Modeling Ambiguity, Subjectivity, and Diverging Viewpointsin Opinion Question Answering Systems
TABLE III: Results on binary questions where multiple noisy labels are involved.
['[EMPTY]', '[BOLD] MoE', '[BOLD] KL-MoE', '[BOLD] EM-MoE', '[BOLD] EM-MoE-S']
We notice that while KL-MoE is not able to improve upon MoE for all categories, EM-MoE and EM-MoE-S yield consistent improvements in all cases. This improvement is relatively large for some large categories, such as Cell Phones & Accessories and Electronics. Incorporating subjective features (EM-MoE-S) seems to help most for large categories, indicating that it is useful when enough training data is available to make the additional parameters affordable.