Slav Petrov

Universal Dependencies v1: A Multilingual Treebank Collection

2016

Cross-linguistically consistent annotation is necessary for sound comparative evaluation and cross-lingual learning experiments.It is also useful for multilingual system development and comparative linguistic studies.Universal Dependencies is an open community effort to create cross-linguistically consistent treebank …

Structured Training for Neural Network Transition-Based Parsing

2015

David Weiss, Chris Alberti, Michael Collins, Slav Petrov. Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). 2015.

Globally Normalized Transition-Based Neural Networks

2016

Daniel Andor, Chris Alberti, David Weiss, Aliaksei Severyn, Alessandro Presta, Kuzman Ganchev, Slav Petrov, Michael Collins. Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2016.

CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies

2017

Daniel Zeman, Martin Popel, Milan Straka, Jan Hajič, Joakim Nivre, Filip Ginter, Juhani Luotolahti, Sampo Pyysalo, Slav Petrov, Martin Potthast, Francis Tyers, Elena Badmaeva, Memduh Gokirmak, Anna Nedoluzhko, Silvie Cinková, Jan Hajič jr., Jaroslava Hlaváčová, …

Natural Questions: A Benchmark for Question Answering Research

2019 · Transactions of the Association for Computational Linguistics

We present the Natural Questions corpus, a question answering data set. Questions consist of real anonymized, aggregated queries issued to the Google search engine. An annotator is presented with a question along with a Wikipedia …

CoNLL 2018 Shared Task : Multilingual Parsing from Raw Text to Universal Dependencies

2018 · Conference on Computational Natural Language Learning

Every year, the Conference on Computational Natural Language Learning (CoNLL) features a shared task, in which participants train and test their learning systems on the same data sets. In 2018, one of two tasks was …

PaLM: Scaling Language Modeling with Pathways

2022 · arXiv (Cornell University)

Large language models have been shown to achieve remarkable performance across a variety of natural language tasks using few-shot learning, which drastically reduces the number of task-specific training examples needed to adapt the model to …

Scaling Instruction-Finetuned Language Models

2022 · arXiv (Cornell University)

Finetuning language models on a collection of datasets phrased as instructions has been shown to improve model performance and generalization to unseen tasks. In this paper we explore instruction finetuning with a particular focus on …