Neural Models for Sequence Chunking
Table 5: Segment-F1 on different chunk-lengths.
['[EMPTY]', 'ATIS 1', 'ATIS 2', 'ATIS >=3', 'LARGE 1', 'LARGE 2', 'LARGE >=3']
We further investigate the segmentation process and show the segmentation F1-score The results demonstrate that the poor performance on LARGE data is mainly due to the bad performance on identifying long chunks (around 55%). Our Model III improves this score by 2% over baseline (54.88% vs. 56.59%). As the absolute performance on this subset is still low, future research efforts should focus on improving this performance. In addition, Model I and II get comparable segmentation results with the baseline model on one-words chunks, while being worse on longer chunks, further supporting this analysis. Finally, our Model III could achieve better F1 score on all chunk lengths.