Neural networks are a family of powerful machine learning models and this book focuses on
their application to natural language data. The first half of the book (Parts I and II) covers the …

We describe a simple neural language model that relies only on character-level inputs.
Predictions are still made at the word-level. Our model employs a convolutional neural …

We describe an open-source toolkit for statistical machine translation whose novel
contributions are (a) support for linguistically motivated factors,(b) confusion network …

This paper presents a scalable method for integrating compositional morphological
representations into a vector-based probabilistic language model. Our approach is …

We review developments in the SRI Language Mod-eling Toolkit (SRILM) since 2002, when
a previous paper on SRILM was published. These developments include measures to make …

On Achieving and Evaluating Language-Independence in NLP Page 1 Linguistic Issues in
Language Technology LiLT Submitted, October 2011 On Achieving and Evaluating …

Linguistic similarity is multi-faceted. For instance, two words may be similar with respect to
semantics, syntax, or morphology inter alia. Continuous word-embeddings have been …

The book will appeal to scholars and advanced students of morphology, syntax,
computational linguistics and natural language processing (NLP). It provides a critical and …

The prototypical NLP experiment trains a standard architecture on labeled English data and
optimizes for accuracy, without accounting for other dimensions such as fairness …

Most previous work on trainable language generation has focused on two paradigms:(a)
using a statistical model to rank a set of generated utterances, or (b) using statistics to inform …