546 research outputs found
Transductive data-selection algorithms for fine-tuning neural machine translation
Machine Translation models are trained to translate a variety of documents from one language into another. However, models specifically trained for a particular characteristics of the documents tend to perform better. Fine-tuning is a technique for adapting an NMT model to some domain. In this work, we want to use this technique to adapt the model to a given test set. In particular, we are using transductive data selection algorithms which take advantage the information of the test set to retrieve sentences from a larger parallel set
Feature decay algorithms for neural machine translation
Neural Machine Translation (NMT) systems require a lot of data to be competitive. For this reason, data selection techniques are used only for finetuning systems that have been trained with larger amounts of data. In this work we aim to use Feature Decay Algorithms (FDA) data selection techniques not only to fine-tune a system but also to build a complete system with less data. Our findings reveal that it is possible to find a subset of sentence pairs, that outperforms by 1.11 BLEU points the full training corpus, when used for training a German-English NMT system
Selecting artificially-generated sentences for fine-tuning neural machine translation
Neural Machine Translation (NMT) models
tend to achieve best performance when larger
sets of parallel sentences are provided for trai-
ning. For this reason, augmenting the training
set with artificially-generated sentence pairs
can boost performance.
Nonetheless, the performance can also be im-
proved with a small number of sentences
if they are in the same domain as the test
set. Accordingly, we want to explore the use
of artificially-generated sentences along with
data-selection algorithms to improve German-
to-English NMT models trained solely with
authentic data.
In this work, we show how artificially-
generated sentences can be more beneficial
than authentic pairs, and demonstrate their ad-
vantages when used in combination with data-
selection algorithms
Adaptation of machine translation models with back-translated data using transductive data selection methods
Data selection has proven its merit for improving Neural Machine Translation (NMT), when applied to authentic data. But the beneļ¬t of using synthetic data in NMT training, produced by the popular back-translation technique, raises the question if data selection could also be useful for synthetic data? In this work we use Infrequent n-gram Recovery (INR) and Feature Decay Algorithms (FDA), two transductive data selection methods to obtain subsets of sentences from synthetic data. These methods ensure that selected sentences share n-grams with the test set so the NMT model can be adapted to translate it. Performing data selection on back-translated data creates new challenges as the source-side may contain noise originated by the model used in the back-translation. Hence, ļ¬nding ngrams present in the test set become more diļ¬cult. Despite that, in our work we show that adapting a model with a selection of synthetic data is an useful approach
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
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