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 …

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 …

Federated machine unlearning (FMU) aims to remove the influence of a specified subset of
training data upon request from a trained federated learning model. Despite achieving …

As Large Language Models (LLMs) become popular, there emerged an important trend of
using multimodality to augment the LLMs' generation ability, which enables LLMs to better …

A dialogue is essentially a multi-turn interaction among interlocutors. Effective evaluation
metrics should reflect the dynamics of such interaction. Existing automatic metrics are …

Dialog response generation in open domain is an important research topic where the main
challenge is to generate relevant and diverse responses. In this paper, we propose a new …

Implicit knowledge, such as common sense, is key to fluid human conversations. Current
neural response generation (RG) models are trained to generate responses directly …

Human-like chatbots necessitate the use of commonsense reasoning in order to effectively
comprehend and respond to implicit information present within conversations. Achieving …

Smart healthcare systems that make use of abundant health data can improve access to
healthcare services, reduce medical costs and provide consistently high-quality patient care …

Countermeasures to effectively fight the ever increasing hate speech online without blocking
freedom of speech is of great social interest. Natural Language Generation (NLG), is …