Papers by Dongyu Wang

10 papers
Rhetorical Device-Aware Sarcasm Detection with Counterfactual Data Augmentation (2025.findings-acl)

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Challenge: Sarcasm is a complex form of sentiment expression widely used in human daily life.
Approach: They propose a device-aware sarcasm dataset with counterfactually augmented data to capture its complexity.
Outcome: The proposed dataset shows that it is more balanced than zero-shot models.
VET: Verifiable Execution Tracing for Reliable Text-to-SQL Generation (2026.findings-acl)

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Challenge: Existing methods for text-to-SQL generation are prone to hallucinations and grounding . authors present a novel reasoning paradigm that transforms text- to-Sql from unverifiable textual rationales into step-wise executable semantics.
Approach: They propose a reasoning paradigm that transforms text-to-SQL from unverifiable textual rationales into step-wise executable semantics.
Outcome: The proposed reasoning paradigm transforms text-to-SQL from unverifiable textual rationales into step-wise executable semantics.
StoryAnalogy: Deriving Story-level Analogies from Large Language Models to Unlock Analogical Understanding (2023.emnlp-main)

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Challenge: Analogy-making between narratives is crucial for human reasoning . despite its importance, there has been limited research on story analogies .
Approach: They construct a large-scale story-level analogy corpus with 24K story pairs . they find that the tasks are incredibly difficult for large language models such as ChatGPT .
Outcome: The proposed corpus contains 24K story pairs from diverse domains with human annotations on two similarities from the extended Structure-Mapping Theory.
Active Sentence Learning by Adversarial Uncertainty Sampling in Discrete Space (2020.findings-emnlp)

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Challenge: Existing uncertainty sampling methods are time-consuming and can't be executed frequently.
Approach: They propose adversarial uncertainty sampling in discrete space to find informative unlabeled text samples for annotation using adversarials.
Outcome: The proposed approach outperforms baselines on effectiveness on five datasets.
A Unified Generative Framework for Bilingual Euphemism Detection and Identification (2024.findings-acl)

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Challenge: Existing euphemism datasets are only domain-specific or language-specific.
Approach: They propose a unified model to jointly conduct bilingual euphemism detection and identification tasks.
Outcome: The proposed model is effective and provides a new reference standard for euphemism detection and identification.
OpenResearcher: Unleashing AI for Accelerated Scientific Research (2024.emnlp-demo)

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Challenge: Global scientific publications are growing annually by about 4%-5% (Pinedo et al., 2024).
Approach: They introduce an AI-assisted platform that answers diverse questions from researchers using Retrieval-Augmented Generation (RAG) they develop various tools to understand queries, search from the scientific literature, filter retrieved information, provide accurate and comprehensive answers, and self-refine answers.
Outcome: OpenResearcher is built on Retrieval-Augmented Generation (RAG) to integrate Large Language Models (LLMs) with up-to-date, domain-specific knowledge.
TCSinger 2: Customizable Multilingual Zero-shot Singing Voice Synthesis (2025.findings-acl)

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Challenge: Existing zero-shot singing voice synthesis models depend on phoneme and note boundary annotations, limiting their robustness and producing poor transitions between phonemes and notes.
Approach: They propose a multi-task multilingual zero-shot SVS model with style transfer and style control based on various prompts.
Outcome: Experimental results show that TCSinger 2 outperforms baseline models in subjective and objective metrics across multiple related tasks.
Correct after Answer: Enhancing Multi-Span Question Answering with Post-Processing Method (2024.findings-emnlp)

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Challenge: Prior work focuses on designing specific methods or applying heuristic strategies to encourage models to predict more correct predictions.
Approach: They propose a framework that uses a post-processing strategy to handle incorrect predictions.
Outcome: The proposed framework significantly improves the Exact Match scores on multiple MSQA datasets.
Bold Claims or Self-Doubt? Factuality Hallucination Type Detection via Belief State (2025.findings-emnlp)

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Challenge: Existing studies focus on detecting the presence of hallucinations but lack a systematic classification approach, which hinders deeper exploration of their characteristics.
Approach: They propose a method to categorize hallucinations into two types: Overconfident and Unaware .
Outcome: The proposed method categorizes factuality hallucination into two types: Overconfident and Unaware Hallucinations.
WECA: A WordNet-Encoded Collocation-Attention Network for Homographic Pun Recognition (D18-1)

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Challenge: Homographic puns have a long history in human writing, widely used in written and spoken literature, which intended as jokes.
Approach: They propose a WordNet-encoded model to settle polysemy of homographic puns and a word weighted model for recognizing them.
Outcome: The proposed model can distinguish between homographic pun and non-homographic pun texts.

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