Semi-supervised Domain Adaptation for Dependency Parsing via Improved Contextualized Word Representations

In recent years, parsing performance is dramatically improved on in-domain texts thanks to the rapid progress of deep neural network models. The major challenge for current parsing research is to improve parsing performance on out-of-domain texts that are very different from the indomain training data when there is only a small-scale out-domain labeled data. To deal with this problem, we propose to improve the contextualized word representations via adversarial learning and fine-tuning BERT processes. Concretely, we apply adversarial learning to three representative semi-supervised domain adaption methods, i.e., direct concatenation (CON), feature augmentation (FA), and domain embedding (DE) with two useful strategies, i.e., fused targetdomain word representations and orthogonality constraints, thus enabling to model more pure yet effective domain-specific and domain-invariant representations. Simultaneously, we utilize a large-scale target-domain unlabeled data to fine-tune BERT with only the language model loss, thus obtaining reliable contextualized word representations that benefit for the cross-domain dependency parsing. Experiments on a benchmark dataset show that our proposed adversarial approaches achieve consistent improvements, and fine-tuning BERT further boosts the parsing accuracy by a large margin. Our single model achieves the same state-of-the-art performance as the top submitted system in the NLPCC-2019 shared task, which uses ensemble models and BERT.