1,678 research outputs found
Analyzing and Interpreting Neural Networks for NLP: A Report on the First BlackboxNLP Workshop
The EMNLP 2018 workshop BlackboxNLP was dedicated to resources and techniques
specifically developed for analyzing and understanding the inner-workings and
representations acquired by neural models of language. Approaches included:
systematic manipulation of input to neural networks and investigating the
impact on their performance, testing whether interpretable knowledge can be
decoded from intermediate representations acquired by neural networks,
proposing modifications to neural network architectures to make their knowledge
state or generated output more explainable, and examining the performance of
networks on simplified or formal languages. Here we review a number of
representative studies in each category
Learning Word Representations with Hierarchical Sparse Coding
We propose a new method for learning word representations using hierarchical
regularization in sparse coding inspired by the linguistic study of word
meanings. We show an efficient learning algorithm based on stochastic proximal
methods that is significantly faster than previous approaches, making it
possible to perform hierarchical sparse coding on a corpus of billions of word
tokens. Experiments on various benchmark tasks---word similarity ranking,
analogies, sentence completion, and sentiment analysis---demonstrate that the
method outperforms or is competitive with state-of-the-art methods. Our word
representations are available at
\url{http://www.ark.cs.cmu.edu/dyogatam/wordvecs/}
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