--- title: "Yue Zhang — papers, page 2 · Westlake University" description: "Yue Zhang, Westlake University: 63 papers on Elamur Research (2008–2026), h-index 59 — affiliations, topics, co-authors and who cites…" canonical: "https://research.elamur.ai/free/en/authors/yue-zhang/page/2/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/authors/yue-zhang/page/2/) [home](https://md.research.elamur.ai/free/en/) / [authors](https://md.research.elamur.ai/free/en/authors/) / [Yue Zhang](https://md.research.elamur.ai/free/en/authors/yue-zhang/) / page 2 # Yue Zhang · page 2 63 papers in our library · 2008–2026 · page 2 of 3 [The profile, affiliations and co-authors →](https://md.research.elamur.ai/free/en/authors/yue-zhang/) ## Papers The most cited first (OpenAlex and our library). [← prev](https://md.research.elamur.ai/free/en/authors/yue-zhang/) [1](https://md.research.elamur.ai/free/en/authors/yue-zhang/) **2** [3](https://md.research.elamur.ai/free/en/authors/yue-zhang/page/3/) [next →](https://md.research.elamur.ai/free/en/authors/yue-zhang/page/3/) 26. **[Transition-Based Parsing of the Chinese Treebank using a Global Discriminative Model](https://md.research.elamur.ai/free/en/papers/zhang-2009-transition-based-parsing-chinese-treebank/)** *(2009 · 151 citations)* The paper targets constituent parsing of Chinese with the Chinese Treebank (CTB). The best earlier transition-based system, Wang et al. (2006), relied on a greedy shift-reduce parser with a local SVM classifier at each step. Zhang and Clark keep the same shift-reduce transitions over binarized, head-lexicalized… 27. **[Fast-DetectGPT: Efficient Zero-Shot Detection of Machine-Generated Text via Conditional Probability Curvature](https://md.research.elamur.ai/free/en/papers/bao-2023-fast-detectgpt-efficient-zero-shot/)** *(2023 · 148 citations)* Telling machine-written passages apart from human ones without training a dedicated classifier is useful, but the strongest zero-shot method, DetectGPT, is expensive. 28. **[Is ChatGPT a Highly Fluent Grammatical Error Correction System? A Comprehensive Evaluation](https://md.research.elamur.ai/free/en/papers/fang-2023-chatgpt-highly-fluent-grammatical-error/)** *(2023 · 145 citations)* The authors ask how well ChatGPT (gpt-3.5-turbo, accessed through the official API) performs grammatical error correction (GEC). 29. **[What Have We Achieved on Text Summarization?](https://md.research.elamur.ai/free/en/papers/huang-2020-what-have-we-achieved-text/)** *(2020 · 143 citations)* ROUGE gains on benchmarks such as CNN/DM say little about which errors summarizers actually make. This paper addresses that gap with a detailed manual error analysis. Drawing on the Multidimensional Quality Metric (MQM) framework, the authors define PolyTope, a set of 8 error categories split between Accuracy and… 30. **[MAGE: Machine-generated Text Detection in the Wild](https://md.research.elamur.ai/free/en/papers/li-2024-mage-machine-generated-text-detection/)** *(2024 · 129 citations)* The paper asks whether AI-written text can still be detected in realistic conditions, where a detector does not know which domain a text comes from or which model produced it. 31. **[RepoCoder: Repository-Level Code Completion Through Iterative Retrieval and Generation](https://md.research.elamur.ai/free/en/papers/zhang-2023-repocoder-repository-level-code-completion/)** *(2023 · 119 citations)* Most code completion tools see only the current file. Useful hints such as shared helpers, API signatures and project-specific naming are spread across the rest of the repository. RepoCoder uses this wider context without fine-tuning, static analysis or hand-written rules. It cuts repository files into overlapping… 32. **[G-Transformer for Document-Level Machine Translation](https://md.research.elamur.ai/free/en/papers/bao-2021-g-transformer-document-level-machine/)** *(2021 · 109 citations)* Bao et al. study why a standard Transformer often fails when an entire document is translated as one sequence. Under plain supervised training, long inputs, small datasets or large models can make BLEU collapse. The authors show this collapse is not overfitting. Instead, training gets trapped in local minima where… 33. **[AMR-to-text Generation with Synchronous Node Replacement Grammar](https://md.research.elamur.ai/free/en/papers/song-2017-amr-text-generation-synchronous-node/)** *(2017 · 109 citations)* The paper generates sentences directly from the AMR graph, without first making a spanning tree, so that no information is lost in the graph-to-tree step. 34. **[In-Order Transition-based Constituent Parsing](https://md.research.elamur.ai/free/en/papers/liu-2017-order-transition-based-constituent-parsing/)** *(2017 · 99 citations)* Transition-based constituent parsers usually build trees in one of two orders. Bottom-up parsers (post-order traversal) can use features from subtrees they have already built, but they have no lookahead about structure still to come. Top-down parsers (pre-order traversal) give global guidance, but they must commit to… 35. **[SynGEC: Syntax-Enhanced Grammatical Error Correction with a Tailored GEC-Oriented Parser](https://md.research.elamur.ai/free/en/papers/zhang-2022-syngec-syntax-enhanced-grammatical-error/)** *(2022 · 76 citations)* Grammatical error correction (GEC) systems usually treat input as a flat token sequence, even though many errors, such as tense or number agreement, depend on syntactic relations. 36. **[Learning to Reason under Off-Policy Guidance](https://md.research.elamur.ai/free/en/papers/yan-2025-learning-reason-under-off-policy/)** *(2025 · 68 citations)* Reinforcement learning with verifiable rewards (RLVR) has driven recent progress in large reasoning models. 37. **[CycleResearcher: Improving Automated Research via Automated Review](https://md.research.elamur.ai/free/en/papers/weng-2024-cycleresearcher-improving-automated-research/)** *(2024 · 65 citations)* The paper asks whether open-source LLMs, after post-training, can carry out the whole research loop: reading literature, forming ideas, designing experiments, writing, getting reviewed, and revising. 38. **[Transition-Based Syntactic Linearization](https://md.research.elamur.ai/free/en/papers/liu-2015-transition-based-syntactic-linearization/)** *(2015 · 52 citations)* Syntactic linearization takes a bag of words and optional syntactic constraints and builds the sentence and its tree together. 39. **[Universal Dependencies Parsing for Colloquial Singaporean English](https://md.research.elamur.ai/free/en/papers/wang-2017-universal-dependencies-parsing-colloquial-singaporean/)** *(2017 · 49 citations)* The authors ask why an English parser fails on Singlish, an English-based creole with imported Hokkien, Malay and Cantonese words, topic-first sentences, copula deletion and clause-final particles such as "lah". 40. **[Discriminative Syntax-Based Word Ordering for Text Generation](https://md.research.elamur.ai/free/en/papers/zhang-2015-discriminative-syntax-based-word-ordering/)** *(2015 · 48 citations)* The paper addresses word ordering for text generation: turning an unordered bag of words (or lemmas) into a fluent, grammatical sentence. 41. **[Exploiting Sentiment and Common Sense for Zero-shot Stance Detection](https://md.research.elamur.ai/free/en/papers/luo-2022-exploiting-sentiment-common-sense-zero/)** *(2022 · 30 citations)* Zero-shot stance detection asks a model to decide whether a text supports, opposes, or is neutral toward a topic it never saw in training. 42. **[NovelQA: Benchmarking Question Answering on Documents Exceeding 200K Tokens](https://md.research.elamur.ai/free/en/papers/wang-2024-novelqa-benchmarking-question-answering-documents/)** *(2024 · 29 citations)* Current LLMs accept context windows of 200K+ tokens, but long-context benchmarks typically average around 60K tokens. 43. **[ZPR2: Joint Zero Pronoun Recovery and Resolution using Multi-Task Learning and BERT](https://md.research.elamur.ai/free/en/papers/song-2020-zpr2-joint-zero-pronoun-recovery/)** *(2020 · 29 citations)* Chinese often omits pronouns, especially in conversation, which hurts machine translation, QA and dialogue systems. 44. **[How Likely Do LLMs with CoT Mimic Human Reasoning?](https://md.research.elamur.ai/free/en/papers/bao-2025-how-likely-do-llms-cot/)** *(2025 · 22 citations)* Chain-of-thought prompting does not reliably improve accuracy, and the reasoning it produces may not reflect what drives a model's answer. 45. **[Personality Alignment of Large Language Models](https://md.research.elamur.ai/free/en/papers/zhu-2024-personality-alignment-large-language-models/)** *(2024 · 21 citations)* Standard LLM alignment targets broad, shared human values and ignores how individual users differ. The authors call this gap "Personality Alignment": adapting a model's answers and choices to one person or a small, closely related group. To make the task measurable, they build PAPI, a psychometrics-based dataset that… 46. **[Synergetic Event Understanding: A Collaborative Approach to Cross-Document Event Coreference Resolution with Large Language Models](https://md.research.elamur.ai/free/en/papers/min-2024-synergetic-event-understanding-collaborative-approach/)** *(2024 · 19 citations)* The paper addresses cross-document event coreference resolution (CDECR), the task of grouping event mentions from different documents that describe the same real-world event. 47. **[A Rationale-centric Counterfactual Data Augmentation Method for Cross-Document Event Coreference Resolution](https://md.research.elamur.ai/free/en/papers/ding-2024-rationale-centric-counterfactual-data-augmentation/)** *(2024 · 17 citations)* Cross-document event coreference resolution (ECR) groups event mentions from different documents that describe the same real-world event. 48. **[Constituency Parsing using LLMs](https://md.research.elamur.ai/free/en/papers/bai-2023-constituency-parsing-using-llms/)** *(2023 · 14 citations)* The paper asks whether modern large language models can produce phrase-structure trees, a task where supervised parsers reach about 96% while unsupervised methods remain below 65%. 49. **[Assessing the Prosody of Non-Native Speakers of English: Measures and Feature Sets](https://md.research.elamur.ai/free/en/papers/coutinho-2016-assessing-prosody-non-native-speakers/)** *(2016 · 14 citations)* The paper addresses automatic rating of how natural the prosody (melody and rhythm) of non-native English speech sounds. 50. **[Lost in Literalism: How Supervised Training Shapes Translationese in LLMs](https://md.research.elamur.ai/free/en/papers/li-2025-lost-literalism-how-supervised-training/)** *(2025 · 12 citations)* LLMs learn from huge amounts of native text, yet their translations often still sound overly literal. The authors examine this "translationese" in LLM-based machine translation and trace it to supervised fine-tuning (SFT). Professional translators marked literal or unnatural spans in English-Chinese and… [← prev](https://md.research.elamur.ai/free/en/authors/yue-zhang/) [1](https://md.research.elamur.ai/free/en/authors/yue-zhang/) **2** [3](https://md.research.elamur.ai/free/en/authors/yue-zhang/page/3/) [next →](https://md.research.elamur.ai/free/en/authors/yue-zhang/page/3/)