An increasing number of datasets contain multiple views, such as video, sound and automatic captions. A basic challenge in representation learning is how to leverage multiple views to learn better representations. This is further complicated by the existence of a latent alignment between views, such as between speech and its transcription, and by the multitude of choices for the learning objective. We explore an advanced, correlation-based representation learning method on a 4-way parallel, multimodal dataset, and assess the quality of the learned representations on retrieval-based tasks. We show that the proposed approach produces rich representations that capture most of the information shared across views. Our best models for speech and textual modalities achieve retrieval rates from 70.7% to 96.9% on open-domain, user-generated instructional videos. This shows it is possible to learn reliable representations across disparate, unaligned and noisy modalities, and encourages using the proposed approach on larger datasets.Index Terms-Multiview Learning, Representation Learning, Canonical Correlation Analysis * Equal contribution in its Portuguese translation. Figure 1 shows an overview of our learning algorithm with 4 different input views, detailed in Section 3.Given these multiple parallel modalities, we address the following: how much information is shared across modalities? How can we learn a representation that captures information from all modalities? We measure this using intrinsic evaluations. In the remainder of this paper, we describe related work, then the proposed methods and our experimental setup, and finally present our results. CCA loss neural network English speech neural network video neural network Once I have my jack stand there on the rear axle, go ahead and release the hydraulic pressure.. English text neural network Q u a n d o e u t i v e r m e u macaco parado no eixo traseiro, vá em frente e libere a pressão hidráulica.