Paper: Lateen EM: Unsupervised Training with Multiple Objectives Applied to Dependency Grammar Induction

ACL ID D11-1117
Title Lateen EM: Unsupervised Training with Multiple Objectives Applied to Dependency Grammar Induction
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2011
Authors

We present new training methods that aim to mitigate local optima and slow convergence in unsupervised training by using additional im- perfect objectives. In its simplest form, lateen EM alternates between the two objectives of ordinary “soft” and “hard” expectation max- imization (EM) algorithms. Switching objec- tives when stuck can help escape local optima. We find that applying a single such alternation already yields state-of-the-art results for En- glish dependency grammar induction. More elaborate lateen strategies track both objec- tives, with each validating the moves proposed by the other. Disagreements can signal earlier opportunities to switch or terminate, saving it- erations. De-emphasizing fixed points in these ways eliminates some guesswork from tuning EM. An evalu...