Jointly Optimizing Diversity and Relevance in Neural Response Generation

By Xiang Gao, Sungjin Lee, Yizhe Zhang, Chris Brockett, Michel Galley, Jianfeng Gao, William B. Dolan. 2019

83 citations recorded by the source corpus.

Although recent neural conversation models have shown great potential, they often generate bland and generic responses. While various approaches have been explored to diversify the output of the conversation model, the improvement often comes at the cost of decreased relevance. In this paper, we propose a SpaceFusion model to jointly optimize diversity and relevance that essentially fuses the latent space of a sequence-to-sequence model and that of an autoencoder model by leveraging novel regularization terms. As a result, our approach induces a latent space in which the distance and direction from the predicted response vector roughly match the relevance and diversity, respectively. This property also lends itself well to an intuitive visualization of the latent space. Both automatic and human evaluation results demonstrate that the proposed approach brings significant improvement compared to strong baselines in both diversity and relevance.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Natural Language Processing

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Citations in the corpus

Towards a Human-like Open-Domain Chatbot (2020, background): “Recent works also explore new frameworks such as adversarial learning (Li et al., 2017; Zhang et al., 2018c), variational autoencod-ing (Zhao et al., 2017; Gu et al., 2019), or both (Gao et al., 2019b) at the cost of added complexity and less scalability.”
DIALOGPT : Large-Scale Generative Pre-training for Conversational Response Generation (2019, background): “Modelling conversations, however, presents distinct challenges in that human dialogue, which en-capsulates the possibly competing goals of two participants, is intrinsically more diverse in the range of potential responses (Li et al., 2016a; Zhang et al., 2018; Gao et al., 2019a,b).”
Challenges in Building Intelligent Open-domain Dialog Systems (2019, background): “[35] proposed a SpaceFusion model which induces a latent space that fuses the two latent spaces generated by Seq2Seq and auto-encoder, respectively, in such a way that after encoding X ⊕ C into a vector in the space, the distance and direction from the predicted response vector given the context rou”
Conversing by Reading: Contentful Neural Conversation with On-demand Machine Reading (2019, background): “While end-to-end neural conversation models (Shang et al., 2015; Sordoni et al., 2015; Vinyals and Le, 2015; Serban et al., 2016; Li et al., 2016a; Gao et al., 2019a, etc.) are effective in learning how to be fluent, their responses are often vacuous and uninformative.”
A Controllable Model of Grounded Response Generation (2020, background): “…generation models were characterized by bland and evasive responses (Li et al. 2016a), surprisingly human-like conversations can be generated using recent diversity-enhancing strategies (Holtzman et al. 2020; Gao et al. 2019a) and massive GPT-2 style models (Radford et al. 2019; Zhang et al. 2020).”
Optimus: Organizing Sentences via Pre-trained Modeling of a Latent Space (2020, methodology): “The ﬁne-tuning objective is: where L fusion is the same with fusion term in (Gao et al., 2019a We benchmark representative baselines and state-of-the-art approaches, including: ( i ) Seq2Seq: a generalized sequence-to-sequence model with hierarchical RNN encoder (Serban et al., 2016); ( ii )…”
PLATO: Pre-trained Dialogue Generation Model with Discrete Latent Variable (2019, background): “Recently, some attempts (Golovanov et al., 2019; Wolf et al., 2019; Zhang et al., 2019) have been made to adapt generative language models GPT or GPT-2 for dialogue generation.”
Grounded Conversation Generation as Guided Traverses in Commonsense Knowledge Graphs (2019, background): “Nevertheless, the degenerating of irrelevant, off-topic, and non-useful responses is still one of the main challenges in conversational generation (Rosset et al., 2020; Tang et al., 2019; Zhang et al., 2018; Gao et al., 2019).”
Improving Generative Visual Dialog by Answering Diverse Questions (2019, unknown): “…a diverse set of natural language dialog responses have previously been explored in Zhang et al. (2018) via adversarial information maximization, in Gao et al. (2019) by jointly modeling diversity and relevance in a shared latent space, and in Li et al. (2016a) us-ing a maximum mutual information…”
Microsoft Icecaps: An Open-Source Toolkit for Conversation Modeling (2019, background): “Multi-task learning is a powerful training paradigm that promotes robust feature represen-1 tations (Gao et al., 2019b; Liu et al., 2019).”
Improving Dialog Evaluation with a Multi-reference Adversarial Dataset and Large Scale Pretraining (2020, background): “…been made to build dialog datasets with multiple relevant responses ( i.e. , multiple references), but these datasets are either very small (1000 contexts) (Moghe et al., 2018; Gupta et al., 2019) or automatically constructed from Reddit conversations, hence, potentially noisy (Gao et al., 2019).”