Neural Models for Sequence Chunking
Table 3: Main results of our neural sequence chunking models on slot filling task.
['[EMPTY]', 'ATIS F1', 'ATIS Segment-F1', 'LARGE F1', 'LARGE Segment-F1']
(∼99% vs. ∼80%). This is because the ATIS data is much easier for segmentation than LARGE data. Also, compared to the small ATIS vocabulary (572 words), it is harder to learn a good segmentation model with a more complicated vocabulary (about 16k words) in LARGE data. , we observe that Model III has the best F1 score as compared to the baseline and other neural chunking models. Another observation is that Model I and II get better improvements over baseline even though they are poor at segmentation in slot filling task.