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 …

Alongside huge volumes of research on deep learning models in NLP in the recent years,
there has been much work on benchmark datasets needed to track modeling progress …

Recent advancements in artificial intelligence have led to the development of chatbots
capable of engaging in argumentative dialogues and debates with human users. Although …

Research suggests that English as a foreign language (EFL) students' argumentative writing
benefits from interactions with peers. However, finding an ideal partner to interact with can …

We introduce Korean Language Understanding Evaluation (KLUE) benchmark. KLUE is a
collection of 8 Korean natural language understanding (NLU) tasks, including Topic …

Task-oriented dialogue is often decomposed into three tasks: understanding user input,
deciding actions, and generating a response. While such decomposition might suggest a …

Conversational information seeking (CIS) is concerned with a sequence of interactions
between one or more users and an information system. Interactions in CIS are primarily …

Semi-structured tables are ubiquitous. There has been a variety of tasks that aim to
automatically interpret, augment, and query tables. Current methods often require …

Even though machine learning has become the major scene in dialogue research
community, the real breakthrough has been blocked by the scale of data available. To …

Pre-trained language models have been recently shown to benefit task-oriented dialogue
(TOD) systems. Despite their success, existing methods often formulate this task as a …