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 …

There is a resurgent interest in developing intelligent open-domain dialog systems due to
the availability of large amounts of conversational data and the recent progress on neural …

The latest work on language representations carefully integrates contextualized features into
language model training, which enables a series of success especially in various machine …

The use of deep pre-trained bidirectional transformers has led to remarkable progress in a
number of applications (Devlin et al., 2018). For tasks that make pairwise comparisons …

Abstract Machine reading comprehension (MRC) is an AI challenge that requires machines
to determine the correct answers to questions based on a given passage. MRC systems …

For machine reading comprehension, the capacity of effectively modeling the linguistic
knowledge from the detail-riddled and lengthy passages and getting ride of the noises is …

In this paper, we study the problem of employing pre-trained language models for multi-turn
response selection in retrieval-based chatbots. A new model, named Speaker-Aware BERT …

Head-driven phrase structure grammar (HPSG) enjoys a uniform formalism representing rich
contextual syntactic and even semantic meanings. This paper makes the first attempt to …

Non-task oriented dialogue systems have achieved great success in recent years due to
largely accessible conversation data and the development of deep learning techniques …

Multi-turn retrieval-based conversation is an important task for building intelligent dialogue
systems. Existing works mainly focus on matching candidate responses with every context …