Papers by Yuting Zhao
Align-then-Enhance: Multilingual Entailment Graph Enhancement with Soft Predicate Alignment (2023.findings-acl)
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| Challenge: | Existing approaches to learn typed entailment graphs with predicates as nodes and enttailment relations as edges are incomplete. |
| Approach: | They propose a task to utilize entailment information from one EG to enhance another in a different language. |
| Outcome: | The proposed framework outperforms existing graphs in multilingual entailment graph enhancement tasks. |
Parallelism and Generation Order in Masked Diffusion Language Models: Limits Today, Potential Tomorrow (2026.findings-acl)
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Yangyang Zhong, Yanmei Gu, Zhengqing Zang, Xiaomeng Li, Yuqi Ding, Xibei Jia, Yuting Shen, Zhenzhong Lan, Liwang Zhu, Weiping Liu, Junlin Zhou, Haisheng Liu, Zhong Xin Yu, Pengxin Luo, Donglian Qi, Yunfeng Yan, Junbo Zhao
| Challenge: | Autoregressive (AR) language models dominate modern natural language processing due to strong likelihood-based training objectives and reliable left-to-right decoding. |
| Approach: | They characterize MDLM behavior along two dimensions: parallelism strength and generation order . authors propose a Generate-then-Edit paradigm that mitigates dependency loss . |
| Outcome: | The proposed model improves on tasks that require "backward information" the Generate-then-Edit paradigm improves parallel decoding efficiency while reducing dependency loss. |
Find-the-Common: A Benchmark for Explaining Visual Patterns from Images (2024.lrec-main)
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| Challenge: | Recent advances in Instruction-fine-tuned Vision and Language Models (IVLMs) have prompted some studies to analyze the reasoning capabilities of IVLMs. |
| Approach: | They introduce a vision and language task for Inductive Visual Reasoning that uses common attributes across visual scenes to find common answers. |
| Outcome: | The proposed model can archive with 48% accuracy on the FTC, compared with state-of-the-art models. |
Everything Has a Cause: Leveraging Causal Inference in Legal Text Analysis (2021.naacl-main)
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| Challenge: | Existing studies focus on analyzing structured data, while mining causal relationship among factors from unstructured data is of great importance. |
| Approach: | They propose a graph-based causal inference framework which builds causal graphs from fact descriptions without much human involvement. |
| Outcome: | The proposed framework can capture nuance from fact descriptions among confusing charges and provide explainable discrimination in few-shot settings. |
PharmMT: A Neural Machine Translation Approach to Simplify Prescription Directions (2020.findings-emnlp)
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| Challenge: | a novel machine translation-based approach to simplify prescription directions is proposed . the language used by physicians and health professionals includes medical jargon and implicit directives . |
| Approach: | They propose a machine translation-based approach to automatically and reliably simplify prescription directions into patient-friendly language. |
| Outcome: | The proposed system achieves a BLEU score of 60.27 over 530K prescriptions from a large mail-order pharmacy. |
Neighborhood Matching Network for Entity Alignment (2020.acl-main)
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| Challenge: | Structural heterogeneity between knowledge graphs is an outstanding challenge for entity alignment. |
| Approach: | They propose a framework for entity alignment that uses a neighborhood matching module to combine neighborhood differences. |
| Outcome: | The proposed framework outperforms existing methods on three datasets. |
Multimodal Robustness for Neural Machine Translation (2022.emnlp-main)
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| Challenge: | Existing approaches to deal with noisy multimodal inputs are not robust enough to deal effectively with noisy data. |
| Approach: | They propose a method that composes domain adapters to deal with noisy inputs . they combine these adapters at runtime via dynamic routing or when source of noise is unknown . |
| Outcome: | The proposed model is flexible and state-of-the-art to deal with noisy multimodal inputs. |
H-MAS: Hierarchical Multi-Agent Scheduling for Multi-Tenant LLM Serving (2026.findings-acl)
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Yuhan Liu, Cong Xu, Qi Jia, Yihua Wang, Feiyu Chen, Liang Jin, Lu Liu, Yaqian Zhao, Yuting Ding, Xiang Li
| Challenge: | Multi-tenant Model-as-a-Service (MaaS) workloads exhibit non-stationarity across multiple time scales . existing request schedulers often rely on a fixed policy that remains unchanged at runtime . |
| Approach: | They propose a hierarchical multi-agent scheduler that operates in a layered closed loop . they propose to maintain 1.2–3.0 higher Goodput than SGLang and vLLM . |
| Outcome: | Experiments show that H-MAS achieves 1.2–3.0 higher Goodput than SGLang and vLLM . it maintains more stable QoS under diverse request lengths and heterogeneous SLO targets . |
Jointly Learning Entity and Relation Representations for Entity Alignment (D19-1)
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| Challenge: | Entity alignment is a viable method for integrating heterogeneous knowledge among different knowledge graphs (KGs). |
| Approach: | They propose a Graph Convolutional Network-based framework for learning relation representations by embedding relation seeds into entities and incorporating relation approximation into entities to iteratively improve alignment. |
| Outcome: | The proposed approach outperforms state-of-the-art methods on three real-world cross-lingual datasets. |
In Search of the Long-Tail: Systematic Generation of Long-Tail Inferential Knowledge via Logical Rule Guided Search (2024.emnlp-main)
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Huihan Li, Yuting Ning, Zeyi Liao, Siyuan Wang, Xiang Li, Ximing Lu, Wenting Zhao, Faeze Brahman, Yejin Choi, Xiang Ren
| Challenge: | Logic-Induced-Knowledge-Search (LINK) is a framework for generating factually-correct yet long-tail inferential knowledge. |
| Approach: | They introduce a framework to obtain factually-correct yet long-tail inferential statements using variable-wise prompting grounded on symbolic rules. |
| Outcome: | The proposed framework is able to obtain factually-correct yet long-tail inferential statements while ensuring factual correctness. |