Papers by Shuyan Dong

4 papers
SUPERB-SG: Enhanced Speech processing Universal PERformance Benchmark for Semantic and Generative Capabilities (2022.acl-long)

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Challenge: Existing evaluation methods for transfer learning are limited in speech research . authors show that pre-trained models transfer well across multiple tasks .
Approach: They propose a benchmark to evaluate pre-trained models by increasing task diversity and difficulty over SUPERB.
Outcome: The proposed benchmark increases task diversity and difficulty over SUPERB-SG.
Introducing Semantics into Speech Encoders (2023.acl-long)

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Challenge: Existing self-supervised speech encoders contain primarily acoustic rather than semantic information.
Approach: They propose a task-agnostic unsupervised way to incorporate semantic information from large language model (LLM) systems into self-supervised speech encoders without labeled audio transcriptions.
Outcome: The proposed approach improves spoken language understanding (SLU) performance by over 5% on intent classification (IC), with modest gains in named entity resolution (NER) and slot filling (SF), and spoken question answering (SQA) score by over 22%.
Perovskite-LLM: Knowledge-Enhanced Large Language Models for Perovskite Solar Cell Research (2025.findings-emnlp)

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Challenge: a rapid advancement of perovskite solar cells has led to an exponential growth in research publications.
Approach: They propose a knowledge-enhanced system for perovskite solar cells that integrates three key components.
Outcome: The proposed system outperforms existing models in domain-specific knowledge retrieval and scientific reasoning tasks.
Optimizing NLU Reranking Using Entity Resolution Signals in Multi-domain Dialog Systems (2021.naacl-industry)

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Challenge: In dialog systems, the Natural Language Understanding component makes the interpretation decision before the mentioned entities are resolved.
Approach: They propose to leverage Entity Resolution (ER) features in NLU reranking to learn model weights . they propose a score distribution matching method to ensure the models are calibrated .
Outcome: The proposed approach outperforms the baseline model on multiple domain evaluations.

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