| Challenge: | Neural conversation models are easy to generate bland and generic responses . however, their improvement of generating high-quality responses is still unsatisfactory . |
| Approach: | They propose to use a discrete latent variable with an explicit semantic meaning to improve the conditional variational autoencoder on short-text conversation. |
| Outcome: | The proposed model outperforms various kinds of generation models under automatic and human evaluations and generates more diverse and informative responses. |
Similar Papers
Generating Relevant and Coherent Dialogue Responses using Self-Separated Conditional Variational AutoEncoders (2021.acl-long)
Copied to clipboard
| Challenge: | Conditional Variational AutoEncoders (CVAE) can enhance the diversity and informativeness of responses in open-domain dialogue generation tasks. |
| Approach: | They propose a Conditional Variational AutoEncoder (CVAE) that regularizes latent variables and introduces group information to regularize them. |
| Outcome: | Empirical results show that the proposed model can significantly boost responses in well-established open-domain dialogue datasets. |
Variational Autoregressive Decoder for Neural Response Generation (D18-1)
Copied to clipboard
| Challenge: | Existing variational Bayesian models generate responses from a single latent variable, which is not sufficient to model high variability in responses. |
| Approach: | They propose a conditional variable auto-encoder that sequentially introduces latent variables to condition the generation of each word in the response sequence. |
| Outcome: | Empirical results show that the proposed model improves on state-of-the-art models on Opensubtitle and Reddit datasets. |
Diversifying Reply Suggestions Using a Matching-Conditional Variational Autoencoder (N19-2)
Copied to clipboard
| Challenge: | Automated reply suggestions (SR) are becoming common in many popular applications such as Gmail (2016) . |
| Approach: | They propose a constrained-sampling approach to make the variational inference efficient for a commercial instant-messaging system. |
| Outcome: | The proposed model increases diversity without losing relevance in offline experiments. |
Speculative Sampling in Variational Autoencoders for Dialogue Response Generation (2021.findings-emnlp)
Copied to clipboard
| Challenge: | Existing studies have tried to improve variational models but they fail to learn proper mappings. |
| Approach: | They propose to use a variable-based sampling technique to find the most probable one from redundantly sampled latent variables to tie up the variable with a given response. |
| Outcome: | The proposed method is effective in response generation with massive dialogue data constructed from Twitter posts. |
Jointly Optimizing Diversity and Relevance in Neural Response Generation (N19-1)
Copied to clipboard
| Challenge: | Recent neural conversation models often generate bland and generic responses . however, the improvement often comes at the cost of decreased relevance . |
| Approach: | They propose a spacefusion model to jointly optimize diversity and relevance that fuses the latent space of a sequence-to-sequence model and that of an autoencoder model by leveraging novel regularization terms. |
| Outcome: | The proposed model improves diversity and relevance compared to baselines in both diversity and diversity. |
Incorporating Causal Analysis into Diversified and Logical Response Generation (2022.coling-1)
Copied to clipboard
| Challenge: | Existing generation-based models generate generic and safe responses such as "So am I" or "I don't know" |
| Approach: | They propose to predict the mediators to preserve relevant information and auto-regressively incorporate the mediator into generating process. |
| Outcome: | The proposed model generates relevant and informative responses and outperforms the state-of-the-art in terms of automatic metrics and human evaluations. |
Better Conversations by Modeling, Filtering, and Optimizing for Coherence and Diversity (D18-1)
Copied to clipboard
| Challenge: | Existing encoder-decoder models for open domain dialogue generate generic, uninformative, and non-coherent responses. |
| Approach: | They propose to introduce a measure of coherence as the GloVe embedding similarity between dialogue context and generated response to improve output diversity. |
| Outcome: | The proposed model improves on the OpenSubtitles corpus in terms of BLEU score and diversity metrics. |
Hierarchy Response Learning for Neural Conversation Generation (D19-1)
Copied to clipboard
| Challenge: | Neural conversation generation models can't perceive and express the intention effectively, causing dull and generic responses. |
| Approach: | They propose a hierarchical response generation model to capture conversation intention . they propose an expression reconstruction model and an expression attention model . |
| Outcome: | The proposed model can generate the responses with more appropriate content and expression. |
Dior-CVAE: Pre-trained Language Models and Diffusion Priors for Variational Dialog Generation (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Existing variational dialog models have pre-trained, restricting diversity of responses . a diffusion model increases complexity of prior distribution and its compatibility with PLMs . |
| Approach: | They propose a hierarchical conditional variational autoencoder with diffusion priors to address these challenges. |
| Outcome: | The proposed method generates more diverse responses without dialog pre-training. |
Modeling Complex Dialogue Mappings via Sentence Semantic Segmentation Guided Conditional Variational Auto-Encoder (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Existing efforts to identify and avoid CDM to facilitate dialogue learning failed to solve the problem. |
| Approach: | They propose a Sentence Semantic Segmentation guided Conditional Variational Auto-Encoder which can model and take advantage of the CDM data. |
| Outcome: | The proposed method can model and take advantages of the CDM data. |