2 citations · 4 across the 14 of their papers we have counts for
8 papers · 1 filter
CausalDialogue: Modeling Utterance-level Causality in Conversations
Yi-Lin Tuan, Alon Albalak, Wenda Xu +4
Despite their widespread adoption, neural conversation models have yet to exhibit natural chat capabilities with humans. In this research, we examine user utterances as causes and…
Not All Errors are Equal: Learning Text Generation Metrics using Stratified Error Synthesis
Wenda Xu, Yilin Tuan, Yujie Lu +3
Is it possible to build a general and automatic natural language generation (NLG) evaluation metric? Existing learned metrics either perform unsatisfactorily or are restricted to t…
Flexible Attention-Based Multi-Policy Fusion for Efficient Deep Reinforcement Learning
Zih-Yun Chiu, Yi-Lin Tuan, William Yang Wang +1
Reinforcement learning (RL) agents have long sought to approach the efficiency of human learning. Humans are great observers who can learn by aggregating external knowledge from va…
Dynamic Latent Separation for Deep Learning
Yi-Lin Tuan, Zih-Yun Chiu, William Yang Wang
A core problem in machine learning is to learn expressive latent variables for model prediction on complex data that involves multiple sub-components in a flexible and interpretabl…
FETA: A Benchmark for Few-Sample Task Transfer in Open-Domain Dialogue
Alon Albalak, Yi-Lin Tuan, Pegah Jandaghi +6
Task transfer, transferring knowledge contained in related tasks, holds the promise of reducing the quantity of labeled data required to fine-tune language models. Dialogue underst…
HybriDialogue: An Information-Seeking Dialogue Dataset Grounded on Tabular and Textual Data
Kai Nakamura, Sharon Levy, Yi-Lin Tuan +2
A pressing challenge in current dialogue systems is to successfully converse with users on topics with information distributed across different modalities. Previous work in multitu…