Search Results for author: Fernando Alva-Manchego

Found 16 papers, 11 papers with code

Towards Readability-Controlled Machine Translation of COVID-19 Texts

no code implementations EAMT 2022 Fernando Alva-Manchego, Matthew Shardlow

This project investigates the capabilities of Machine Translation models for generating translations at varying levels of readability, focusing on texts related to COVID-19.

Machine Translation Text Simplification +1

The (Un)Suitability of Automatic Evaluation Metrics for Text Simplification

1 code implementation CL (ACL) 2021 Fernando Alva-Manchego, Carolina Scarton, Lucia Specia

Second, we conduct the first meta-evaluation of automatic metrics in Text Simplification, using our new data set (and other existing data) to analyze the variation of the correlation between metrics’ scores and human judgments across three dimensions: the perceived simplicity level, the system type, and the set of references used for computation.

Sentence Text Simplification

Validating Quality Estimation in a Computer-Aided Translation Workflow: Speed, Cost and Quality Trade-off

no code implementations MTSummit 2021 Fernando Alva-Manchego, Lucia Specia, Sara Szoc, Tom Vanallemeersch, Heidi Depraetere

In this scenario, a Quality Estimation (QE) tool can be used to score MT outputs, and a threshold on the QE scores can be applied to decide whether an MT output can be used as-is or requires human post-edition.

Machine Translation Translation

Simple TICO-19: A Dataset for Joint Translation and Simplification of COVID-19 Texts

1 code implementation LREC 2022 Matthew Shardlow, Fernando Alva-Manchego

Specialist high-quality information is typically first available in English, and it is written in a language that may be difficult to understand by most readers.

Machine Translation Translation

PeruSIL: A Framework to Build a Continuous Peruvian Sign Language Interpretation Dataset

no code implementations SignLang (LREC) 2022 Gissella Bejarano, Joe Huamani-Malca, Francisco Cerna-Herrera, Fernando Alva-Manchego, Pablo Rivas

Our contributions: i) we design a framework to annotate a sign Language dataset; ii) we release the first annotated LSP multi-modal interpretation dataset (AEC); iii) we evaluate the annotation done by hearing people by training a sign language recognition model.

Sign Language Recognition

A Practical Toolkit for Multilingual Question and Answer Generation

1 code implementation27 May 2023 Asahi Ushio, Fernando Alva-Manchego, Jose Camacho-Collados

Generating questions along with associated answers from a text has applications in several domains, such as creating reading comprehension tests for students, or improving document search by providing auxiliary questions and answers based on the query.

Answer Generation Reading Comprehension +1

An Empirical Comparison of LM-based Question and Answer Generation Methods

1 code implementation26 May 2023 Asahi Ushio, Fernando Alva-Manchego, Jose Camacho-Collados

This task has a variety of applications, such as data augmentation for question answering (QA) models, information retrieval and education.

Answer Generation Data Augmentation +4

Generative Language Models for Paragraph-Level Question Generation

1 code implementation8 Oct 2022 Asahi Ushio, Fernando Alva-Manchego, Jose Camacho-Collados

It includes general-purpose datasets such as SQuAD for English, datasets from ten domains and two styles, as well as datasets in eight different languages.

Question Answering Question Generation +1

Knowledge Distillation for Quality Estimation

1 code implementation Findings (ACL) 2021 Amit Gajbhiye, Marina Fomicheva, Fernando Alva-Manchego, Frédéric Blain, Abiola Obamuyide, Nikolaos Aletras, Lucia Specia

Quality Estimation (QE) is the task of automatically predicting Machine Translation quality in the absence of reference translations, making it applicable in real-time settings, such as translating online social media conversations.

Data Augmentation Knowledge Distillation +2

Controllable Text Simplification with Explicit Paraphrasing

no code implementations NAACL 2021 Mounica Maddela, Fernando Alva-Manchego, Wei Xu

Text Simplification improves the readability of sentences through several rewriting transformations, such as lexical paraphrasing, deletion, and splitting.

Data Augmentation Text Simplification

ASSET: A Dataset for Tuning and Evaluation of Sentence Simplification Models with Multiple Rewriting Transformations

1 code implementation ACL 2020 Fernando Alva-Manchego, Louis Martin, Antoine Bordes, Carolina Scarton, Benoît Sagot, Lucia Specia

Furthermore, we motivate the need for developing better methods for automatic evaluation using ASSET, since we show that current popular metrics may not be suitable when multiple simplification transformations are performed.

Sentence

EASSE: Easier Automatic Sentence Simplification Evaluation

1 code implementation IJCNLP 2019 Fernando Alva-Manchego, Louis Martin, Carolina Scarton, Lucia Specia

We introduce EASSE, a Python package aiming to facilitate and standardise automatic evaluation and comparison of Sentence Simplification (SS) systems.

Sentence

Cannot find the paper you are looking for? You can Submit a new open access paper.