Papers by Yidong Shi
wav2vec-S: Adapting Pre-trained Speech Models for Streaming (2024.findings-acl)
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| Challenge: | Pre-trained speech models have advanced speech-related tasks, including speech recognition and translation. |
| Approach: | They propose a pre-trained speech model that incorporates modifications to ensure consistent speech representations during training and inference phases for streaming speech inputs. |
| Outcome: | The proposed model outperforms baseline models on speech recognition and translation tasks and achieves a superior balance between quality and latency. |
LLMs Can Achieve High-quality Simultaneous Machine Translation as Efficiently as Offline (2025.findings-acl)
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| Challenge: | Large language models perform well in offline machine translation when the complete source sentence is provided . however, in many real scenarios, the source tokens arrive in a streaming manner and simultaneous machine translation is required . |
| Approach: | They propose a new paradigm that includes constructing supervised fine-tuning data for simultaneous machine translation (SiMT) to achieve SiMT, source and target tokens are rearranged into interleaved sequences, separated by special tokens according to varying latency requirements. |
| Outcome: | The proposed approach achieves state-of-the-art performance across various SiMT benchmarks and evaluation metrics while maintaining efficient auto-regressive decoding. |
AI Chatbots as Professional Service Agents: Developing a Professional Identity (2025.emnlp-main)
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| Challenge: | Existing studies often overlook the act of communicating consistent with professional identities of LLM-based AI chatbots. |
| Approach: | They propose a framework for designing professional service agents for medical question-and-answer services that aligns professional identities with a theory-guided task planning process. |
| Outcome: | The proposed approach outperforms baseline methods on various LLMs across key metrics such as fluency, naturalness, empathy, patient-centricity, and ROUGE-L scores. |
Towards Better Document-level Relation Extraction via Iterative Inference (2022.emnlp-main)
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| Challenge: | Existing methods only consider feature information of entity pairs, but our model exploits both feature information and previous predictions of entity pair. |
| Approach: | They propose a document-level relation extraction model with iterative inference to extract relations between entities from raw texts. |
| Outcome: | The proposed model outperforms existing methods on three commonly-used datasets. |
Always the Best Fit: Adaptive Domain Gap Filling from Causal Perspective for Few-Shot Relation Extraction (2023.findings-emnlp)
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Ge Bai, Chenji Lu, Jiaxiang Geng, Shilong Li, Yidong Shi, Xiyan Liu, Ying Liu, Zhang Zhang, Ruifang Liu
| Challenge: | Existing approaches to cross-domain relation extraction have been limited by domains . data bias between domains can be difficult to fill, especially in few-shot scenarios . |
| Approach: | They propose a framework to bridge the semantic gap caused by data bias between domains . they use syntactic structure, label distribution, and entities to calculate causal effects . |
| Outcome: | The proposed framework fills the domain gap and yields better results on the few-shot task. |
A Multi-Task Approach for Improving Biomedical Named Entity Recognition by Incorporating Multi-Granularity information (2021.findings-acl)
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| Challenge: | Neural named entity recognition (BioNER) methods require large amount of annotated data, while the annotating BioNER datasets are often difficult to obtain and small in scale due to the limitations of privacy, ethics and high degree of specialization. |
| Approach: | They propose a method that utilizes latent multi-granularity information in annotated bioNER datasets to alleviate the lack of training samples. |
| Outcome: | The proposed model improves over the BioBERT baseline and can get more than 3% improvement of F1score in low-resource scenarios. |
Representation Purification for End-to-End Speech Translation (2025.coling-main)
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| Challenge: | Existing approaches to enhance speech translation focus on enhancing knowledge transfer . factors in speech that are not relevant to translation content, such as timbre and rhythm, often limit the efficiency of knowledge transfer. |
| Approach: | They propose a framework that excludes content-agnostic perturbations from speech representations to mitigate their negative impact on ST. |
| Outcome: | The proposed framework significantly improves translation performance across all translation directions in three settings and achieves preeminent performance under a *transcript-free* setting. |
PersLEARN: Research Training through the Lens of Perspective Cultivation (2023.acl-demo)
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Yu-Zhe Shi, Shiqian Li, Xinyi Niu, Qiao Xu, Jiawen Liu, Yifan Xu, Shiyu Gu, Bingru He, Xinyang Li, Xinyu Zhao, Zijian Zhao, Yidong Lyu, Zhen Li, Sijia Liu, Lin Qiu, Jinhao Ji, Lecheng Ruan, Yuxi Ma, Wenjuan Han, Yixin Zhu
| Challenge: | PersLEARN is a tool designed to facilitate the cultivation of scientific perspectives . junior researchers struggle to identify the perspectives reflected in the literature and struggle to develop their own viewpoints. |
| Approach: | They propose a tool to facilitate the cultivation of scientific perspectives by interacting with a prompt-based model and allowing students to develop their own perspectives explicitly. |
| Outcome: | The proposed tool outperforms baseline approaches across multiple domains of literature from different perspectives. |
Entity-level Interaction via Heterogeneous Graph for Multimodal Named Entity Recognition (2022.findings-emnlp)
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| Challenge: | Existing methods for name-based entity recognition neglect the integrity of entity semantics and conduct cross-modal interaction at token-level. |
| Approach: | They propose a multimodal named entity recognition model that captures visual information and fuses it into tokens to rid non-entity tokens of visual noise. |
| Outcome: | The proposed model captures entity-related visual information and fuses it into tokens . it eliminates visual noise and makes non-entity tokens easily misidentified as entities . |
Adapting Offline Speech Translation Models for Streaming with Future-Aware Distillation and Inference (2023.emnlp-main)
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| Challenge: | Existing approaches to streaming speech translation use an offline model with a wait-k policy . however, there is a mismatch problem with an offline inference model trained with complete utterances . |
| Approach: | They propose an offline streaming speech translation model with wait-k policy to support different latency requirements. |
| Outcome: | The proposed model achieves better trade-offs between translation quality and latency than baselines. |
Adaptive Simultaneous Sign Language Translation with Confident Translation Length Estimation (2024.lrec-main)
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| Challenge: | Existing non-simultaneous sign language translation methods suffer from inherent inference delays in real-time scenarios. |
| Approach: | They propose an adaptive policy for simultaneous sign language translation that progressively converts incrementally received sign video into its corresponding natural sentence. |
| Outcome: | The proposed policy excels in situations requiring extremely low latency. |
A Document-Level Neural Machine Translation Model with Dynamic Caching Guided by Theme-Rheme Information (2020.coling-main)
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| Challenge: | Recent studies have shown that inter-sentence information is helpful for improving the performance of document-level Neural Machine Translation models, but what information should be regarded as context remains ambiguous. |
| Approach: | They propose a cache-based document-level NMT model which conducts dynamic caching guided by theme-rheme information. |
| Outcome: | The proposed model achieves substantial improvements over the state-of-the-art models on NIST evaluation sets. |
Signer Diversity-driven Data Augmentation for Signer-Independent Sign Language Translation (2024.findings-naacl)
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| Challenge: | Existing methods for sign language translation (SLT) rely on signer identity labels, which is often impractical and costly in real-world applications. |
| Approach: | They propose a signer diversity-driven data augmentation method that can generalize to signers not encountered during training. |
| Outcome: | The proposed method achieves state-of-the-art results without relying on signer identity labels. |
Translation with Thought: Difficulty-Adaptive Reasoning via Reinforcement Learning for Multi-Domain Machine Translation (2026.acl-long)
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| Challenge: | Multi-domain machine translation (MDMT) is a unique challenge due to varying levels of linguistic complexity across domains. |
| Approach: | They propose a resource-rational framework that learns to modulate inference between intuitive and deliberate reasoning. |
| Outcome: | Evaluated on 15 benchmarks spanning in-domain and out-of-domain settings, as well as 3 seen and 59 unseen languages, with ablations across three backbone models, TwT-7B and Twt-14B outperform much larger SOTA reasoning models in translation quality, while reducing token usage by 32–60%. |
Learning to Compose Representations of Different Encoder Layers towards Improving Compositional Generalization (2023.findings-emnlp)
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| Challenge: | Recent studies show that sequence-to-sequence (seq2sequ) models struggle with compositional generalization (CG) a crucial property of human language learning is its compositional globalization (GC), the algebraic ability to understand and produce a potentially infinite number of novel combinations from known components. |
| Approach: | They propose a sequence-to-sequence (seq2sequ) extension which learns to compose representations of different encoder layers dynamically for different tasks. |
| Outcome: | The proposed model achieves competitive results on two comprehensive and realistic benchmarks, which empirically demonstrates the effectiveness of the proposed model. |