Learning and time : on using memory and curricula for language understanding

The goal of this thesis is to present some of the small steps taken on the path towards solving natural language understanding and learning long-term dependencies to develop artificial intelligence algorithms that can reason with language. This thesis is written as a thesis by articles and contains five articles. Each article in this thesis proposes a new model or algorithm and demonstrates the efficiency of the proposed approach to solve problems that involve long-term dependencies or require natural language understanding. Although some of the models are tested on a particular task (such as neural machine translation), the proposed methods in this thesis are generally applicable to other domains and tasks (and have been used in the literature).  In the introduction of this thesis, we introduce some of the fundamental concepts behind training sequence models using neural networks. We first provide a brief introduction to neural networks and then dive into details of the some of approaches and algorithms that are used throughout this thesis.  In our first article, we propose a novel method to utilize the abundant amount of available monolingual data for training neural machine translation models. We have accomplished this goal by first training a long short-term memory (LSTM) language model on a large monolingual corpus and then fusing the outputs or the hidden states of the LSTM language model with the decoder of the neural machine translation model. Our neural machine translation model is trained end to end with an attention mechanism. We have shown that our proposed approaches can improve the performance of the neural machine translation models significantly on the rare resource translation tasks and our approach improved the data-efficiency of the end to end neural machine translation systems. We report improvements on Turkish-English (Tr-En), German-English (De-En), Chinese-English (Zh-En) and Czech-English (Cz-En) translation tasks.  In our second paper, we propose an approach to address the problem of rare words for natural language processing tasks. Our approach augments the encoder-decoder architecture with attention model by replacing the final softmax layer with our proposed pointer-softmax layer that creates pointers to the source sentences as the decoder translates. In the case of pointer-softmax, our model learns to switch between copying a word from the source and predicting a word from a shortlist vocabulary in a probabilistic manner. Our proposed approach is end-to-end trainable with a single maximum likelihood objective of the NMT model. We have also shown that it improves the performance of summarization and the neural machine translation model. We report significant improvements in machine translation and summarization tasks.  In our "Plan, Attend, Generate: Planning for Sequence-to-Sequence Models" paper, we propose two new approaches to learn alignments in a sequence to sequence model. If the input and the source context is very long, learning the alignments for a sequence to sequence model can be difficult. In particular, because when the decoder is a large network, it can learn to ignore the alignments and attend more on the last token of the input sequence. We propose a new approach which is inspired by a hierarchical reinforcement learning algorithm and extend our model with an explicit planning mechanism. The proposed alignment mechanism plans and computes the alignments for the next $k$ tokens in the decoder. Our model also learns a commitment plan to decide when to recompute the alignment matrix. Our proposed approach can learn high-level temporal abstractions, and we show that it qualitatively learns better alignments. We also achieve significant improvements over our baseline despite using smaller models and with less training.  In "Dynamic Neural Turing Machine with Soft and Hard Addressing Schemes," we propose a new approach for augmenting neural networks with an explicit memory mechanism. As opposed to conventional RNNs, the memory is not only represented in the activations of the neural network but in an external memory that can be accessed via the neural network controller. Our model, D-NTM uses a more straightforward memory addressing mechanism than NTM which is achieved by using key-value pairs for each memory cell. We find out that the models augmented with an external memory mechanism can learn tasks that involve long-term dependencies more efficiently and achieve better generalization. We achieve improvements on many tasks including but not limited to episodic question answering on bAbI, reasoning with entailment, permuted MNIST task and synthetic tasks.  In our "Noisy Activation Functions" paper, we propose a novel activation function that makes the activations stochastic by injecting a particular form of noise to them. Our motivation in this paper is to address the optimization problem of commonly used saturating activation functions that are used with the recurrent neural networks. Our approach enables us to use piece-wise linear activation functions on the gated recurrent neural network models. We show improvements in a wide range of tasks without doing any extensive hyperparameter search by a drop-in replacement. We also show that annealing the noise of the activation function can have a profound continuation-like effect on the optimization of the network.

Keyphrases: Deep learning, Curriculum learning, Optimization, Natural language processing, Neural networks, Recurrent neural networks, Language modelling, Memory, Machine translation