With the advent of Web 2.0, there exist many online platforms that result in massive textual-
data production. With ever-increasing textual data at hand, it is of immense importance to …

Over the past few years, neural networks have re-emerged as powerful machine-learning
models, yielding state-of-the-art results in fields such as image recognition and speech …

Neural text generation is a key tool in natural language applications, but it is well known
there are major problems at its core. In particular, standard likelihood training and decoding …

Recent advances in language modeling using recurrent neural networks have made it
viable to model language as distributions over characters. By learning to predict the next …

Over-dependence on domain ontology and lack of knowledge sharing across domains are
two practical and yet less studied problems of dialogue state tracking. Existing approaches …

Abstract Machine learning research has advanced in multiple aspects, including model
structures and learning methods. The effort to automate such research, known as AutoML …

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 …

Pre-trained word embeddings learned from unlabeled text have become a standard
component of neural network architectures for NLP tasks. However, in most cases, the …

How and to what extent does BERT encode syntactically-sensitive hierarchical information
or positionally-sensitive linear information? Recent work has shown that contextual …

With the advent of Web 2.0, people became more eager to express and share their opinions
on web regarding day-to-day activities and global issues as well. Evolution of social media …