D ialog VED : A Pre-trained Latent Variable Encoder-Decoder Model for Dialog Response Generation (2022)

Abstract: Dialog response generation in open domain is an important research topic where the main challenge is to generate relevant and diverse responses. In this paper, we propose a new dialog pre-training framework called DialogVED, which introduces continuous latent variables into the enhanced encoder-decoder pre-training framework to increase the relevance and diversity of responses. With the help of a large dialog corpus (Reddit), we pre-train the model using the following 4 tasks, used in training language models (LMs) and Variational Autoencoders (VAEs) literature: 1) masked language model; 2) response generation; 3) bag-of-words prediction; and 4) KL divergence reduction. We also add additional parameters to model the turn structure in dialogs to improve the performance of the pre-trained model. We conduct experiments on PersonaChat, DailyDialog, and DSTC7-AVSD benchmarks for response generation. Experimental results show that our model achieves the new state-of-the-art results on all these datasets.

## introduction pre-trained language models (plms) have been widely explored both in natural language understanding (nlu) and generation (nlg) in recent years, this pre-training and fine-tuning paradigm sheds light on various downstream tasks in natural language processing (nlp). compared with general pre-trained models, task-oriented pre-trained models (such as summarization, dialog and etc.), which is designed in line with task characteristics, may achieve better performance and be more robust. in this paper, we proposes a novel pre-trained dialog response generation model based on previous research. dialogue response generation (dsg) in open domain is a challenging task with a wide range of application scenarios. recent advances in dsg utilize pre-trained language models (plms) such as bert @xcite and gpt2 @xcite in two major categories. the first one focuses on how to fine-tune plms in downstream tasks and address the various application-specific needs and challenges @xcite . the second one augments dialog specific tasks into the plm training @xcite and then fine-tunes the new pre-trained model in downstream tasks. we study the latter in this paper. there is a proverbial one-to-many problem in dsg, i.e., a single dialog context could be followed by multiple reasonable responses. existing works introduce latent variables to model this problem. for example, vhred @xcite incorporates latent continuous variable into the sequenceto-sequence (seq2seq) rnn model to improve the diversity of generated responses. vae-seq2seq @xcite proposes variational attention to replace the vanilla encoder-decoder attention @xcite , to avoid attention to bypass the latent space and invalidate the latent variable. for controllability and interpretability, some discrete vaes have also been proposed, such as @xcite . recently, plato @xcite firstly introduces latent variables into their pre-training dialog model, where the authors introduce a k-way (@xmath0, and the pretrained model shows significant gains in multiple downstream response generation tasks. continuous latent variables besides discrete latent variables is popularly used for modeling one-to-many mapping in dialog system, but the potential of incorporating continuous latent variables with large-scale language pretraining is less explored. in this paper, we propose a pre-trained latent variable encoder-decoder model for dialog generation, which is called dialogved. in this model, we introduce a continuous latent variable into the enhanced encoder-decoder pre-training framework and we adopt the optimization techniques based on the vaes literature to learn the model with continuous latent variables. more specifically, we conduct the pre-training by optimizing the following 4 pre-training objectives simultaneously: 1) masked language spans loss to enhance the encoder's understanding of context, 2) response generation with n-gram loss to improve the decoder's planning ability, 3) kullback-leibler divergence loss to minimize the difference between the posterior and prior distribution of the latent variables, and 4) bag-ofwords loss to reduce posterior distribution collapse. in addition, we also explore the effect of absolute and relative position embeddings specific for conversational data on the model performance. we conduct experiments on three different kinds of conversation tasks: chit-chat, knowledge grounded conversation, and conversational question answering. experimental results verify the effectiveness and superiority of our model compared with the previous state-of-the-art method. we further carry out ablation study to better understand the impact of different components in the dialogved on model performance including latent space sizes, different decoding strategies, and position embeddings for turns and roles. the main contributions of this paper can be summarized as follows: 1) we propose a pretrained dialog model, which incorporates continuous latent variables into the enhanced encoder-decoder pre-training framework; 2) we explore the impact of latent variable sizes, different decoding strategies, and position embeddings for turns and roles in our model; 3) extensive experiments show that the proposed model achieves the new state-of-theart (sota) in multiple downstream tasks, and our model has better performance both on relevance and diversity than previous sota in response generation. ## experiments in this section, we firstly introduce the pre-training datasets and fine-tuning benchmarks in § 3.1, and implement details in § 3.2. then we present the main results in § 3.3. lastly, we analyze the influence of parameters and position embeddings in § 3.4. ## related work encoder-decoder dialog models unlike retrieval based dialogue systems @xcite , encoder-decoder models are widely used in dialog response generation, but it tends to generate generic responses and dull responses (e.g., i don't know). to enhance encoderdecode models and generate diverse responses, researchers have tried different approaches: using diversity promotion objectives @xcite , using different decoding algorithms @xcite , adding additional contents @xcite , or introducing large-scale knowledge graphs into dialog generation @xcite . another class of methods is using the latent variable to address the one-to-many problem in response generation. these models introduce discourse-level diversity and are able to generate diverse dialog responses @xcite @xcite . in this paper, we also adopt this approach and further we incorporate the latent variables both in the pre-training and fine-tuning.