Emotion-Cause Pair Extraction: A New Task to Emotion Analysis in Texts
Table 4: Experimental results of all proposed models and variants using precision, recall, and F1-measure as metrics on the ECPE task with or without the pair filter.
['[EMPTY]', 'without emotion-cause pair filtering  [ITALIC] P', 'without emotion-cause pair filtering  [ITALIC] R', 'without emotion-cause pair filtering  [ITALIC] F1', 'with emotion-cause pair filtering  [ITALIC] P', 'with emotion-cause pair filtering  [ITALIC] R', 'with emotion-cause pair filtering  [ITALIC] F1', 'with emotion-cause pair filtering  [ITALIC] keep_ [ITALIC] rate']
With/Without pair filtering indicates whether we adopt a pair filter after applying a Cartesian product in the second step. keep_rate indicates the proportion of emotion-cause pairs in Pall that are finally retained after pair filtering. An obvious observation is that the F1 scores of all models on the ECPE task are significantly improved by adopting the pair filter. These results demonstrate the effectiveness of the pair filter. Specifically, by introducing the pair filter, some of the candidate emotion-cause pairs in Pall are filtered out, which may result in a decrease in the recall rate and an increase in precision.