Daniel Marcu

Extracting Structured Scholarly Information from the Machine Translation Literature

2016

Understanding the experimental results of a scientific paper is crucial to understanding its contribution and to comparing it with related work.We introduce a structured, queryable representation for experimental results and a baseline system that automatically …

Unsupervised Neural Hidden Markov Models

2016

In this work, we present the first results for neuralizing an Unsupervised Hidden Markov Model. We evaluate our approach on tag induction. Our approach outperforms existing generative models and is competitive with the state-of-the-art though …