Character-Level Feature Extractionwith Densely Connected Networks
Table 3: Comparison with state-of-the-art approaches in the literature.
['[BOLD] Slot Approach', '[BOLD] Slot F1', '[BOLD] POS Approach', '[BOLD] POS Acc.', '[BOLD] NER Approach', '[BOLD] NER F1']
The proposed method was able to surpass the previous state-of-the-art result on the ATIS dataset with a large margin, even without the CRF layer. With the help of CRF, our method obtains a new state-of-the-art result with a 96.62 F1-score. For the POS tagging task with PTB WSJ dataset, we obtain a new state-of-the-art result with a 97.73% accuracy with the model without a CRF layer. Interestingly, utilizing a CRF layer on this model degraded the performance on this task whereas it helped with the other two tasks. We hypothesize that this is due to the fact that unlike the other two tasks where there are many hard constraints between labels (e.g. an O tag cannot be followed by I- tags), the label dependencies are more ”soft” on POS tagging task. In the latter case, it is possible that naively taking label transition probability into account could have a negative impact on performance. On the task of recognizing named entities, we obtain a result that is comparable to state-of-the-art with a 91.13 F1-score when a CRF layer is used. Like in slot tagging task, utilizing CRF lead to a significant increase in performance. It is notable that all results from our method are achieved without depending on any hand-crafted or language/task-specific features (e.g. capitalization, character type, gazetteer), whereas most previous approaches utilizes one or more type of such features. This fact supports the generalizability of the proposed method.