An Experimental Study of The Effects of Position Bias on Emotion Cause Extraction
Table 3: Comparing Precision, Recall and F1 of different baseline methods on the original benchmark.
['[BOLD] Method', '[BOLD] P', '[BOLD] R', '[BOLD] F1']
Even without observing the context, our approach achieved a result that is almost indistinguishable (¡ 2%) from the best baseline on F1. The high performance of our proposed method was contributed solely by our knowledge of the relative positions of emotion cause clause, in which the instances of “-1” position occupy a majority (54.45%). This result illustrates the bias in the benchmark. Many models, especially deep neural network models, can achieve high performance because of this position bias and not the ability of learning deep semantics from texts, as originally hypothesized. We revisit this idea in Section 5, where some deep neural network models will be re-evaluated on a modified version of this benchmark that has a different, more balanced distribution of cause clauses in terms of their position relative to the emotion clause.