Domain Adaptation for Parsing

We compare two different methods in domain adaptation applied to constituent parsing: parser combination and cotraining, each used to transfer information from the source domain of news to the target domain of natural dialogs, in a setting without annotated data. Both methods outperform the baselines and reach similar results. Parser combination profits most from the large amounts of training data combined with a robust probability model. Co-training, in contrast, relies on a small set of higher quality data.

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