Causal Explanation Analysis on Social Media
Table 6: The effect of Word-level LSTM (Word LSTM) and discourse argument LSTM (DA AVG LSTM) for causality prediction (CP) and causal explanation identification (CEI). Note that, as described in methods, there are architectural differences for CP and CEI models with the same names, most notably that the output layer is always a single classification for CP and a sequence of classifications for CEI.
['[BOLD] Model', 'CP (F1)', 'CEI (F1)']
For causality prediction, we experimented with only word tokens in the whole message without help of Word-level LSTM layer Also, when we used the average of the sequence of word embeddings of each discourse argument as an input to the DA-level LSTM and it caused F1 drop of 0.073. This suggests that the information gained from both the interaction of words in and in between discourse arguments help when the model utilizes the distributional representation of the texts. Also, when we ran DA-level LSTM on the average vectors of the word sequences of each discourse argument of messages, F1 decreased to 0.818. This follows the similar pattern observed from other types of models performances (i.e., SVMs and Random Forest classifiers) that the models with higher complexity for capturing the interaction of discourse arguments tend to identify causal explanation with the higher accuracies.