[Mova](https://md.mova.elamur.ai/free/en/) · [this page in HTML](https://mova.elamur.ai/free/en/docs/papers/authors/ngoc-thang-vu/) [home](https://md.mova.elamur.ai/free/en/) / [docs](https://md.mova.elamur.ai/free/en/docs/) / [papers](https://md.mova.elamur.ai/free/en/docs/papers/) / [authors](https://md.mova.elamur.ai/free/en/docs/papers/authors/) / Ngoc Thang Vu # Ngoc Thang Vu 3 paper cards in Mova's library · 2018–2020 Ngoc Thang Vu wrote 3 of the papers Mova reads, in 2018–2020, most of them on [Dependency parsing](https://md.mova.elamur.ai/free/en/docs/papers/topics/dependency-parsing/), [Machine translation](https://md.mova.elamur.ai/free/en/docs/papers/topics/machine-translation/), [Treebanks](https://md.mova.elamur.ai/free/en/docs/papers/topics/treebanks/); often with [Jonas Kuhn](https://md.mova.elamur.ai/free/en/docs/papers/authors/jonas-kuhn/), [Xiang Yu](https://md.mova.elamur.ai/free/en/docs/papers/authors/xiang-yu/), [Agnieszka Falenska](https://md.mova.elamur.ai/free/en/docs/papers/authors/agnieszka-falenska/). [OpenAlex](https://openalex.org/A5020700841) · [ORCID](https://orcid.org/0000-0001-7893-9147) · [**h-index 34**](https://openalex.org/authors/A5020700841) · [3888 citations](https://openalex.org/authors/A5020700841) · [186 works](https://openalex.org/works?filter=author.id:A5020700841) — openalex, 2026-10-18 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=Ngoc+Thang+Vu) · [Semantic Scholar](https://www.semanticscholar.org/search?q=Ngoc+Thang+Vu) · [dblp](https://dblp.org/search?q=Ngoc+Thang+Vu) · [ACL Anthology](https://aclanthology.org/search/?q=Ngoc+Thang+Vu) · [ORCID](https://orcid.org/orcid-search/search?searchQuery=Ngoc+Thang+Vu) Topics: [Dependency parsing](https://md.mova.elamur.ai/free/en/docs/papers/topics/dependency-parsing/) 2 · [Machine translation](https://md.mova.elamur.ai/free/en/docs/papers/topics/machine-translation/) 2 · [Treebanks](https://md.mova.elamur.ai/free/en/docs/papers/topics/treebanks/) 1 ## Ideas Mova took (1) From the ideas ledger: an idea tried, kept or queued, or measured with no gain, and what it gave. - **measured, no gain** — en/src/parse.rs: add a dynamic oracle for arc-standard that charges each action by the number of gold arcs it makes unreachable, and train with it; measure cv LAS on the train folds against the static oracle, two runs each. *([Approximate Dynamic Oracle for Dependency Parsing with…](https://md.mova.elamur.ai/free/en/docs/papers/ud-2018-yu-approximate-dynamic-oracle-dependency-parsing/) · approximate arc-standard dynamic oracle with exploration, greedy parser, EWT dev our tags: 77.67 -> 76.40 (variants 76.04-76.49; 20 epochs 77.50 -> 76.47)…)* ## Papers 1. **[Fast and Accurate Non-Projective Dependency Tree Linearization](https://md.mova.elamur.ai/free/en/docs/papers/yu-2020-fast-accurate-non-projective-dependency/)** *(2020)* The paper treats linearization as a travelling-salesman problem. A biaffine model gives the cost of putting one token before another, and the costs are solved per subtree with a greedy TSP solver. The subtree orders are then combined into a projective tree. A transition system, borrowed from non-projective parsing… 2. **[Head-First Linearization with Tree-Structured Representation](https://md.mova.elamur.ai/free/en/docs/papers/yu-2019-head-first-linearization-tree-structured/)** *(2019)* The paper linearises a dependency tree, that is, it finds the word order of an unordered tree. It has two parts. The encoder is a bidirectional Tree-LSTM that passes information first up the tree and then down, so that each token sees the whole tree. The decoder is head-first: it starts at the head and attaches… 3. **[Approximate Dynamic Oracle for Dependency Parsing with Reinforcement Learning](https://md.mova.elamur.ai/free/en/docs/papers/ud-2018-yu-approximate-dynamic-oracle-dependency-parsing/)** *(2018)* A dynamic oracle tells a transition parser which action is best from a state the parser itself got into by mistake, so that training covers its own errors. ## Co-authors (4) - [Jonas Kuhn](https://md.mova.elamur.ai/free/en/docs/papers/authors/jonas-kuhn/) ×3 - [Xiang Yu](https://md.mova.elamur.ai/free/en/docs/papers/authors/xiang-yu/) ×3 - [Agnieszka Falenska](https://md.mova.elamur.ai/free/en/docs/papers/authors/agnieszka-falenska/) - Simon Tannert - [← previous Neville Ryant](https://md.mova.elamur.ai/free/en/docs/papers/authors/neville-ryant/) - [next → Nianwen Xue](https://md.mova.elamur.ai/free/en/docs/papers/authors/nianwen-xue/) - [↑ back to Authors](https://md.mova.elamur.ai/free/en/docs/papers/authors/)