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Theano: A Python framework for fast computation of mathematical expressions

The performance of Theano is compared against Torch7 and TensorFlow on several machine learning models and recently-introduced functionalities and improvements are discussed.
arXiv (opens in a new tab)

Adversarial Learning for Neural Dialogue Generation

This work applies adversarial training to open-domain dialogue generation, training a system to produce sequences that are indistinguishable from human-generated dialogue utterances, and investigates models for adversarial evaluation that uses success in fooling an adversary as a dialogue evaluation metric, while avoiding a number of potential pitfalls.
ACL (opens in a new tab)

On Using Very Large _target Vocabulary for Neural Machine Translation

It is shown that decoding can be efficiently done even with the model having a very large _target vocabulary by selecting only a small subset of the whole _target vocabulary.
ACL (opens in a new tab)

Does Neural Machine Translation Benefit from Larger Context?

We propose a neural machine translation architecture that models the surrounding text in addition to the source sentence. These models lead to better performance, both in terms of general translation…
arXiv (opens in a new tab)

Lingvo: a Modular and Scalable Framework for Sequence-to-Sequence Modeling

This document outlines the underlying design of Lingvo and serves as an introduction to the various pieces of the framework, while also offering examples of advanced features that showcase the capabilities of the Framework.
arXiv (opens in a new tab)

Combining modality specific deep neural networks for emotion recognition in video

In this paper we present the techniques used for the University of Montréal's team submissions to the 2013 Emotion Recognition in the Wild Challenge. The challenge is to classify the emotions…
ACM (opens in a new tab)

EmoNets: Multimodal deep learning approaches for emotion recognition in video

This paper explores multiple methods for the combination of cues from these modalities into one common classifier, which achieves a considerably greater accuracy than predictions from the strongest single-modality classifier.
Springer (opens in a new tab)

Neural Machine Translation Systems for WMT ’ 15

The Montreal Institute for Learning Algorithms (MILA) submission to WMT’15 is to evaluate this new approach to NMT on a greater variety of language pairs, using the RNNsearch architecture, which adds an attention mechanism to the encoderdecoder.

Adaptive Scheduling for Multi-Task Learning

This work first considers existing non-adaptive techniques, then moves on to adaptive schedules that over-sample tasks with poorer results compared to their respective baseline, and considers implicit schedules, learning to scale learning rates or gradients of individual tasks instead.
arXiv (opens in a new tab)

Embedding Word Similarity with Neural Machine Translation

Embeddings from translation models outperform those learned by monolingual models at tasks that require knowledge of both conceptual similarity and lexical-syntactic role, and are indicated that translation-based embeddings should be used in applications that require concepts to be organised according to similarity and/or lexical function.
arXiv (opens in a new tab)
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