Domain adaptation methods aim to learn a good prediction model in a label-scarce target domain by leveraging labeled patterns from a related source domain where there is a large amount of labeled data. However, in many practical domain adaptation learning scenarios, the feature distribution in the source domain is different from that in the target domain. In the extreme, the two distributions could differ completely when the feature representation of the source domain is totally different from that of the target domain. To address the problems of substantial feature distribution divergence across domains and heterogeneous feature representations of different domains, we propose a novel feature space independent semi-supervised kernel matching method for domain adaptation in this work. Our approach learns a prediction function on the labeled source data while mapping the target data points to similar source data points by matching the target kernel matrix to a submatrix of the source kernel matrix based on a Hilbert Schmidt Independence Criterion. We formulate this simultaneous learning and mapping process as a non-convex integer optimization problem and present a local minimization procedure for its relaxed continuous form. We evaluate the proposed kernel matching method using both cross domain sentiment classification tasks of Amazon product reviews and cross language text classification tasks of Reuters multilingual newswire stories. Our empirical results demonstrate that the proposed kernel matching method consistently and significantly outperforms comparison methods on both cross domain classification problems with homogeneous feature spaces and cross domain classification problems with heterogeneous feature spaces.

This paper proposes to learn languageindependent word representations to address cross-lingual dependency parsing, which aims to predict the dependency parsing trees for sentences in the target language by training a dependency parser with labeled sentences from a source language. We first combine all sentences from both languages to induce real-valued distributed representation of words under a deep neural network architecture, which is expected to capture semantic similarities of words not only within the same language but also across different languages. We then use the induced interlingual word representation as augmenting features to train a delexicalized dependency parser on labeled sentences in the source language and apply it to the target sentences. To investigate the effectiveness of the proposed technique, extensive experiments are conducted on cross-lingual dependency parsing tasks with nine different languages. The experimental results demonstrate the superior cross-lingual generalizability of the word representation induced by the proposed approach, comparing to alternative comparison methods.

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In cross-lingual text classification problems, it is costly and time-consuming to annotate documents for each individual language. To avoid the expensive re-labeling process, domain adaptation techniques can be applied to adapt a learning system trained in one language domain to another language domain. In this paper we develop a transductive subspace representation learning method to address domain adaptation for cross-lingual text classifications. The proposed approach is formulated as a nonnegative matrix factorization problem and solved using an iterative optimization procedure. Our empirical study on cross-lingual text classification tasks shows the proposed approach consistently outperforms a number of comparison methods.