35,477 research outputs found
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
Adaptive Semi-supervised Learning for Cross-domain Sentiment Classification
We consider the cross-domain sentiment classification problem, where a
sentiment classifier is to be learned from a source domain and to be
generalized to a target domain. Our approach explicitly minimizes the distance
between the source and the target instances in an embedded feature space. With
the difference between source and target minimized, we then exploit additional
information from the target domain by consolidating the idea of semi-supervised
learning, for which, we jointly employ two regularizations -- entropy
minimization and self-ensemble bootstrapping -- to incorporate the unlabeled
target data for classifier refinement. Our experimental results demonstrate
that the proposed approach can better leverage unlabeled data from the target
domain and achieve substantial improvements over baseline methods in various
experimental settings.Comment: Accepted to EMNLP201
Domain adaptation for sequence labeling using hidden Markov models
Most natural language processing systems based on machine learning are not
robust to domain shift. For example, a state-of-the-art syntactic dependency
parser trained on Wall Street Journal sentences has an absolute drop in
performance of more than ten points when tested on textual data from the Web.
An efficient solution to make these methods more robust to domain shift is to
first learn a word representation using large amounts of unlabeled data from
both domains, and then use this representation as features in a supervised
learning algorithm. In this paper, we propose to use hidden Markov models to
learn word representations for part-of-speech tagging. In particular, we study
the influence of using data from the source, the target or both domains to
learn the representation and the different ways to represent words using an
HMM.Comment: New Directions in Transfer and Multi-Task: Learning Across Domains
and Tasks (NIPS Workshop) (2013
Cross-Lingual Adaptation using Structural Correspondence Learning
Cross-lingual adaptation, a special case of domain adaptation, refers to the
transfer of classification knowledge between two languages. In this article we
describe an extension of Structural Correspondence Learning (SCL), a recently
proposed algorithm for domain adaptation, for cross-lingual adaptation. The
proposed method uses unlabeled documents from both languages, along with a word
translation oracle, to induce cross-lingual feature correspondences. From these
correspondences a cross-lingual representation is created that enables the
transfer of classification knowledge from the source to the target language.
The main advantages of this approach over other approaches are its resource
efficiency and task specificity.
We conduct experiments in the area of cross-language topic and sentiment
classification involving English as source language and German, French, and
Japanese as target languages. The results show a significant improvement of the
proposed method over a machine translation baseline, reducing the relative
error due to cross-lingual adaptation by an average of 30% (topic
classification) and 59% (sentiment classification). We further report on
empirical analyses that reveal insights into the use of unlabeled data, the
sensitivity with respect to important hyperparameters, and the nature of the
induced cross-lingual correspondences
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