SparseMAP: Differentiable Sparse Structured Inference
Table 1: Unlabeled attachment accuracy scores for dependency parsing, using a bi-LSTM model (kg). SparseMAP and its margin version, m-SparseMAP, produce the best parser on 4/5 datasets. For context, we include the scores of the CoNLL 2017 UDPipe baseline, which is trained under the same conditions (udpipe).
['Loss', 'en', 'zh', 'vi', 'ro', 'ja']
We experiment with 5 languages, diverse both in terms of family and in terms of the amount of training data (ranging from 1,400 sentences for Vietnamese to 12,525 for English). indicate that the SparseMAP losses outperform the SVM and CRF losses on 4 out of the 5 languages considered. This suggests that SparseMAP is a good middle ground between MAP-based and marginal-based losses in terms of smoothness and gradient sparsity.