Empower Sequence Labeling with Task-Aware Neural Language Model
Table 5: Accuracy on the WSJ dataset. We mark models adopting pre-trained word embedding as †, and record models which leverage language models as ‡.
['Ind & Model', 'Accuracy Type', 'Accuracy Value (±std)']
Compared Methods We consider three classes of baseline sequence labeling methods in our experiments. [noitemsep,nolistsep] Sequence Labeling Only. Joint Model with Other Supervised Tasks. Joint Model with Language Model: Language models have been employed by some recent works to extract knowledge from raw text and thus enhancing sequence labeling task. leverages pre-trained language models and shows the effectiveness with the large external corpus, but the large model scale and long training time make it hard to re-run this model. In this section, we focus on the comparisons between LM-LSTM-CRF and previous state-of-the-arts, including both effectiveness and efficiency. Moreover, even for those baselines with extra resources, LM-LSTM-CRF beats most of them and is only slightly worse than TagLM (index 4)