Text Generation aims to produce plausible and readable text in human language from input
data. The resurgence of deep learning has greatly advanced this field, in particular, with the …

This is a report on the NSF Future Directions Workshop on Automatic Evaluation of Dialog.
The workshop explored the current state of the art along with its limitations and suggested …

With direct access to human-written reference as memory, retrieval-augmented generation
has achieved much progress in a wide range of text generation tasks. Since better memory …

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 …

Automatic evaluation metrics are a crucial component of dialog systems research. Standard
language evaluation metrics are known to be ineffective for evaluating dialog. As such …

Abstract Conversational Information Seeking (CIS) is a relatively new research area within
conversational AI that attempts to seek information from end-users in order to understand …

Despite recent progress of pre-trained language models on generating fluent text, existing
methods still suffer from incoherence problems in long-form text generation tasks that …

Topic models have been thoroughly investigated for multiple years due to their great
potential in analyzing and understanding texts. Recently, researchers combine the study of …

Evaluating open-domain dialogue systems is challenging for reasons such as the one-to-
many problem, ie, many appropriate responses other than just the golden response. As of …

Emotion Support Conversation (ESC) is an emerging and challenging task with the goal of
reducing the emotional distress of people. Previous attempts fail to maintain smooth …