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.
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.
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.
1 code implementation • EMNLP (ACL) 2021 • Fernando Alva-Manchego, Abiola Obamuyide, Amit Gajbhiye, Frédéric Blain, Marina Fomicheva, Lucia Specia
We introduce deepQuest-py, a framework for training and evaluation of large and light-weight models for Quality Estimation (QE).
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.
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.
1 code implementation • 24 Oct 2023 • Tannon Kew, Alison Chi, Laura Vásquez-Rodríguez, Sweta Agrawal, Dennis Aumiller, Fernando Alva-Manchego, Matthew Shardlow
Our performance benchmark will be available as a resource for the development of future TS methods and evaluation metrics.
1 code implementation • 27 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.
1 code implementation • 26 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.
1 code implementation • 8 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.
no code implementations • SEMEVAL 2021 • Kervy Rivas Rojas, Fernando Alva-Manchego
This paper describes our submission to SemEval-2021 Task 1: predicting the complexity score for single words.
Complex Word Identification Lexical Complexity Prediction +2
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.
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.
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.
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.
1 code implementation • NAACL 2019 • Pierre Finnimore, Elisabeth Fritzsch, Daniel King, Alison Sneyd, Aneeq Ur Rehman, Fernando Alva-Manchego, Andreas Vlachos
Complex Word Identification (CWI) is the task of identifying which words or phrases in a sentence are difficult to understand by a target audience.