Search Results for author: Ran Zmigrod

Found 12 papers, 7 papers with code

Efficient Sampling of Dependency Structure

1 code implementation EMNLP 2021 Ran Zmigrod, Tim Vieira, Ryan Cotterell

In this paper, we adapt two spanning tree sampling algorithms to faithfully sample dependency trees from a graph subject to the root constraint.

UniMorph 4.0: Universal Morphology

no code implementations7 May 2022 Khuyagbaatar Batsuren, Omer Goldman, Salam Khalifa, Nizar Habash, Witold Kieraś, Gábor Bella, Brian Leonard, Garrett Nicolai, Kyle Gorman, Yustinus Ghanggo Ate, Maria Ryskina, Sabrina J. Mielke, Elena Budianskaya, Charbel El-Khaissi, Tiago Pimentel, Michael Gasser, William Lane, Mohit Raj, Matt Coler, Jaime Rafael Montoya Samame, Delio Siticonatzi Camaiteri, Esaú Zumaeta Rojas, Didier López Francis, Arturo Oncevay, Juan López Bautista, Gema Celeste Silva Villegas, Lucas Torroba Hennigen, Adam Ek, David Guriel, Peter Dirix, Jean-Philippe Bernardy, Andrey Scherbakov, Aziyana Bayyr-ool, Antonios Anastasopoulos, Roberto Zariquiey, Karina Sheifer, Sofya Ganieva, Hilaria Cruz, Ritván Karahóǧa, Stella Markantonatou, George Pavlidis, Matvey Plugaryov, Elena Klyachko, Ali Salehi, Candy Angulo, Jatayu Baxi, Andrew Krizhanovsky, Natalia Krizhanovskaya, Elizabeth Salesky, Clara Vania, Sardana Ivanova, Jennifer White, Rowan Hall Maudslay, Josef Valvoda, Ran Zmigrod, Paula Czarnowska, Irene Nikkarinen, Aelita Salchak, Brijesh Bhatt, Christopher Straughn, Zoey Liu, Jonathan North Washington, Yuval Pinter, Duygu Ataman, Marcin Wolinski, Totok Suhardijanto, Anna Yablonskaya, Niklas Stoehr, Hossep Dolatian, Zahroh Nuriah, Shyam Ratan, Francis M. Tyers, Edoardo M. Ponti, Grant Aiton, Aryaman Arora, Richard J. Hatcher, Ritesh Kumar, Jeremiah Young, Daria Rodionova, Anastasia Yemelina, Taras Andrushko, Igor Marchenko, Polina Mashkovtseva, Alexandra Serova, Emily Prud'hommeaux, Maria Nepomniashchaya, Fausto Giunchiglia, Eleanor Chodroff, Mans Hulden, Miikka Silfverberg, Arya D. McCarthy, David Yarowsky, Ryan Cotterell, Reut Tsarfaty, Ekaterina Vylomova

The project comprises two major thrusts: a language-independent feature schema for rich morphological annotation and a type-level resource of annotated data in diverse languages realizing that schema.

Morphological Inflection

Exact Paired-Permutation Testing for Structured Test Statistics

1 code implementation3 May 2022 Ran Zmigrod, Tim Vieira, Ryan Cotterell

However, practitioners rely on Monte Carlo approximation to perform this test due to a lack of a suitable exact algorithm.

Efficient Sampling of Dependency Structures

no code implementations14 Sep 2021 Ran Zmigrod, Tim Vieira, Ryan Cotterell

Colbourn (1996)'s sampling algorithm has a running time of $\mathcal{O}(N^3)$, which is often greater than the mean hitting time of a directed graph.

On Finding the K-best Non-projective Dependency Trees

1 code implementation ACL 2021 Ran Zmigrod, Tim Vieira, Ryan Cotterell

Furthermore, we present a novel extension of the algorithm for decoding the K-best dependency trees of a graph which are subject to a root constraint.

Dependency Parsing

On Finding the $K$-best Non-projective Dependency Trees

1 code implementation1 Jun 2021 Ran Zmigrod, Tim Vieira, Ryan Cotterell

Furthermore, we present a novel extension of the algorithm for decoding the $K$-best dependency trees of a graph which are subject to a root constraint.

Dependency Parsing

Higher-order Derivatives of Weighted Finite-state Machines

no code implementations ACL 2021 Ran Zmigrod, Tim Vieira, Ryan Cotterell

In the case of second-order derivatives, our scheme runs in the optimal $\mathcal{O}(A^2 N^4)$ time where $A$ is the alphabet size and $N$ is the number of states.

Please Mind the Root: Decoding Arborescences for Dependency Parsing

1 code implementation EMNLP 2020 Ran Zmigrod, Tim Vieira, Ryan Cotterell

The connection between dependency trees and spanning trees is exploited by the NLP community to train and to decode graph-based dependency parsers.

Dependency Parsing

Efficient Computation of Expectations under Spanning Tree Distributions

no code implementations29 Aug 2020 Ran Zmigrod, Tim Vieira, Ryan Cotterell

We propose unified algorithms for the important cases of first-order expectations and second-order expectations in edge-factored, non-projective spanning-tree models.

Information-Theoretic Probing for Linguistic Structure

1 code implementation ACL 2020 Tiago Pimentel, Josef Valvoda, Rowan Hall Maudslay, Ran Zmigrod, Adina Williams, Ryan Cotterell

The success of neural networks on a diverse set of NLP tasks has led researchers to question how much these networks actually ``know'' about natural language.

Word Embeddings

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