We consider context-response matching with multiple types of representations for multi-turn
response selection in retrieval-based chatbots. The representations encode semantics of …

Conversational AI is of growing importance since it enables easy interaction interface
between humans and computers. Due to its promising potential and alluring commercial …

End-to-end sequence generation is a popular technique for develo** open domain
dialogue systems, though they suffer from the safe response problem. Researchers have …

Human–computer conversation has long been an interest of artificial intelligence and
natural language processing research. Recent years have seen a dramatic improvement in …

Current state-of-the-art neural dialogue models learn from human conversations following
the data-driven paradigm. As such, a reliable training corpus is the crux of building a robust …

User engagement is a critical metric for evaluating the quality of open-domain dialogue
systems. Prior work has focused on conversation-level engagement by using heuristically …

In multi-turn dialogue, utterances do not always take the full form of sentences. These
incomplete utterances will greatly reduce the performance of open-domain dialogue …

Due to its potential applications, open-domain dialogue generation has become popular
and achieved remarkable progress in recent years, but sometimes suffers from generic …

Finding a concise and interpretable mathematical formula that accurately describes the
relationship between each variable and the predicted value in the data is a crucial task in …

As it is cumbersome and expensive to acquire a huge amount of data for training neural
dialog models, data augmentation is proposed to effectively utilize existing training samples …