Advancing NLP with Cognitive Language Processing Signals
Table 3: Results of the multi-task learning experiments on NER, binary and ternary sentiment analysis.
['[BOLD] main task', '[BOLD] aux task(s)', '[BOLD] accuracy']
Note that only the best feature combinations are included in the table. Learning word frequency as an auxiliary task is a strong baseline. Learning gaze and EEG features as auxiliary tasks does not improve the performance over the single-task baseline for NER and only minimally for sentiment analysis. Learning two auxiliary tasks, a gaze of EEG feature and word frequency in parallel yields modest improvements over the frequency baseline.