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Found 11 papers, 2 papers with code
ParCor 1.0: A Parallel Pronoun-Coreference Corpus to Support Statistical MT
We present ParCor, a parallel corpus of texts in which pronoun coreference ― reduced coreference in which pronouns are used as referring expressions ― has been annotated.
Parser Training with Heterogeneous Treebanks
How to make the most of multiple heterogeneous treebanks when training a monolingual dependency parser is an open question.
Does Hamiltonian Monte Carlo mix faster than a random walk on multimodal densities?
In this paper, we investigate a different scaling question: does HMC beat RWM for highly $\textit{multimodal}$ targets?
An Investigation of the Interactions Between Pre-Trained Word Embeddings, Character Models and POS Tags in Dependency Parsing
We provide a comprehensive analysis of the interactions between pre-trained word embeddings, character models and POS tags in a transition-based dependency parser.
82 Treebanks, 34 Models: Universal Dependency Parsing with Multi-Treebank Models
We present the Uppsala system for the CoNLL 2018 Shared Task on universal dependency parsing.
Importance is Important: A Guide to Informed Importance Tempering Methods
Informed importance tempering (IIT) is an easy-to-implement MCMC algorithm that can be seen as an extension of the familiar Metropolis-Hastings algorithm with the special feature that informed proposals are always accepted, and which was shown in Zhou and Smith (2022) to converge much more quickly in some common circumstances.
On Cyclical MCMC Sampling
Cyclical MCMC is a novel MCMC framework recently proposed by Zhang et al. (2019) to address the challenge posed by high-dimensional multimodal posterior distributions like those arising in deep learning.
