The Mathematical Universe

I explore physics implications of the External Reality Hypothesis (ERH) that there exists an external physical reality completely independent of us humans. I argue that with a sufficiently broad definition of mathematics, it implies the Mathematical Universe Hypothesis (MUH) that our physical world is an abstract mathematical structure. I discuss various implications of the ERH and MUH, ranging from standard physics topics like symmetries, irreducible representations, units, free parameters, randomness and initial conditions to broader issues like consciousness, parallel universes and Godel incompleteness. I hypothesize that only computable and decidable (in Godel's sense) structures exist, which alleviates the cosmological measure problem and help explain why our physical laws appear so simple. I also comment on the intimate relation between mathematical structures, computations, simulations and physical systems.Comment: Replaced to match accepted Found. Phys. version, 31 pages, 5 figs; more details at http://space.mit.edu/home/tegmark/toe.htm

Stream-based statistical machine translation

We investigate a new approach for SMT system training within the streaming model of computation. We develop and test incrementally retrainable models which, given an incoming stream of new data, can efficiently incorporate the stream data online. A naive approach using a stream would use an unbounded amount of space. Instead, our online SMT system can incorporate information from unbounded incoming streams and maintain constant space and time. Crucially, we are able to match (or even exceed) translation performance of comparable systems which are batch retrained and use unbounded space. Our approach is particularly suited for situations when there is arbitrarily large amounts of new training material and we wish to incorporate it efficiently and in small space. The novel contributions of this thesis are: 1. An online, randomised language model that can model unbounded input streams in constant space and time. 2. An incrementally retrainable translationmodel for both phrase-based and grammarbased systems. The model presented is efficient enough to incorporate novel parallel text at the single sentence level. 3. Strategies for updating our stream-based language model and translation model which demonstrate how such components can be successfully used in a streaming translation setting. This operates both within a single streaming environment and also in the novel situation of having to translate multiple streams. 4. Demonstration that recent data from the stream is beneficial to translation performance. Our stream-based SMT system is efficient for tackling massive volumes of new training data and offers-up new ways of thinking about translating web data and dealing with other natural language streams

Phonetic study and text mining of Spanish for English to Spanish translation system

Projecte realitzat en col.laboració amb el centre University of Southern Californi

An Empirical Evaluation Of Attention And Pointer Networks For Paraphrase Generation

In computer vision, one of the common practice to augment the image dataset is by creating new images using geometric transformation, which preserves the similarity. This data augmentation was one of the most significant factors to win the Image Net competition in 2012 with vast neural networks. Similarly, in speech recognition, we saw similar results by augmenting the signal by noise, slowing signal or accelerating it, and spectrogram modification. Unlike in computer vision and speech data, there haven not been many techniques explored to augment data in natural language processing (NLP). The only technique explored in text data is by lexical substitution, which only focuses on replacing words by synonyms. In this thesis, we investigate the use of different pointer networks with the sequence to sequence models, which have shown excellent results in neural machine translation (NMT) and text simplification tasks, in generating similar sentences using a sequence to sequence model and of the paraphrase dataset (PPDB). The evaluation of these paraphrases is carried out by augmenting the training dataset of IMDb movie review dataset and comparing its performance with the baseline model. We show how these paraphrases can affect downstream tasks. Furthermore, We train different classifiers to create a stable baseline for evaluation on IMDb movie dataset. To our best knowledge, this is the first study on generating paraphrases using these models with the help of PPDB dataset and evaluating these paraphrases in the downstream task