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Data needs cleaning before machine learning can find meaning

After all, most machine learning tools are open source and there are plenty of commercial machine learning platforms that promise to run a number of models on your data.

Introduction

The paper presents a database of ranked English and Spanish paraphrases derived by:

Extracting lexical, phrasal, and syntactic paraphrases from large bilingual parallel corpora.
Computing the similarity scores for the pair of paraphrases using Google ngrams and the Annotated Gigaword corpus.

Link to the paper

Extracting Paraphrase from Bilingual Text

The basic idea is that if two English strings e1 and e2 translate to the same foreign string f (also c...

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Paper summary: Never-Ending Learning

I am trying a new initiative — a-paper-a-week. This post is the summary of the very first paper I read as a part of this initiative. The idea behind writing such posts is to have a summary of these papers — for my future self and for others. I believe that summarizing an academic writing helps to understand it better.

For my first week, I picked up the paper titled Never-Ending Learning. It is a part of Read The Web project at Carnegie Mellon University. It introduces a new machine learning paradigm called never-ending learning. In most machine learning applications, a system aims to learn a single function or data model from a given data set. But in this new paradigm, the system learns the different type of functions from years of experience in a way that previously learned knowledge makes way for accumulating new types of knowledge. Stagnation and performance plateaus are avoided by the ability to formulate new learning tasks.

Summary

A never-ending learning problem has 2 components — a...

Introduction

Introduces fastText, a simple and highly efficient approach for text classification.
At par with deep learning models in terms of accuracy though an order of magnitude faster in performance.
Link to the paper
Link to code

Architecture

Built on top of linear models with a rank constraint and a fast loss approximation.
Start with word representations that are averaged into text representation and feed them to a linear classifier.
Think of text representation as a hidden s...

Introduction

The paper presents a suite of benchmark tasks to evaluate end-to-end dialogue systems such that performing well on the tasks is a necessary (but not sufficient) condition for a fully functional dialogue agent.
Link to the paper

Dataset

Created using large-scale real-world sources - OMDB (Open Movie Database), MovieLens and Reddit.
Consists of ~75K movie entities and ~3.5M training examples.

Introduction

Extracting Paraphrase from Bilingual Text

Introduction

Architecture

Introduction

Dataset

Tasks