[Mova](https://md.mova.elamur.ai/free/en/) · [this page in HTML](https://mova.elamur.ai/free/en/docs/papers/authors/daniel-gildea/) [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/) / Daniel Gildea # Daniel Gildea 6 paper cards in Mova's library · 2001–2019 Daniel Gildea wrote 6 of the papers Mova reads, in 2001–2019, most of them on [Machine translation](https://md.mova.elamur.ai/free/en/docs/papers/topics/machine-translation/), [Semantic role labeling](https://md.mova.elamur.ai/free/en/docs/papers/topics/semantic-role-labeling/), [Treebanks](https://md.mova.elamur.ai/free/en/docs/papers/topics/treebanks/); often with [Linfeng Song](https://md.mova.elamur.ai/free/en/docs/papers/authors/linfeng-song/), [Yue Zhang](https://md.mova.elamur.ai/free/en/docs/papers/authors/yue-zhang/), [Zhiguo Wang](https://md.mova.elamur.ai/free/en/docs/papers/authors/zhiguo-wang/). [OpenAlex](https://openalex.org/A5020945377) · [ORCID](https://orcid.org/0000-0002-7858-2624) · [**h-index 37**](https://openalex.org/authors/A5020945377) · [9308 citations](https://openalex.org/authors/A5020945377) · [190 works](https://openalex.org/works?filter=author.id:A5020945377) — 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=Daniel+Gildea) · [Semantic Scholar](https://www.semanticscholar.org/search?q=Daniel+Gildea) · [dblp](https://dblp.org/search?q=Daniel+Gildea) · [ACL Anthology](https://aclanthology.org/search/?q=Daniel+Gildea) · [ORCID](https://orcid.org/orcid-search/search?searchQuery=Daniel+Gildea) Topics: [Machine translation](https://md.mova.elamur.ai/free/en/docs/papers/topics/machine-translation/) 3 · [Semantic role labeling](https://md.mova.elamur.ai/free/en/docs/papers/topics/semantic-role-labeling/) 2 · [Treebanks](https://md.mova.elamur.ai/free/en/docs/papers/topics/treebanks/) 1 ## Papers 1. **[Semantic Neural Machine Translation Using AMR](https://md.mova.elamur.ai/free/en/docs/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… 2. **[AMR-to-text Generation with Synchronous Node Replacement Grammar](https://md.mova.elamur.ai/free/en/docs/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. 3. **[The Proposition Bank: An Annotated Corpus of Semantic Roles](https://md.mova.elamur.ai/free/en/docs/papers/palmer-2005-proposition-bank-annotated-corpus-semantic/)** *(2005)* PropBank adds predicate-argument labels to the Penn Treebank (Wall Street Journal part): numbered roles Arg0 to Arg5 that are defined per verb sense, plus adjunct tags (ArgM with function types such as temporal, locative, manner). 4. **[Syntactic Features for Evaluation of Machine Translation](https://md.mova.elamur.ai/free/en/docs/papers/liu-2005-syntactic-features-evaluation-machine-translation/)** *(2005)* The paper adds syntax to n-gram evaluation. The headword-chain metric (HWCM) matches the head-modifier paths of the reference against the hypothesis. The subtree metric (STM) matches constituent subtrees, and a dependency subtree variant (DSTM) matches head subtrees. All of them need a parser on the hypothesis. At… 5. **[Automatic Labeling of Semantic Roles](https://md.mova.elamur.ai/free/en/docs/papers/gildea-2002-automatic-labeling-semantic-roles/)** *(2002)* The first statistical system for labeling semantic roles with FrameNet frame elements (Agent, Patient, Speaker, Topic and others). 6. **[Corpus Variation and Parser Performance](https://md.mova.elamur.ai/free/en/docs/papers/aclbulk-2001-gildea-corpus-variation-parser-performance/)** *(2001)* The paper asks how much a statistical parser depends on the corpus it is trained on. It uses Collins' Model 1, a lexicalized generative parser, trained and tested on the Wall Street Journal (39,832 training sentences) and on the Brown corpus (about 21,800 training sentences, with every tenth sentence held out). A… ## Co-authors (9) - [Linfeng Song](https://md.mova.elamur.ai/free/en/docs/papers/authors/linfeng-song/) ×2 - [Yue Zhang](https://md.mova.elamur.ai/free/en/docs/papers/authors/yue-zhang/) ×2 - [Zhiguo Wang](https://md.mova.elamur.ai/free/en/docs/papers/authors/zhiguo-wang/) ×2 - [Daniel Jurafsky](https://md.mova.elamur.ai/free/en/docs/papers/authors/daniel-jurafsky/) - Ding Liu - Jinsong Su - [Martha Palmer](https://md.mova.elamur.ai/free/en/docs/papers/authors/martha-palmer/) - [Paul Kingsbury](https://md.mova.elamur.ai/free/en/docs/papers/authors/paul-kingsbury/) - Xiaochang Peng - [← previous Daniel G. Swanson](https://md.mova.elamur.ai/free/en/docs/papers/authors/daniel-g-swanson/) - [next → Daniel Hardt](https://md.mova.elamur.ai/free/en/docs/papers/authors/daniel-hardt/) - [↑ back to Authors](https://md.mova.elamur.ai/free/en/docs/papers/authors/)