Abstract
Recent advances in neural sequence-to-sequence models have led to promising results for several language generation-based tasks, including dialogue response generation, summarization, and machine translation. However, these models are known to have several problems, especially in the context of chit-chat based dialogue systems: they tend to generate short and dull responses that are often too generic. Furthermore, these models do not ground conversational responses on knowledge and facts, resulting in turns that are not accurate, informative and engaging for the users. In this paper, we propose and experiment with a series of response generation models that aim to serve in the general scenario where in addition to the dialogue context, relevant unstructured external knowledge in the form of text is also assumed to be available for models to harness. Our proposed approach extends pointer-generator networks (See et al., 2017) by allowing the decoder to hierarchically attend and copy from external knowledge in addition to the dialogue context. We empirically show the effectiveness of the proposed model compared to several baselines including (Ghazvininejadet al., 2018; Zhang et al., 2018) through both automatic evaluation metrics and human evaluation on ConvAI2 dataset.
Highlights
Deep neural networks have achieved stateof-the-art results in various tasks including computer vision, natural language and speech processing
4.3.1 Automatic Evaluation In Table 1, we present our results in comparison with the existing and proposed baseline models
We report the performance of each model across several metrics commonly used for evaluation of text generation models including perplexity, corpus BLEU (Papineni et al, 2002), ROUGE-L (Lin and Och, 2004), CIDEr (Vedantam et al, 2014)
Summary
Deep neural networks have achieved stateof-the-art results in various tasks including computer vision, natural language and speech processing. Achieving satisfactory performance on dialogue still remains an open problem This is because dialogues can have multiple valid responses with varying semantic content. Most common problems include inconsistency in personality, dull and generic responses, and unawareness of long-term dialogue context. To alleviate these limitations, we turn our focus on a different problem setting for dialogue response generation where the model is provided a set of relevant textual facts (speaker persona descriptions) and is allowed to harness this knowledge when generating responses in a multi-turn dialogue. Towards encouraging generation of more specific and appropriate responses while avoiding generic and dull ones, we use a hierarchical pointer network in our model such that it can copy content from two sources: current dialogue history and persona descriptions
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