7 citations · 24 across the 19 of their papers we have counts for
14 papers · 1 filter
A Gradient Analysis Framework for Rewarding Good and Penalizing Bad Examples in Language Models
Yi-Lin Tuan, William Yang Wang
Beyond maximum likelihood estimation (MLE), the standard objective of a language model (LM) that optimizes good examples probabilities, many studies have explored ways that also pe…
Towards Safety and Helpfulness Balanced Responses via Controllable Large Language Models
Yi-Lin Tuan, Xilun Chen, Eric Michael Smith +5
As large language models (LLMs) become easily accessible nowadays, the trade-off between safety and helpfulness can significantly impact user experience. A model that prioritizes s…
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…
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…
Towards Large-Scale Interpretable Knowledge Graph Reasoning for Dialogue Systems
Yi-Lin Tuan, Sajjad Beygi, Maryam Fazel-Zarandi +3
Users interacting with voice assistants today need to phrase their requests in a very specific manner to elicit an appropriate response. This limits the user experience, and is par…
D-REX: Dialogue Relation Extraction with Explanations
Alon Albalak, Varun Embar, Yi-Lin Tuan +2
Existing research studies on cross-sentence relation extraction in long-form multi-party conversations aim to improve relation extraction without considering the explainability of…
Local Explanation of Dialogue Response Generation
Yi-Lin Tuan, Connor Pryor, Wenhu Chen +2
In comparison to the interpretation of classification models, the explanation of sequence generation models is also an important problem, however it has seen little attention. In t…
Quality Estimation without Human-labeled Data
Yi-Lin Tuan, Ahmed El-Kishky, Adithya Renduchintala +3
Quality estimation aims to measure the quality of translated content without access to a reference translation. This is crucial for machine translation systems in real-world scenar…
Knowledge Injection into Dialogue Generation via Language Models
Yi-Lin Tuan, Wei Wei, William Yang Wang
Dialogue generation has been successfully learned from scratch by neural networks, but tends to produce the same general response, e.g., "what are you talking about?", in many conv…
DyKgChat: Benchmarking Dialogue Generation Grounding on Dynamic Knowledge Graphs
Yi-Lin Tuan, Yun-Nung Chen, Hung-yi Lee
Data-driven, knowledge-grounded neural conversation models are capable of generating more informative responses. However, these models have not yet demonstrated that they can zero-…
Proximal Policy Optimization and its Dynamic Version for Sequence Generation
Yi-Lin Tuan, Jinzhi Zhang, Yujia Li +1
In sequence generation task, many works use policy gradient for model optimization to tackle the intractable backpropagation issue when maximizing the non-differentiable evaluation…
Improving Conditional Sequence Generative Adversarial Networks by Stepwise Evaluation
Yi-Lin Tuan, Hung-Yi Lee
Sequence generative adversarial networks (SeqGAN) have been used to improve conditional sequence generation tasks, for example, chit-chat dialogue generation. To stabilize the trai…