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We consider the task of generating dialogue responses from backgroundknowledge comprising of domain specific resources. Specifically, given aconversation around a movie, the task is to generate the next response based onbackground knowledge about the movie such as the plot, review, Reddit commentsetc. This requires capturing structural, sequential and semantic informationfrom the conversation context and the background resources. This is a new taskand has not received much attention from the community. We propose a newarchitecture that uses the ability of BERT to capture deep contextualizedrepresentations in conjunction with explicit structure and sequenceinformation. More specifically, we use (i) Graph Convolutional Networks (GCNs)to capture structural information, (ii) LSTMs to capture sequential informationand (iii) BERT for the deep contextualized representations that capturesemantic information. We analyze the proposed architecture extensively. To thisend, we propose a plug-and-play Semantics-Sequences-Structures (SSS) frameworkwhich allows us to effectively combine such linguistic information. Through aseries of experiments we make some interesting observations. First, we observethat the popular adaptation of the GCN model for NLP tasks where structuralinformation (GCNs) was added on top of sequential information (LSTMs) performspoorly on our task. This leads us to explore interesting ways of combiningsemantic and structural information to improve the performance. Second, weobserve that while BERT already outperforms other deep contextualizedrepresentations such as ELMo, it still benefits from the additional structuralinformation explicitly added using GCNs. This is a bit surprising given therecent claims that BERT already captures structural information. Lastly, theproposed SSS framework gives an improvement of 7.95% over the baseline.

On Incorporating Structural Information to improve Dialogue Response Generation