Prompt Pool based Class-Incremental Continual Learning for Dialog State Tracking

1 code implementation • 17 Nov 2023 • Hong Liu, Yucheng Cai, Yuan Zhou, Zhijian Ou, Yi Huang, Junlan Feng

Inspired by the recently emerging prompt tuning method that performs well on dialog systems, we propose to use the prompt pool method, where we maintain a pool of key-value paired prompts and select prompts from the pool according to the distance between the dialog history and the prompt keys.

Continual Learning dialog state tracking

UniPCM: Universal Pre-trained Conversation Model with Task-aware Automatic Prompt

no code implementations • 20 Sep 2023 • Yucheng Cai, Wentao Ma, Yuchuan Wu, Shuzheng Si, Yuan Shao, Zhijian Ou, Yongbin Li

Using the high-quality prompts generated, we scale the corpus of the pre-trained conversation model to 122 datasets from 15 dialog-related tasks, resulting in Universal Pre-trained Conversation Model (UniPCM), a powerful foundation model for various conversational tasks and different dialog systems.

Knowledge-Retrieval Task-Oriented Dialog Systems with Semi-Supervision

1 code implementation • 22 May 2023 • Yucheng Cai, Hong Liu, Zhijian Ou, Yi Huang, Junlan Feng

Most existing task-oriented dialog (TOD) systems track dialog states in terms of slots and values and use them to query a database to get relevant knowledge to generate responses.

Question Answering Retrieval

A Generative User Simulator with GPT-based Architecture and Goal State Tracking for Reinforced Multi-Domain Dialog Systems

1 code implementation • 17 Oct 2022 • Hong Liu, Yucheng Cai, Zhijian Ou, Yi Huang, Junlan Feng

Second, an important ingredient in a US is that the user goal can be effectively incorporated and tracked; but how to flexibly integrate goal state tracking and develop an end-to-end trainable US for multi-domains has remained to be a challenge.

Reinforcement Learning (RL)

Advancing Semi-Supervised Task Oriented Dialog Systems by JSA Learning of Discrete Latent Variable Models

1 code implementation • SIGDIAL (ACL) 2022 • Yucheng Cai, Hong Liu, Zhijian Ou, Yi Huang, Junlan Feng

In this paper, we propose to apply JSA to semi-supervised learning of the latent state TOD models, which is referred to as JSA-TOD.

Learning-based Autonomous Channel Access in the Presence of Hidden Terminals

no code implementations • 7 Jul 2022 • Yulin Shao, Yucheng Cai, Taotao Wang, Ziyang Guo, Peng Liu, Jiajun Luo, Deniz Gunduz

We consider the problem of autonomous channel access (AutoCA), where a group of terminals tries to discover a communication strategy with an access point (AP) via a common wireless channel in a distributed fashion.

Collision Avoidance

Building Markovian Generative Architectures over Pretrained LM Backbones for Efficient Task-Oriented Dialog Systems

2 code implementations • 13 Apr 2022 • Hong Liu, Yucheng Cai, Zhijian Ou, Yi Huang, Junlan Feng

Recently, Transformer based pretrained language models (PLMs), such as GPT2 and T5, have been leveraged to build generative task-oriented dialog (TOD) systems.

Variational Latent-State GPT for Semi-Supervised Task-Oriented Dialog Systems

2 code implementations • 9 Sep 2021 • Hong Liu, Yucheng Cai, Zhenru Lin, Zhijian Ou, Yi Huang, Junlan Feng

In this paper, we propose Variational Latent-State GPT model (VLS-GPT), which is the first to combine the strengths of the two approaches.