14,398 research outputs found
Multi-Domain Neural Machine Translation through Unsupervised Adaptation.
We investigate the application of Neural Machine Translation (NMT) under the following three conditions posed by real-world application scenarios. First, we operate with an input stream of sentences coming from many different domains and with no predefined order. Second, the sentences are presented without domain information. Third, the input stream should be processed by a single generic NMTmodel. To tackle the weaknesses of current NMT technology in this unsupervised multi-domain setting, we explore an efficient instance-based adaptation method that, by exploiting the similarity between the training instances and each test sentence, dynamically sets the hyperparameters of the learning algorithm and updates the generic model on-the-fly. The results of our experiments with multi-domain data show that local adaptation outperforms not only the original generic NMT system, but also a strong phrase-based system and even single-domain NMT models specifically optimized on each domain and applicable only by violating two of our afore-mentioned assumptions
Recent Advances in Transfer Learning for Cross-Dataset Visual Recognition: A Problem-Oriented Perspective
This paper takes a problem-oriented perspective and presents a comprehensive
review of transfer learning methods, both shallow and deep, for cross-dataset
visual recognition. Specifically, it categorises the cross-dataset recognition
into seventeen problems based on a set of carefully chosen data and label
attributes. Such a problem-oriented taxonomy has allowed us to examine how
different transfer learning approaches tackle each problem and how well each
problem has been researched to date. The comprehensive problem-oriented review
of the advances in transfer learning with respect to the problem has not only
revealed the challenges in transfer learning for visual recognition, but also
the problems (e.g. eight of the seventeen problems) that have been scarcely
studied. This survey not only presents an up-to-date technical review for
researchers, but also a systematic approach and a reference for a machine
learning practitioner to categorise a real problem and to look up for a
possible solution accordingly
Transfer Learning for Speech and Language Processing
Transfer learning is a vital technique that generalizes models trained for
one setting or task to other settings or tasks. For example in speech
recognition, an acoustic model trained for one language can be used to
recognize speech in another language, with little or no re-training data.
Transfer learning is closely related to multi-task learning (cross-lingual vs.
multilingual), and is traditionally studied in the name of `model adaptation'.
Recent advance in deep learning shows that transfer learning becomes much
easier and more effective with high-level abstract features learned by deep
models, and the `transfer' can be conducted not only between data distributions
and data types, but also between model structures (e.g., shallow nets and deep
nets) or even model types (e.g., Bayesian models and neural models). This
review paper summarizes some recent prominent research towards this direction,
particularly for speech and language processing. We also report some results
from our group and highlight the potential of this very interesting research
field.Comment: 13 pages, APSIPA 201
- …