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Found 10 papers, 3 papers with code
Dialect-robust Evaluation of Generated Text
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
We present FRMT, a new dataset and evaluation benchmark for Few-shot Region-aware Machine Translation, a type of style-targeted translation.
FormNet: Structural Encoding beyond Sequential Modeling in Form Document Information Extraction
Sequence modeling has demonstrated state-of-the-art performance on natural language and document understanding tasks.
Universal Dependency Parsing from Scratch
This paper describes Stanford's system at the CoNLL 2018 UD Shared Task.
Ranked #4 on
Dependency Parsing
on Universal Dependencies
Simpler but More Accurate Semantic Dependency Parsing
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.
Ranked #4 on
Semantic Dependency Parsing
on DM
Stanford's Graph-based Neural Dependency Parser at the CoNLL 2017 Shared Task
This paper describes the neural dependency parser submitted by Stanford to the CoNLL 2017 Shared Task on parsing Universal Dependencies.
Deep Biaffine Attention for Neural Dependency Parsing
This paper builds off recent work from Kiperwasser & Goldberg (2016) using neural attention in a simple graph-based dependency parser.
Ranked #2 on
Dependency Parsing
on CoNLL-2009
Universal Stanford dependencies: A cross-linguistic typology
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
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.
