Search Results for author: Matthew R. Gormley

Found 20 papers, 6 papers with code

Effective Convolutional Attention Network for Multi-label Clinical Document Classification

no code implementations EMNLP 2021 Yang Liu, Hua Cheng, Russell Klopfer, Matthew R. Gormley, Thomas Schaaf

Multi-label document classification (MLDC) problems can be challenging, especially for long documents with a large label set and a long-tail distribution over labels.

Classification Document Classification +1

Comparative Error Analysis in Neural and Finite-state Models for Unsupervised Character-level Transduction

no code implementations ACL (SIGMORPHON) 2021 Maria Ryskina, Eduard Hovy, Taylor Berg-Kirkpatrick, Matthew R. Gormley

Traditionally, character-level transduction problems have been solved with finite-state models designed to encode structural and linguistic knowledge of the underlying process, whereas recent approaches rely on the power and flexibility of sequence-to-sequence models with attention.

Limitations of Autoregressive Models and Their Alternatives

no code implementations NAACL 2021 Chu-Cheng Lin, Aaron Jaech, Xin Li, Matthew R. Gormley, Jason Eisner

Standard autoregressive language models perform only polynomial-time computation to compute the probability of the next symbol.

Language Modelling

Phonetic and Visual Priors for Decipherment of Informal Romanization

1 code implementation ACL 2020 Maria Ryskina, Matthew R. Gormley, Taylor Berg-Kirkpatrick

Informal romanization is an idiosyncratic process used by humans in informal digital communication to encode non-Latin script languages into Latin character sets found on common keyboards.

Decipherment

Bilingual Lexicon Induction with Semi-supervision in Non-Isometric Embedding Spaces

1 code implementation ACL 2019 Barun Patra, Joel Ruben Antony Moniz, Sarthak Garg, Matthew R. Gormley, Graham Neubig

We then propose Bilingual Lexicon Induction with Semi-Supervision (BLISS) --- a semi-supervised approach that relaxes the isometric assumption while leveraging both limited aligned bilingual lexicons and a larger set of unaligned word embeddings, as well as a novel hubness filtering technique.

Bilingual Lexicon Induction Word Embeddings

Neural Finite-State Transducers: Beyond Rational Relations

no code implementations NAACL 2019 Chu-Cheng Lin, Hao Zhu, Matthew R. Gormley, Jason Eisner

We introduce neural finite state transducers (NFSTs), a family of string transduction models defining joint and conditional probability distributions over pairs of strings.

Approximation-Aware Dependency Parsing by Belief Propagation

no code implementations TACL 2015 Matthew R. Gormley, Mark Dredze, Jason Eisner

We show how to adjust the model parameters to compensate for the errors introduced by this approximation, by following the gradient of the actual loss on training data.

Dependency Parsing

Improved Relation Extraction with Feature-Rich Compositional Embedding Models

1 code implementation EMNLP 2015 Matthew R. Gormley, Mo Yu, Mark Dredze

We propose a Feature-rich Compositional Embedding Model (FCM) for relation extraction that is expressive, generalizes to new domains, and is easy-to-implement.

 Ranked #1 on Relation Extraction on ACE 2005 (Cross Sentence metric)

Relation Classification Word Embeddings

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