1,346 research outputs found
Unsupervised Neural Machine Translation with SMT as Posterior Regularization
Without real bilingual corpus available, unsupervised Neural Machine
Translation (NMT) typically requires pseudo parallel data generated with the
back-translation method for the model training. However, due to weak
supervision, the pseudo data inevitably contain noises and errors that will be
accumulated and reinforced in the subsequent training process, leading to bad
translation performance. To address this issue, we introduce phrase based
Statistic Machine Translation (SMT) models which are robust to noisy data, as
posterior regularizations to guide the training of unsupervised NMT models in
the iterative back-translation process. Our method starts from SMT models built
with pre-trained language models and word-level translation tables inferred
from cross-lingual embeddings. Then SMT and NMT models are optimized jointly
and boost each other incrementally in a unified EM framework. In this way, (1)
the negative effect caused by errors in the iterative back-translation process
can be alleviated timely by SMT filtering noises from its phrase tables;
meanwhile, (2) NMT can compensate for the deficiency of fluency inherent in
SMT. Experiments conducted on en-fr and en-de translation tasks show that our
method outperforms the strong baseline and achieves new state-of-the-art
unsupervised machine translation performance.Comment: To be presented at AAAI 2019; 9 pages, 4 figure
Does Privacy Still Matter in the Era of Web 2.0? A Qualitative Study of User Behavior towards Online Social Networking Activities
In this study, we attempt to understand one frequently observed paradox in user social networking behavior – highly concerned about privacy issues on social networking sites, yet actively participating in social networking activities. Based on qualitative analysis of student essays on their social networking activities and perceptions, we propose a theory for user online social networking behavior – the adaptive cognition theory (ACT). The main argument of the theory is that user behavior toward social networking is dynamic and adaptive primarily influenced by the perceived benefits and risks. More often than not, the perceived benefits dominate the perceived risks in user behavior calculus, resulting in the commonly observed phenomenon that users seem to ignore privacy concerns when participating in social networking activities and using social networking web sites. We argue that ACT explains user social networking behavior better than well-established behavioral theories do such as TAM, TPB, and rational choice. Furthermore, ACT provides prescriptive insights for managers of social networking sites and companies interested in taking advantage of the social networking phenomenon. Limitations and future research directions are discussed as well
A Scheme to fabricate magnetic graphene-like cobalt nitride CoN4monolayer proposed by first-principles calculations
We propose a scheme to fabricate the cobalt nitride CoN4 monolayer, a
magnetic graphene-like two-dimensional material, in which all Co and N atoms
are in a plane. Under the pressure above 40 GPa, the bulk CoN4 is stabilized in
a triclinic phase. With the pressure decreasing, the triclinic phase of CoN4 is
transformed into an orthorhombic phase, and the latter is a layered compound
with large interlayer spacing. At ambient condition, the weak interlayer
couplings are so small that single CoN4 layer can be exfoliated by the
mechanical method
Robust Human Motion Forecasting using Transformer-based Model
Comprehending human motion is a fundamental challenge for developing
Human-Robot Collaborative applications. Computer vision researchers have
addressed this field by only focusing on reducing error in predictions, but not
taking into account the requirements to facilitate its implementation in
robots. In this paper, we propose a new model based on Transformer that
simultaneously deals with the real time 3D human motion forecasting in the
short and long term. Our 2-Channel Transformer (2CH-TR) is able to efficiently
exploit the spatio-temporal information of a shortly observed sequence (400ms)
and generates a competitive accuracy against the current state-of-the-art.
2CH-TR stands out for the efficient performance of the Transformer, being
lighter and faster than its competitors. In addition, our model is tested in
conditions where the human motion is severely occluded, demonstrating its
robustness in reconstructing and predicting 3D human motion in a highly noisy
environment. Our experiment results show that the proposed 2CH-TR outperforms
the ST-Transformer, which is another state-of-the-art model based on the
Transformer, in terms of reconstruction and prediction under the same
conditions of input prefix. Our model reduces in 8.89% the mean squared error
of ST-Transformer in short-term prediction, and 2.57% in long-term prediction
in Human3.6M dataset with 400ms input prefix.Comment: This paper has been already accepted to the 2022 IEEE/RSJ
International Conference on Intelligent Robots and Systems (IROS 2022
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