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 …

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

Automatic evaluation metrics are essential for the rapid development of open-domain
dialogue systems as they facilitate hyper-parameter tuning and comparison between …

User Satisfaction Estimation (USE) is an important yet challenging task in goal-oriented
conversational systems. Whether the user is satisfied with the system largely depends on the …

Building an intelligent dialogue system capable of naturally and coherently conversing with
humans has been a long-standing goal of artificial intelligence. In the past decade, with the …

Evaluating the quality of generated text is a challenging task in NLP, due to the inherent
complexity and diversity of text. Recently, large language models (LLMs) have garnered …

In this paper, we introduce a novel BERT-enhanced Relational Sentence Ordering Network
(referred to as BRSON) by leveraging BERT for capturing better dependency relationship …

Neural network based deep learning methods aim to learn representations of data and have
produced state-of-the-art results in many natural language processing (NLP) tasks …

Recent advancements in reference-free learned metrics for open-domain dialogue
evaluation have been driven by the progress in pre-trained language models and the …

Automatic dialogue coherence evaluation has attracted increasing attention and is crucial
for developing promising dialogue systems. However, existing metrics have two major …