Unsupervised Learning of Sequence Representations by Autoencoders
This work addresses the problem of handling sequence data in machine learning, offering a method that could improve semi-supervised learning tasks, but it appears incremental as it integrates existing mechanisms with a new stop feature.
The paper tackles the challenge of learning fixed-length representations from variable-length sequences by proposing an Integrated Sequence Autoencoder (ISA) that minimizes reconstruction error, achieving the ability to capture both apparent features and high-level style information, such as recognizing spoken text and discriminating speakers in speech sequences.
Sequence data is challenging for machine learning approaches, because the lengths of the sequences may vary between samples. In this paper, we present an unsupervised learning model for sequence data, called the Integrated Sequence Autoencoder (ISA), to learn a fixed-length vectorial representation by minimizing the reconstruction error. Specifically, we propose to integrate two classical mechanisms for sequence reconstruction which takes into account both the global silhouette information and the local temporal dependencies. Furthermore, we propose a stop feature that serves as a temporal stamp to guide the reconstruction process, which results in a higher-quality representation. The learned representation is able to effectively summarize not only the apparent features, but also the underlying and high-level style information. Take for example a speech sequence sample: our ISA model can not only recognize the spoken text (apparent feature), but can also discriminate the speaker who utters the audio (more high-level style). One promising application of the ISA model is that it can be readily used in the semi-supervised learning scenario, in which a large amount of unlabeled data is leveraged to extract high-quality sequence representations and thus to improve the performance of the subsequent supervised learning tasks on limited labeled data.