Guanhuan Huang


Autoregressive Entity Generation for End-to-End Task-Oriented Dialog
Guanhuan Huang | Xiaojun Quan | Qifan Wang
Proceedings of the 29th International Conference on Computational Linguistics

Task-oriented dialog (TOD) systems are often required to interact with an external knowledge base (KB) to retrieve necessary entity (e.g., restaurants) information to support their response generation. Most current end-to-end TOD systems either retrieve the KB information explicitly or embed it into model parameters for implicit access. While the first approach demands scanning the KB at each turn of response generation, which is inefficient when the KB scales up, the second approach shows higher flexibility and efficiency. In either approach, the response shall contain attributes of the same entity, however the systems may generate a response with conflicting entities. To address this, we propose to generate the entity autoregressively before leveraging it to guide the response generation in an end-to-end system. To ensure entity consistency, we impose a trie constraint on the decoding of an entity. We also introduce a logit concatenation strategy to facilitate gradient backpropagation for end-to-end training. Experiments on MultiWOZ 2.1 single and CAMREST show that our system can generate more high-quality and entity-consistent responses in an end-to-end manner.


Bi-Granularity Contrastive Learning for Post-Training in Few-Shot Scene
Ruikun Luo | Guanhuan Huang | Xiaojun Quan
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021