activity
20182022
most citedSPACE-2: Tree-Structured Semi-Supervised Contrastive Pre-training for Task-Oriented Dialog Understanding

14 citations · 26 across the 5 of their papers we have counts for

collaborators

6 papers

cs.CL2022

CGoDial: A Large-Scale Benchmark for Chinese Goal-oriented Dialog Evaluation

Yinpei Dai, Wanwei He, Bowen Li +5

Practical dialog systems need to deal with various knowledge sources, noisy user expressions, and the shortage of annotated data. To better solve the above problems, we propose CGo…

cs.CL20226 cited

SPACE-3: Unified Dialog Model Pre-training for Task-Oriented Dialog Understanding and Generation

Wanwei He, Yinpei Dai, Min Yang +4

Recently, pre-training methods have shown remarkable success in task-oriented dialog (TOD) systems. However, most existing pre-trained models for TOD focus on either dialog underst…

cs.CL202214 cited

SPACE-2: Tree-Structured Semi-Supervised Contrastive Pre-training for Task-Oriented Dialog Understanding

Wanwei He, Yinpei Dai, Binyuan Hui +6

Pre-training methods with contrastive learning objectives have shown remarkable success in dialog understanding tasks. However, current contrastive learning solely considers the se…

cs.CL20212 cited

Transferable Dialogue Systems and User Simulators

Bo-Hsiang Tseng, Yinpei Dai, Florian Kreyssig +1

One of the difficulties in training dialogue systems is the lack of training data. We explore the possibility of creating dialogue data through the interaction between a dialogue s…

cs.CL20214 cited

Preview, Attend and Review: Schema-Aware Curriculum Learning for Multi-Domain Dialog State Tracking

Yinpei Dai, Hangyu Li, Yongbin Li +4

Existing dialog state tracking (DST) models are trained with dialog data in a random order, neglecting rich structural information in a dataset. In this paper, we propose to use cu…

cs.CL2018

Deep learning for language understanding of mental health concepts derived from Cognitive Behavioural Therapy

Lina Rojas-Barahona, Bo-Hsiang Tseng, Yinpei Dai +5

In recent years, we have seen deep learning and distributed representations of words and sentences make impact on a number of natural language processing tasks, such as similarity,…