
agent learns to make decisions on when to translate from the interaction with a
pre-trained NMT environment. To trade off quality and delay, we extensively
explore various targets for delay and design a method for beam-search
applicable in the simultaneous MT setting. Experiments against state-of-the-art
baselines on two language pairs demonstrate the efficacy of the proposed
framework both quantitatively and qualitatively.Comment: 10 pages, camera read
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