Some of Them Can be Guessed!Exploring the Effect of Linguistic Context in Predicting Quantifiers
Table 3: Responses by humans (top) and AttCon-LSTM (bottom) in 3-Sent (val). Values in bold are the highest in the row.
['[ITALIC] none  [ITALIC] few', '[BOLD] 19 5', '1  [BOLD] 9', '2', '0 6', '2 5', '0', '0 3', '0', '12 2']
To check whether humans and the models make similar errors, we look into the distribution of responses in 3-Sent (val), which is the most comparable setting with respect to accuracy. Human errors generally involve quantifiers that display a similar magnitude as the correct one. To illustrate, ‘some’ is chosen in place of ‘a few’, and ‘most’ in place of either ‘almost all’ or ‘more than half’. A similar pattern is observed in the model’s predictions, though we note a bias toward ‘more than half’.