Dialogue systems are a popular natural language processing (NLP) task as it is promising in
real-life applications. It is also a complicated task since many NLP tasks deserving study are …

Dialogue policy learning (DPL) is a key component in a task-oriented dialogue (TOD)
system. Its goal is to decide the next action of the dialogue system, given the dialogue state …

A conversational information retrieval (CIR) system is an information retrieval (IR) system
with a conversational interface, which allows users to interact with the system to seek …

In this paper, we propose to formulate the task-oriented dialogue system as the purely
natural language generation task, so as to fully leverage the large-scale pre-trained models …

Aligning large language models (LLMs) with human preferences is crucial for enhancing
their utility in terms of helpfulness, truthfulness, safety, harmlessness, and interestingness …

Attracted by their easy-to-use interfaces and captivating benefits, conversational systems
have been widely embraced by many individuals and organizations as side-by-side digital …

Event extraction is a ftask for natural language processing. Finding the roles of event
arguments like event participants is essential for event extraction. However, doing so for real …

Many studies have applied reinforcement learning to train a dialog policy and show great
promise these years. One common approach is to employ a user simulator to obtain a large …

This article presents a hybrid approach based on a Grounded Text Generation (GTG) model
to building robust task bots at scale. GTG is a hybrid model which uses a large-scale …

Dialog management (DM) is a crucial component in a task-oriented dialog system. Given the
dialog history, DM predicts the dialog state and decides the next action that the dialog agent …