Fernando Alva-Manchego
Fernando Alva-Manchego
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Simple TICO-19: A Dataset for Joint Translation and Simplification of COVID-19 Texts
We introduce Simple TICO-19, a new language resource containing manual simplifications of the English and Spanish portions of the TICO-19 corpus for Machine Translation of COVID-19 literature.
Matthew Shardlow
,
Fernando Alva-Manchego
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Dataset
ACL Anthology
Towards Readability-Controlled Machine Translation of COVID-19 Texts
This project proposes to investigate the capabilities of machine translation models for generating translations at varying levels of readability, focusing on texts about COVID-19.
Fernando Alva-Manchego
,
Matthew Shardlow
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Poster
ACL Anthology
deepQuest-py: Large and Distilled Models for Quality Estimation
We introduce deepQuest-py, a framework for training and evaluation of large and light-weight models for Quality Estimation
Fernando Alva-Manchego
,
Abiola Obamuyide
,
Amit Gajbhiye
,
Frédéric Blain
,
Marina Fomicheva
,
Lucia Specia
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Code
Poster
Slides
DOI
ACL Anthology
Validating Quality Estimation in a Computer-Aided Translation Workflow: Speed, Cost and Quality Trade-off
We set up a case-study on the trade-off between speed, cost and quality, investigating the benefits of Quality Estimation models in a real-world scenario, where we rely on end-user acceptability as quality metric.
Fernando Alva-Manchego
,
Lucia Specia
,
Sara Szoc
,
Tom Vanallemeersch
,
Heidi Depraetere
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Slides
ACL Anthology
IAPUCP at SemEval-2021 Task 1: Stacking Fine-Tuned Transformers is Almost All You Need for Lexical Complexity Prediction
This paper describes our submission to SemEval-2021 Task 1: predicting the complexity score for single words. Our model leverages …
Kervy Rivas Rojas
,
Fernando Alva-Manchego
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Code
DOI
ACL Anthology
Knowledge Distillation for Quality Estimation
We propose to transfer knowledge from a strong Quality Estimation (QE) teacher model to a much smaller model with a different, shallower architecture, leading to light-weight QE models that perform competitively with distilled pre-trained representations.
Amit Gajbhiye
,
Marina Fomicheva
,
Fernando Alva-Manchego
,
Frédéric Blain
,
Abiola Obamuyide
,
Nikolaos Aletras
,
Lucia Specia
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Code
DOI
ACL Anthology
Controllable Text Simplification with Explicit Paraphrasing
We propose a novel hybrid approach that leverages linguistically-motivated rules for splitting and deletion, and couples them with a neural paraphrasing model to produce varied rewriting style.
Mounica Maddela
,
Fernando Alva-Manchego
,
Wei Xu
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Code
Poster
Slides
DOI
ACL Anthology
ASSET: A Dataset for Tuning and Evaluation of Sentence Simplification Models with Multiple Rewriting Transformations
We introduce ASSET, a dataset for assessing sentence simplification in English. ASSET is a crowdsourced multi-reference corpus where each simplification was produced by executing several rewriting transformations.
Fernando Alva-Manchego
,
Louis Martin
,
Antoine Bordes
,
Carolina Scarton
,
Benoı̂t Sagot
,
Lucia Specia
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Code
Slides
DOI
ACL Anthology
EASSE: Easier Automatic Sentence Simplification Evaluation
We introduce EASSE, a Python package aiming to facilitate and standardise automatic evaluation and comparison of Sentence Simplification systems.
Fernando Alva-Manchego
,
Louis Martin
,
Carolina Scarton
,
Lucia Specia
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Code
Poster
DOI
ACL Anthology
Cross-Sentence Transformations in Text Simplification
Current approaches to Text Simplification focus on simplifying sentences individually. However, certain simplification transformations …
Fernando Alva-Manchego
,
Carolina Scarton
,
Lucia Specia
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Poster
ACL Anthology
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