Viterbi Training Improves Unsupervised Dependency Parsing.

We show that Viterbi (or "hard") EM is well-suited to unsupervised grammar induction. It is more accurate than standard inside-outside re-estimation (classic EM), significantly faster, and simpler. Our experiments with Klein and Manning's Dependency Model with Valence (DMV) attain state-of-the-art performance --- 44.8% accuracy on Section 23 (all sentences) of the Wall Street Journal corpus --- without clever initialization; with a good initializer, Viterbi training improves to 47.9%. This generalizes to the Brown corpus, our held-out set, where accuracy reaches 50.8% --- a 7.5% gain over previous best results. We find that classic EM learns better from short sentences but cannot cope with longer ones, where Viterbi thrives. However, we explain that both algorithms optimize the wrong objectives and prove that there are fundamental disconnects between the likelihoods of sentences, best parses, and true parses, beyond the well-established discrepancies between likelihood, accuracy and extrinsic performance.