--- title: "Yue Zhang — papers and cards" description: "Yue Zhang: 6 paper cards in the Research library (2008–2026), with what each paper is about, the ideas Mova took from it, and the co-authors." canonical: "https://research.elamur.ai/free/en/authors/yue-zhang/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/authors/yue-zhang/) [home](https://md.research.elamur.ai/free/en/) / [authors](https://md.research.elamur.ai/free/en/authors/) / Yue Zhang # Yue Zhang 6 paper cards in the Research library · 2008–2026 Yue Zhang wrote 6 of the papers Mova reads, in 2008–2026, most of them on [Machine translation](https://md.research.elamur.ai/free/en/topics/machine-translation/), [Dependency parsing](https://md.research.elamur.ai/free/en/topics/dependency-parsing/), [Universal Dependencies](https://md.research.elamur.ai/free/en/topics/universal-dependencies/); often with [Daniel Gildea](https://md.research.elamur.ai/free/en/authors/daniel-gildea/), [Linfeng Song](https://md.research.elamur.ai/free/en/authors/linfeng-song/), [Zhiguo Wang](https://md.research.elamur.ai/free/en/authors/zhiguo-wang/). [OpenAlex](https://openalex.org/A5100328212) · [ORCID](https://orcid.org/0000-0003-4012-537X) · [**h-index 19**](https://openalex.org/authors/A5100328212) · [1557 citations](https://openalex.org/authors/A5100328212) · [107 works](https://openalex.org/works?filter=author.id:A5100328212) — openalex-snapshot, 2026-10-14 The profile is the library's match of the name; two people with one name are not always told apart. Elsewhere: [Google Scholar](https://scholar.google.com/citations?view_op=search_authors&mauthors=Yue+Zhang) · [Semantic Scholar](https://www.semanticscholar.org/search?q=Yue+Zhang) · [dblp](https://dblp.org/search?q=Yue+Zhang) · [ACL Anthology](https://aclanthology.org/search/?q=Yue+Zhang) · [ORCID](https://orcid.org/orcid-search/search?searchQuery=Yue+Zhang) Topics: [Machine translation](https://md.research.elamur.ai/free/en/topics/machine-translation/) 4 · [Dependency parsing](https://md.research.elamur.ai/free/en/topics/dependency-parsing/) 2 · [Universal Dependencies](https://md.research.elamur.ai/free/en/topics/universal-dependencies/) 1 ## Papers 1. **[Modular Neural Machine Translation with a Semantic Pivot - Pilot Study Using AMR](https://md.research.elamur.ai/free/en/papers/gao-2026-modular-neural-machine-translation-semantic/)** *(2026)* The paper builds a translation pipeline with a semantic pivot, in the spirit of the translation pyramid. Each language gets two modules: one that reads a sentence into English AMR, and one that writes the target sentence from AMR. The training data is a silver AMR corpus made by a strong parser from the United… 2. **[Semantic Neural Machine Translation Using AMR](https://md.research.elamur.ai/free/en/papers/song-2019-semantic-neural-machine-translation-amr/)** *(2019)* The paper asks whether an AMR graph of the source sentence helps neural machine translation. The model keeps a BiLSTM encoder over the words and adds a graph recurrent network that encodes the AMR graph without linearising it. A doubly attentive LSTM decoder reads both encodings. On WMT16 English-German (4.17M… 3. **[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)* 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. 4. **[Universal Dependencies Parsing for Colloquial Singaporean English](https://md.research.elamur.ai/free/en/papers/ud-2017-wang-universal-dependencies-parsing-colloquial-singaporean/)** *(2017)* 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". 5. **[Transition-Based Syntactic Linearization](https://md.research.elamur.ai/free/en/papers/liu-2015-transition-based-syntactic-linearization/)** *(2015)* Syntactic linearization takes a bag of words and optional syntactic constraints and builds the sentence and its tree together. 6. **[A Tale of Two Parsers: Investigating and Combining Graph-based and Transition-based Dependency Parsing](https://md.research.elamur.ai/free/en/papers/zhang-2008-tale-two-parsers-investigating-combining/)** *(2008)* `measured` The paper compares graph-based and transition-based dependency parsing within one framework based on beam search and a discriminatively trained perceptron. ## Co-authors (17) - [Daniel Gildea](https://md.research.elamur.ai/free/en/authors/daniel-gildea/) ×2 - [Linfeng Song](https://md.research.elamur.ai/free/en/authors/linfeng-song/) ×2 - [Zhiguo Wang](https://md.research.elamur.ai/free/en/authors/zhiguo-wang/) ×2 - [Bing Qin](https://md.research.elamur.ai/free/en/authors/bing-qin/) - GuangYong Leonard Chan - Hai Leong Chieu - [Hongmin Wang](https://md.research.elamur.ai/free/en/authors/hongmin-wang/) - Jie Yang - Jinsong Su - Shulin Huang - [Stephen Clark](https://md.research.elamur.ai/free/en/authors/stephen-clark/) - [Wanxiang Che](https://md.research.elamur.ai/free/en/authors/wanxiang-che/) - Wenyang Gao - Xiaochang Peng - Yaxuan Li - [Yijia Liu](https://md.research.elamur.ai/free/en/authors/yijia-liu/) - Yunxin Bao ## More papers in the library (18) Pages of the Research library: our summary, the ideas, references and who cites each. - [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/) — Bowen Ding, Qingkai Min, Shengkun Ma, et al. · 2024 - [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/) — Qingkai Min, Qipeng Guo, Xiangkun Hu, et al. · 2024 - [A Survey on Evaluation of Large Language Models](https://md.research.elamur.ai/free/en/papers/chang-2023-survey-evaluation-large-language-models/) — Yupeng Chang, Xu Wang, Jindong Wang, et al. · 2023 - [A Survey on Large Language Model (LLM) Security and Privacy: The Good, the Bad, and the Ugly](https://md.research.elamur.ai/free/en/papers/yao-2023-survey-large-language-model-llm/) — Yifan Yao, Jinhao Duan, Kaidi Xu, et al. · 2023 - [Dual Cache for Long Document Neural Coreference Resolution](https://md.research.elamur.ai/free/en/papers/guo-2023-dual-cache-long-document-neural/) — Qipeng Guo, Xiangkun Hu, Yue Zhang, et al. · 2023 - [Evaluating the Logical Reasoning Ability of ChatGPT and GPT-4](https://md.research.elamur.ai/free/en/papers/liu-2023-evaluating-logical-reasoning-ability-chatgpt/) — Hanmeng Liu, Ruoxi Ning, Zhiyang Teng, et al. · 2023 - [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/) — Guangsheng Bao, Yanbin Zhao, Zhiyang Teng, et al. · 2023 - [PromptRobust: Towards Evaluating the Robustness of Large Language Models on Adversarial Prompts](https://md.research.elamur.ai/free/en/papers/zhu-2023-promptrobust-towards-evaluating-robustness-large/) — Kaijie Zhu, Jindong Wang, Jiaheng Zhou, et al. · 2023 - [Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language Models](https://md.research.elamur.ai/free/en/papers/zhang-2023-siren-s-song-ai-ocean/) — Yue Zhang, Yafu Li, Leyang Cui, et al. · 2023 - [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/) — Yun Luo, Zihan Liu, Yuefeng Shi, et al. · 2022 - [Graph Pre-training for AMR Parsing and Generation](https://md.research.elamur.ai/free/en/papers/bai-2022-graph-pre-training-amr-parsing/) — Xuefeng Bai, Yulong Chen, Yue Zhang · 2022 - [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/) — Yue Zhang, Bo Zhang, Zhenghua Li, et al. · 2022 - [DialogSum: A Real-Life Scenario Dialogue Summarization Dataset](https://md.research.elamur.ai/free/en/papers/chen-2021-dialogsum-real-life-scenario-dialogue/) — Yulong Chen, Yang Liu, Liang Chen, et al. · 2021 - [LogiQA: A Challenge Dataset for Machine Reading Comprehension with Logical Reasoning](https://md.research.elamur.ai/free/en/papers/liu-2020-logiqa-challenge-dataset-machine-reading/) — Jian Liu, Leyang Cui, Hanmeng Liu, et al. · 2020 - [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/) — Linfeng Song, Kun Xu, Yue Zhang, et al. · 2020 - [A Graph-to-Sequence Model for AMR-to-Text Generation](https://md.research.elamur.ai/free/en/papers/song-2018-graph-sequence-model-amr-text/) — Linfeng Song, Yue Zhang, Zhiguo Wang, et al. · 2018 - [Attention-based Recurrent Convolutional Neural Network for Automatic Essay Scoring](https://md.research.elamur.ai/free/en/papers/dong-2017-attention-based-recurrent-convolutional-neural/) — Fei Dong, Yue Zhang, Jie Yang · 2017 - [Tweet Sarcasm Detection Using Deep Neural Network](https://md.research.elamur.ai/free/en/papers/zhang-2016-tweet-sarcasm-detection-deep-neural/) — Meishan Zhang, Yue Zhang, Guohong Fu · 2016