On Incorporating Structural Information to improve Dialogue Response Generation
Table 2: Performance of components within the SSS framework.
['Emb', 'Paradigm', 'BLEU', 'ROUGE 1', 'ROUGE 2', 'ROUGE L']
The Sem models condition the response generation directly on the word embeddings. We observe that ELMo and BERT perform much better than GloVe embeddings. The Sem+Seq models condition the decoder on the representation obtained after passing the word embeddings through the LSTM layer. These models outperform their respective Sem models. The gain with ELMo is not significant because the underlying architecture already has two BiLSTM layers whcih are anyways being fine-tuned for the task. Hence the addition of one more LSTM layer may not contribute to learning any new sequential word information. Earlier work has suggested that deep contextualized representations capture syntax and co-reference relations Peters et al. We observe that the SSS model using the simpler GloVe embedding outperforms the ELMo Sem+Seq model and performs slightly better than the BERT Sem+Seq model.