Adversarial Multi-Criteria Learning for Chinese Word Segmentation
Table 2: Results of proposed models on test sets of eight CWS datasets. There are three blocks. The first block consists of two baseline models: Bi-LSTM and stacked Bi-LSTM. The second block consists of our proposed three models without adversarial training. The third block consists of our proposed three models with adversarial training. Here, P, R, F, OOV indicate the precision, recall, F value and OOV recall rate respectively. The maximum F values in each block are highlighted for each dataset.
['Models LSTM', 'Models P', 'MSRA 95.13', 'AS 93.66', 'PKU 93.96', 'CTB 95.36', 'CKIP 91.85', 'CITYU 94.01', 'NCC 91.45', 'SXU 95.02', 'Avg. 93.81']
(3) In the third block, we introduce adversarial training. By introducing adversarial training, the performances are further boosted, and Model -I is slightly better than Model-II and Model-III. The adversarial training tries to make shared layer keep criteria-invariant features. The reason may be that the shared parameters bias to other segmentation criteria and introduce noisy features into shared parameters. When we additionally incorporate the adversarial strategy, we observe that the performance on MSRA is improved and outperforms the baseline results. We could also observe the improvements on other datasets. However, the boost from the adversarial strategy is not significant. The main reason might be that the proposed three sharing models implicitly attempt to keep invariant features by shared parameters and learn discrepancies by the task layer. We further investigate the benefits of the proposed models by comparing the error distributions between the single-criterion learning (baseline model Bi-LSTM) and multi-criteria learning ( Model- I and Model