Learning of indiscriminate distributions of document embeddings for domain adaptation

摘要

Natural language processing (NLP) is an important application area in domain adaptation because properties of texts depend on their corpus. However, a textual input is not fundamentally represented as the numerical vector. Many domain adaptation methods for NLP have been developed on the basis of numerical representations of texts instead of textual inputs. Thus, we develop a distributed representation learning method of words and documents for domain adaptation. The developed method addresses the domain separation problem of document embeddings from different domains, that is, the supports of the embeddings are separable across domains and the distributions of the embeddings are discriminated. We propose a new method based on negative sampling. The proposed method learns document embeddings by assuming that a noise distribution is dependent on a domain. The proposed method moves a document embedding close to the embeddings of the important words in the document and keeps the embedding away from the word embeddings that occur frequently in both domains. For Amazon reviews, we verified that the proposed method outperformed other representation methods in terms of indiscriminability of the distributions of the document embeddings through experiments such as visualizing them and calculating a proxy A-distance measure. We also performed sentiment classification tasks to validate the effectiveness of document embeddings. The proposed method achieved consistently better results than other methods. In addition, we applied the learned document embeddings to the domain adversarial neural network method, which is a popular deep learning-based domain adaptation model. The proposed method obtained not only better performance on most datasets but also more stable convergences for all datasets than the other methods. Therefore, the proposed method are applicable to other domain adaptation methods for NLP using numerical representations of documents or words.