On Incorporating Structural Information to improve Dialogue Response Generation
Table 3: Performance of different hybrid architectures to combine structural information with sequence information
['Emb', 'Seq-GCN BLEU', 'Seq-GCN ROUGE', 'Seq-GCN ROUGE', 'Seq-GCN ROUGE', 'Str-LSTM BLEU', 'Str-LSTM ROUGE', 'Str-LSTM ROUGE', 'Str-LSTM ROUGE', 'Par-GCN-LSTM BLEU', 'Par-GCN-LSTM ROUGE', 'Par-GCN-LSTM ROUGE', 'Par-GCN-LSTM ROUGE']
The response generation task of our dataset is a span based generation task where phrases of text are expected to be copied or generated as they are. The sequential information is thus crucial to reproduce these long phrases from background knowledge. The Str-LSTM model can better capture sequential information with structurally and syntactically rich representations obtained through the initial GCN layer. The Par-GCN-LSTM model performs second best. However, in the parallel model, the LSTM cannot leverage the structural information directly and relies only on the word embeddings. Seq-GCN model performs the worst among all the three as the GCN layer at the top is likely to modify the sequence information from the LSTMs.