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In this work we use the recent advances in representation learning to propose a neural architecture for the problem of natural language inference. Our approach is aligned to mimic how a human does the natural language inference process given two statements. The model uses variants of Long Short Term Memory (LSTM), attention mechanism and composable neural networks, to carry out the task. Each part of our model can be mapped to a clear functionality humans do for carrying out the overall task of natural language inference. The model is end-to-end differentiable enabling training by stochastic gradient descent. On Stanford Natural Language Inference(SNLI) dataset, the proposed model achieves better accuracy numbers than all published models in literature.
A Neural Architecture Mimicking Humans End-to-End for Natural Language Inference

We can program a Real-Time (RT) music improvisation system in C++ without a formal semantic or we can model it with process calculi such as the Non-deterministic Timed Concurrent Constraint (ntcc) calculus. “A Concurrent Constraints Factor Oracle (FO) model for Music Improvisation” (Ccfomi) is an improvisation model specified on ntcc. Since Ccfomi improvises non-deterministically, there is no control on choices and therefore little control over the sequence variation during the improvisation. To avoid this, we extended Ccfomi using the Probabilistic Non-deterministic Timed Concurrent Constraint calculus. Our extension to Ccfomi does not change the time and space complexity of building the FO, thus making our extension compatible with RT. However, there was not a ntcc interpreter capable of RT to execute Ccfomi. We developed Ntccrt –a RT capable interpreter for ntcc– and we executed Ccfomi on Ntccrt. In the future, we plan to extend Ntccrt to execute our extension to Ccfomi.
Probabilistic Extension to the Concurrent Constraint Factor Oracle Model for Music Improvisation

In this paper, we present nmtpy, a flexible Python toolkit based on Theano for training Neural Machine Translation and other neural sequence-to-sequence architectures. nmtpy decouples the specification of a network from the training and inference utilities to simplify the addition of a new architecture and reduce the amount of boilerplate code to be written. nmtpy has been used for LIUM’s topranked submissions to WMT Multimodal Machine Translation and News Translation tasks in 2016 and 2017. 1 OVERVIEW nmtpy is a refactored, extended and Python 3 only version of dl4mt-tutorial 1, a Theano (Theano Development Team, 2016) implementation of attentive Neural Machine Translation (NMT) (Bahdanau et al., 2014). The development of nmtpy project which has been open-sourced2 under MIT license in March 2017, started in March 2016 as an effort to adapt dl4mt-tutorial to multimodal translation models. nmtpy has now become a powerful toolkit where adding a new model is as simple as deriving from an abstract base class to fill in a set of fundamental methods and (optionally) implementing a custom data iterator. The training and inference utilities are as model-agnostic as possible allowing one to use them for different sequence generation networks such as multimodal NMT and image captioning to name a few. This flexibility and the rich set of provided architectures (Section 3) is what differentiates nmtpy from Nematus (Sennrich et al., 2017) another NMT software derived from dl4mt-tutorial.

NMTPY: A FLEXIBLE TOOLKIT FOR ADVANCED NEURAL MACHINE TRANSLATION SYSTEMS