Pre-trained Language Models (PLMs) which are trained on large text corpus via self-
supervised learning method, have yielded promising performance on various tasks in …

The goal of text-to-text generation is to make machines express like a human in many
applications such as conversation, summarization, and translation. It is one of the most …

Despite showing increasingly human-like conversational abilities, state-of-the-art dialogue
models often suffer from factual incorrectness and hallucination of knowledge (Roller et al …

We study knowledge-grounded dialogue generation with pre-trained language models. To
leverage the redundant external knowledge under capacity constraint, we propose …

To build a high-quality open-domain chatbot, we introduce the effective training process of
PLATO-2 via curriculum learning. There are two stages involved in the learning process. In …

Knowledge-grounded dialogue systems are intended to convey information that is based on
evidence provided in a given source text. We discuss the challenges of training a generative …

Multi-task learning (MTL) has become increasingly popular in natural language processing
(NLP) because it improves the performance of related tasks by exploiting their …

While neural conversation models have shown great potentials towards generating
informative and engaging responses via introducing external knowledge, learning such a …

Many text generation systems benefit from using a retriever to retrieve passages from a
textual knowledge corpus (eg, Wikipedia) which are then provided as additional context to …

Structured belief states are crucial for user goal tracking and database query in task-oriented
dialog systems. However, training belief trackers often requires expensive turn-level …