6 citations · 6 across the 4 of their papers we have counts for
4 papers
SynthDST: Synthetic Data is All You Need for Few-Shot Dialog State Tracking
Atharva Kulkarni, Bo-Hsiang Tseng, Joel Ruben Antony Moniz +3
In-context learning with Large Language Models (LLMs) has emerged as a promising avenue of research in Dialog State Tracking (DST). However, the best-performing in-context learning…
Can Large Language Models Understand Context?
Yilun Zhu, Joel Ruben Antony Moniz, Shruti Bhargava +6
Understanding context is key to understanding human language, an ability which Large Language Models (LLMs) have been increasingly seen to demonstrate to an impressive extent. Howe…
Grounding Description-Driven Dialogue State Trackers with Knowledge-Seeking Turns
Alexandru Coca, Bo-Hsiang Tseng, Jinghong Chen +4
Schema-guided dialogue state trackers can generalise to new domains without further training, yet they are sensitive to the writing style of the schemata. Augmenting the training s…
Towards Machine Comprehension of Spoken Content: Initial TOEFL Listening Comprehension Test by Machine
Bo-Hsiang Tseng, Sheng-Syun Shen, Hung-Yi Lee +1
Multimedia or spoken content presents more attractive information than plain text content, but it's more difficult to display on a screen and be selected by a user. As a result, ac…