A Randomized Link Transformer for Diverse Open-Domain Dialogue Generation (2022)

Abstract: A major issue in open-domain dialogue generation is the agent’s tendency to generate repetitive and generic responses. The lack in response diversity has been addressed in recent years via the use of latent variable models, such as the Conditional Variational Auto-Encoder (CVAE), which typically involve learning a latent Gaussian distribution over potential response intents. However, due to latent variable collapse, training latent variable dialogue models are notoriously complex, requiring substantial modification to the standard training process and loss function. Other approaches proposed to improve response diversity also largely entail a significant increase in training complexity. Hence, this paper proposes a Randomized Link (RL) Transformer as an alternative to the latent variable models. The RL Transformer does not require any additional enhancements to the training process or loss function. Empirical results show that, when it comes to response diversity, the RL Transformer achieved comparable performance compared to latent variable models.

## introduction open-domain dialogue generation refers to the task of generating coherent, natural and human-like dialogue given solely the dialogue context (also known as the dialogue history). the development of opendomain dialogue agents that can engage humans in seamless general conversation (or chit-chat) is one of the main objectives of conversational ai. currently, however, agents display a tendency to generate repetitive and generic dialogue responses, which negatively impact both naturalness and contextual coherence. recently, researchers have turned to latent variable models, specifically the conditional variational auto encoder (cvae) @xcite , to address this issue @xcite @xcite . in addition to open-domain dialogue generation, latent variable models have also been applied to related tasks such as personalized dialogue @xcite @xcite , empathetic dialogue @xcite @xcite , and topical dialogue generation @xcite . these works have generally involved modelling the potential dialogue response intents as a latent gaussian prior, which is typically generated by a multi-layer perceptron (mlp). during inference, a latent instance is sampled from the generated gaussian prior via the reparameterization trick and fed to the decoder. stochasticity is induced during the response generation via such random sampling process. however, even though latent variable models are effective at improving response diversity, they are notoriously hard to train primarily due to the kullback-liebler (kl) vanishing problem. usually, this problem is addressed via kl annealing or incorporating a bag-of-words loss. while kl annealing requires tuning the weighting hyperparameter β, attaining the bag-of-words loss involves defining an additional task of predicting the response bag-of-words. this results in additional complexity during training. several other approaches to promoting response diversity which involve introducing an alternate loss function such as the maximum mutual information (mmi) objective @xcite , the inverse token frequency (itf) objective @xcite , and the inverse n-gram frequency (inf) objective @xcite , require considerable additional computation steps. on the other hand, adversarial learning-based @xcite and embedding augmentation-based @xcite approaches require extensive modifications to the standard training process, resulting in a significant increase in training complexity. hence, inspired by randomization-based neural networks @xcite and the random vector functional link (rvfl) neural network @xcite in particular, we introduce a novel randomized link (rl) transformer. an alternative to latent variable models which does not require any modifications or enhancements to the standard training process or loss function. in other words, the rl transformer can be trained via standard gradient descent solely on the standard negative log-likelihood loss. experimental results on the dailydialog @xcite and empatheticdialogues @xcite corpora show that our rl transformer successfully improves the diversity of the generated response, achieving comparable response diversification relative to latent variable approaches. in addition, compared to the latent variable models, responses generated by our rl transformer are noticeably more fluent and contextually coherent. the remainder of this paper is organized as follows: section 2 provides additional background information regarding open-domain dialogue generation and rvfl neural networks; section 3 describes the proposed rl transformer in detail; section 4 provides details regarding our implementations and experiments; section 5 presents the experimental results as well as our analysis of the results; section 6 concludes the paper. ## conclusion in this paper, we have proposed a novel rl transformer which successfully improves response diversity in the task of open-domain dialogue generation. this is achieved by inducing stochasticity in the self-attention and the feed forward networks of a transformer via randomized layers and direct links. experimental results on the dailydialog and empa-theticdialogues corpora show that, compared to latent variable models, the rl transformer achieved comparable levels of diversification while further improving on contextual coherence and fluency. in the future, the rl transformer can be adapted for related dialogue generation tasks such as personalized, empathetic or topical dialogue generation.