Conversational Analysis using Utterance-level Attention-based Bidirectional Recurrent Neural Networks
Table 1: Accuracies (in %) on the SwDA test set, baseline with no context (NC) and Utt-Att-BiRNN model with context (WC)
['[BOLD] Models', '[BOLD] NC', '[BOLD] WC']
Character LM and word-embeddings mean utterance representations perform quite well for this task. Surprisingly, the word-embeddings mean representations of the utterances used from the ConceptNet seem to show good results given the fact of the low dimensionality of the embeddings (300) compared to character LM (4096) size.