Search Results for author: Timothy Dozat

Found 10 papers, 3 papers with code

Dialect-robust Evaluation of Generated Text

no code implementations2 Nov 2022 Jiao Sun, Thibault Sellam, Elizabeth Clark, Tu Vu, Timothy Dozat, Dan Garrette, Aditya Siddhant, Jacob Eisenstein, Sebastian Gehrmann

Evaluation metrics that are not robust to dialect variation make it impossible to tell how well systems perform for many groups of users, and can even penalize systems for producing text in lower-resource dialects.

FRMT: A Benchmark for Few-Shot Region-Aware Machine Translation

no code implementations1 Oct 2022 Parker Riley, Timothy Dozat, Jan A. Botha, Xavier Garcia, Dan Garrette, Jason Riesa, Orhan Firat, Noah Constant

We present FRMT, a new dataset and evaluation benchmark for Few-shot Region-aware Machine Translation, a type of style-targeted translation.

Machine Translation Translation

Simpler but More Accurate Semantic Dependency Parsing

3 code implementations ACL 2018 Timothy Dozat, Christopher D. Manning

While syntactic dependency annotations concentrate on the surface or functional structure of a sentence, semantic dependency annotations aim to capture between-word relationships that are more closely related to the meaning of a sentence, using graph-structured representations.

Dependency Parsing Semantic Dependency Parsing

Stanford's Graph-based Neural Dependency Parser at the CoNLL 2017 Shared Task

no code implementations CONLL 2017 Timothy Dozat, Peng Qi, Christopher D. Manning

This paper describes the neural dependency parser submitted by Stanford to the CoNLL 2017 Shared Task on parsing Universal Dependencies.

Dependency Parsing

Deep Biaffine Attention for Neural Dependency Parsing

25 code implementations6 Nov 2016 Timothy Dozat, Christopher D. Manning

This paper builds off recent work from Kiperwasser & Goldberg (2016) using neural attention in a simple graph-based dependency parser.

Dependency Parsing

Universal Stanford dependencies: A cross-linguistic typology

no code implementations LREC 2014 Marie-Catherine de Marneffe, Timothy Dozat, Natalia Silveira, Katri Haverinen, Filip Ginter, Joakim Nivre, Christopher D. Manning

Revisiting the now de facto standard Stanford dependency representation, we propose an improved taxonomy to capture grammatical relations across languages, including morphologically rich ones.

A Gold Standard Dependency Corpus for English

no code implementations LREC 2014 Natalia Silveira, Timothy Dozat, Marie-Catherine de Marneffe, Samuel Bowman, Miriam Connor, John Bauer, Chris Manning

This resource addresses the lack of a gold standard dependency treebank for English, as well as the limited availability of gold standard syntactic annotations for English informal text genres.

Sentiment Analysis

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