Search Results for author: Chris Hokamp

Found 21 papers, 5 papers with code

A Large-Scale Multi-Document Summarization Dataset from the Wikipedia Current Events Portal

1 code implementation ACL 2020 Demian Gholipour Ghalandari, Chris Hokamp, Nghia The Pham, John Glover, Georgiana Ifrim

Multi-document summarization (MDS) aims to compress the content in large document collections into short summaries and has important applications in story clustering for newsfeeds, presentation of search results, and timeline generation.

Document Summarization Multi-Document Summarization

Task Selection Policies for Multitask Learning

no code implementations14 Jul 2019 John Glover, Chris Hokamp

One of the questions that arises when designing models that learn to solve multiple tasks simultaneously is how much of the available training budget should be devoted to each individual task.

Curriculum Learning Language understanding +1

Evaluating the Supervised and Zero-shot Performance of Multi-lingual Translation Models

no code implementations WS 2019 Chris Hokamp, John Glover, Demian Gholipour

To our knowledge, this is the largest evaluation of multi-lingual translation yet conducted in terms of the total size of the training data we use, and in terms of the diversity of zero-shot translation pairs we evaluate.

Translation

Off-the-Shelf Unsupervised NMT

no code implementations6 Nov 2018 Chris Hokamp, Sebastian Ruder, John Glover

We frame unsupervised machine translation (MT) in the context of multi-task learning (MTL), combining insights from both directions.

Multi-Task Learning Translation +1

Ensembling Factored Neural Machine Translation Models for Automatic Post-Editing and Quality Estimation

1 code implementation WS 2017 Chris Hokamp

This work presents a novel approach to Automatic Post-Editing (APE) and Word-Level Quality Estimation (QE) using ensembles of specialized Neural Machine Translation (NMT) systems.

Automatic Post-Editing Translation

Pushing the Limits of Translation Quality Estimation

no code implementations TACL 2017 Andr{\'e} F. T. Martins, Marcin Junczys-Dowmunt, Fabio N. Kepler, Ram{\'o}n Astudillo, Chris Hokamp, Roman Grundkiewicz

Translation quality estimation is a task of growing importance in NLP, due to its potential to reduce post-editing human effort in disruptive ways.

Automatic Post-Editing Translation

Modeling Language Proficiency Using Implicit Feedback

no code implementations LREC 2014 Chris Hokamp, Rada Mihalcea, Peter Schuelke

We describe the results of several experiments with interactive interfaces for native and L2 English students, designed to collect implicit feedback from students as they complete a reading activity.

Reading Comprehension Text Simplification

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