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Yi-Lin Tuan

8 accepted papers

2023

CausalDialogue: Modeling Utterance-level Causality in Conversations

ACL 2023findings

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 generated responses as effects, recognizing that changes in a cause should produce a different effect. To further explor…

2023

Flexible Attention-Based Multi-Policy Fusion for Efficient Deep Reinforcement Learning

NeurIPS 2023poster

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 various sources, including observations from others' policies of attempting a task. Prior studies in RL have incorporated…

2022

FETA: A Benchmark for Few-Sample Task Transfer in Open-Domain Dialogue

EMNLP 2022main

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 understanding encompasses many diverse tasks, yet task transfer has not been thoroughly studied in conversational AI. This work…

2022

HybriDialogue: An Information-Seeking Dialogue Dataset Grounded on Tabular and Textual Data

ACL 2022findings

A pressing challenge in current dialogue systems is to successfully converse with users on topics with information distributed across different modalities. Previous work in multiturn dialogue systems has primarily focused on either text or table information. In more realistic scenarios, having a joi…

Cited by 26SourcePDFScholar
2022

Not All Errors are Equal: Learning Text Generation Metrics using Stratified Error Synthesis

EMNLP 2022finding

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 tasks where large human rating data is already available. We introduce SESCORE, a model-based metric that is highly corre…

2022

Towards Large-Scale Interpretable Knowledge Graph Reasoning for Dialogue Systems

ACL 2022findings

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 partly due to the lack of reasoning capabilities of dialogue platforms and the hand-crafted rules that require extensive la…

2021

Local Explanation of Dialogue Response Generation

NeurIPS 2021poster

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 this work, we study model-agnostic explanations of a representative text generation task -- dialogue response generation.…

2018

Transcribing Lyrics from Commercial Song Audio: the First Step Towards Singing Content Processing

ICASSP 2018accepted

Spoken content processing (such as retrieval and browsing) is maturing, but the singing content is still almost completely left out. Songs are human voice carrying plenty of semantic information just as speech, and may be considered as a special type of speech with highly flexible prosody. The vario…

Cited by 0SourceScholar