236,388 research outputs found
Multilingual NMT with a language-independent attention bridge
In this paper, we propose a multilingual encoder-decoder architecture capable
of obtaining multilingual sentence representations by means of incorporating an
intermediate {\em attention bridge} that is shared across all languages. That
is, we train the model with language-specific encoders and decoders that are
connected via self-attention with a shared layer that we call attention bridge.
This layer exploits the semantics from each language for performing translation
and develops into a language-independent meaning representation that can
efficiently be used for transfer learning. We present a new framework for the
efficient development of multilingual NMT using this model and scheduled
training. We have tested the approach in a systematic way with a multi-parallel
data set. We show that the model achieves substantial improvements over strong
bilingual models and that it also works well for zero-shot translation, which
demonstrates its ability of abstraction and transfer learning
Learning across communities of practice: how postgraduate students cope with returning to higher education in an international setting
This paper is an exploratory case study into the way postgraduate students cope with the transition from the workplace to university in an international environment. It looks at how students move successfully between these two communities of practice, and the kind of learning that is involved in this process. As well as personal motivation, key factors found in boundary-crossing between the communities are multi-membership of communities and the use of identity as a bridge. Learning is found to involve a collateral transfer, or reconstruction, of knowledge in both directions. The study is intended to inform the development of a learning support program to help postgraduate students improve their learning process
You Only Transfer What You Share: Intersection-Induced Graph Transfer Learning for Link Prediction
Link prediction is central to many real-world applications, but its
performance may be hampered when the graph of interest is sparse. To alleviate
issues caused by sparsity, we investigate a previously overlooked phenomenon:
in many cases, a densely connected, complementary graph can be found for the
original graph. The denser graph may share nodes with the original graph, which
offers a natural bridge for transferring selective, meaningful knowledge. We
identify this setting as Graph Intersection-induced Transfer Learning (GITL),
which is motivated by practical applications in e-commerce or academic
co-authorship predictions. We develop a framework to effectively leverage the
structural prior in this setting. We first create an intersection subgraph
using the shared nodes between the two graphs, then transfer knowledge from the
source-enriched intersection subgraph to the full target graph. In the second
step, we consider two approaches: a modified label propagation, and a
multi-layer perceptron (MLP) model in a teacher-student regime. Experimental
results on proprietary e-commerce datasets and open-source citation graphs show
that the proposed workflow outperforms existing transfer learning baselines
that do not explicitly utilize the intersection structure.Comment: Accepted in TMLR (https://openreview.net/forum?id=Nn71AdKyYH
Blending-target Domain Adaptation by Adversarial Meta-Adaptation Networks
(Unsupervised) Domain Adaptation (DA) seeks for classifying target instances
when solely provided with source labeled and target unlabeled examples for
training. Learning domain-invariant features helps to achieve this goal,
whereas it underpins unlabeled samples drawn from a single or multiple explicit
target domains (Multi-target DA). In this paper, we consider a more realistic
transfer scenario: our target domain is comprised of multiple sub-targets
implicitly blended with each other, so that learners could not identify which
sub-target each unlabeled sample belongs to. This Blending-target Domain
Adaptation (BTDA) scenario commonly appears in practice and threatens the
validities of most existing DA algorithms, due to the presence of domain gaps
and categorical misalignments among these hidden sub-targets.
To reap the transfer performance gains in this new scenario, we propose
Adversarial Meta-Adaptation Network (AMEAN). AMEAN entails two adversarial
transfer learning processes. The first is a conventional adversarial transfer
to bridge our source and mixed target domains. To circumvent the intra-target
category misalignment, the second process presents as ``learning to adapt'': It
deploys an unsupervised meta-learner receiving target data and their ongoing
feature-learning feedbacks, to discover target clusters as our
``meta-sub-target'' domains. These meta-sub-targets auto-design our
meta-sub-target DA loss, which empirically eliminates the implicit category
mismatching in our mixed target. We evaluate AMEAN and a variety of DA
algorithms in three benchmarks under the BTDA setup. Empirical results show
that BTDA is a quite challenging transfer setup for most existing DA
algorithms, yet AMEAN significantly outperforms these state-of-the-art
baselines and effectively restrains the negative transfer effects in BTDA.Comment: CVPR-19 (oral). Code is available at
http://github.com/zjy526223908/BTD
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