One noted issue of vector-quantized variational autoencoder (VQ-VAE) is that the learned discrete representation uses only a fraction of the full capacity of the codebook, also known as codebook collapse. We hypothesize that the training scheme of VQ-VAE, which involves some carefully designed heuristics, underlies this issue. In this paper, we propose a new training scheme that extends the standard VAE via novel stochastic dequantization and quantization, called stochastically quantized variational autoencoder (SQ-VAE). In SQ-VAE, we observe a trend that the quantization is stochastic at the initial stage of the training but gradually converges toward a deterministic quantization, which we call self-annealing. Our experiments show that SQ-VAE improves codebook utilization without using common heuristics. Furthermore, we empirically show that SQ-VAE is superior to VAE and VQ-VAE in vision- and speech-related tasks.

Dialog response ranking is used to rank response candidates by considering their relation to the dialog history. Although researchers have addressed this concept for open-domain dialogs, little attention has been focused on task-oriented dialogs. Furthermore, no previous studies have analyzed whether response ranking can improve the performance of existing dialog systems in real human-computer dialogs with speech recognition errors. In this paper, we propose a context-aware dialog response re-ranking system. Our system reranks responses in two steps: (1) it calculates matching scores for each candidate response and the current dialog context; (2) it combines the matching scores and a probability distribution of the candidates from an existing dialog system for response re-ranking. By using neural word embedding-based models and handcrafted or logistic regression-based ensemble models, we have improved the performance of a recently proposed end-to-end task-oriented dialog system on real dialogs with speech recognition errors.