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 …

Large Language Models (LLMs) are swiftly advancing in architecture and capability, and as
they integrate more deeply into complex systems, the urgency to scrutinize their security …

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 …

Building conversational systems in new domains and with added functionality requires
resource-efficient models that work under low-data regimes (ie, in few-shot setups) …

Data scarcity is a long-standing and crucial challenge that hinders quick development of
task-oriented dialogue systems across multiple domains: task-oriented dialogue models are …

Large datasets of paired images and text have become increasingly popular for learning
generic representations for vision and vision-and-language tasks. Such datasets have been …

General-purpose pretrained sentence encoders such as BERT are not ideal for real-world
conversational AI applications; they are computationally heavy, slow, and expensive to train …

Existing open-domain dialog models are generally trained to minimize the perplexity of
target human responses. However, some human replies are more engaging than others …

There is an increasing focus on model-based dialog evaluation metrics such as ADEM,
RUBER, and the more recent BERT-based metrics. These models aim to assign a high …

Conversational AI intends for machine-human interactions to appear and feel more natural
and inclined to communicate in a near-human context. Chatbots, also known as …