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Found 25 papers, 1 papers with code
A Simple and Effective Approach to Automatic Post-Editing with Transfer Learning
Automatic post-editing (APE) seeks to automatically refine the output of a black-box machine translation (MT) system through human post-edits.
Pushing the Limits of Translation Quality Estimation
Translation quality estimation is a task of growing importance in NLP, due to its potential to reduce post-editing human effort in disruptive ways.
Learning What's Easy: Fully Differentiable Neural Easy-First Taggers
Our models compare favourably to BILSTM taggers on three sequence tagging tasks.
Interpretable Structure Induction via Sparse Attention
Neural network methods are experiencing wide adoption in NLP, thanks to their empirical performance on many tasks.
Findings of the WMT 2018 Shared Task on Quality Estimation
We report the results of the WMT18 shared task on Quality Estimation, i. e. the task of predicting the quality of the output of machine translation systems at various granularity levels: word, phrase, sentence and document.
Priberam Compressive Summarization Corpus: A New Multi-Document Summarization Corpus for European Portuguese
In this paper, we introduce the Priberam Compressive Summarization Corpus, a new multi-document summarization corpus for European Portuguese.
Latent Structure Models for Natural Language Processing
Latent structure models are a powerful tool for modeling compositional data, discovering linguistic structure, and building NLP pipelines.
IT--IST at the SIGMORPHON 2019 Shared Task: Sparse Two-headed Models for Inflection
This paper presents the Instituto de Telecomunica{\c{c}}{\~o}es{--}Instituto Superior T{\'e}cnico submission to Task 1 of the SIGMORPHON 2019 Shared Task.
Findings of the WMT 2019 Shared Tasks on Quality Estimation
We report the results of the WMT19 shared task on Quality Estimation, i. e. the task of predicting the quality of the output of machine translation systems given just the source text and the hypothesis translations.
Revisiting Higher-Order Dependency Parsers
Neural encoders have allowed dependency parsers to shift from higher-order structured models to simpler first-order ones, making decoding faster and still achieving better accuracy than non-neural parsers.
One-Size-Fits-All Multilingual Models
For both tasks, we present multilingual models, training jointly on data in all languages.
