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 …

Text embeddings are commonly evaluated on a small set of datasets from a single task not
covering their possible applications to other tasks. It is unclear whether state-of-the-art …

Recently, prompt tuning has been widely applied to stimulate the rich knowledge in pre-
trained language models (PLMs) to serve NLP tasks. Although prompt tuning has achieved …

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 …

The success of large pretrained language models (LMs) such as BERT and RoBERTa has
sparked interest in probing their representations, in order to unveil what types of knowledge …

Instruction tuning is an emergent paradigm in NLP wherein natural language instructions
are leveraged with language models to induce zero-shot performance on unseen tasks …

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 …

We present a new method, Soloist, that uses transfer learning and machine teaching to build
task bots at scale. We parameterize classical modular task-oriented dialog systems using a …

Abstract The Out-of-Domain (OOD) intent classification is a basic and challenging task for
dialogue systems. Previous methods commonly restrict the region (in feature space) of In …

When applied to processing long text, Large Language Models (LLMs) are limited by their
context window. Existing efforts to address this limitation involve training specialized …