Eneko Agirre

Learning Text Representations for 500

2018

Named Entity Disambiguation algorithms typically learn a single model for all target entities. In this paper we present a word expert model and train separate deep learning models for each target entity string, yielding 500K …

Translation Artifacts in Cross-lingual Transfer Learning

2020 · Communities in ADDI (Universidad del Pais Vasco)

Both human and machine translation play a central role in cross-lingual transfer learning: many multilingual datasets have been created through professional translation services, and using machine translation to translate either the test set or the …

Lessons learned from the evaluation of Spanish Language Models

2022 · arXiv (Cornell University)

Given the impact of language models on the field of Natural Language Processing, a number of Spanish encoder-only masked language models (aka BERTs) have been trained and released. These models were developed either within large …

SemEval-2015 Task 2: Semantic Textual Similarity, English, Spanish and Pilot on Interpretability

2015

Eneko Agirre, Carmen Banea, Claire Cardie, Daniel Cer, Mona Diab, Aitor Gonzalez-Agirre, Weiwei Guo, Iñigo Lopez-Gazpio, Montse Maritxalar, Rada Mihalcea, German Rigau, Larraitz Uria, Janyce Wiebe. Proceedings of the 9th International Workshop on Semantic Evaluation …

SemEval-2016 Task 1: Semantic Textual Similarity, Monolingual and Cross-Lingual Evaluation

2016

Eneko Agirre, Carmen Banea, Daniel Cer, Mona Diab, Aitor Gonzalez-Agirre, Rada Mihalcea, German Rigau, Janyce Wiebe. Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016). 2016.

Learning principled bilingual mappings of word embeddings while preserving monolingual invariance

2016

Mapping word embeddings of different languages into a single space has multiple applications. In order to map from a source space into a target space, a common approach is to learn a linear mapping that …

SemEval-2017 Task 1: Semantic Textual Similarity Multilingual and Crosslingual Focused Evaluation

2017

Semantic Textual Similarity (STS) measures the meaning similarity of sentences. Applications include machine translation (MT), summarization, generation, question answering (QA), short answer grading, semantic search, dialog and conversational systems. The STS shared task is a …

Learning bilingual word embeddings with (almost) no bilingual data

2017

Most methods to learn bilingual word embeddings rely on large parallel corpora, which is difficult to obtain for most language pairs. This has motivated an active research line to relax this requirement, with methods that …

Generalizing and Improving Bilingual Word Embedding Mappings with a Multi-Step Framework of Linear Transformations

2018 · Proceedings of the AAAI Conference on Artificial Intelligence

Using a dictionary to map independently trained word embeddings to a shared space has shown to be an effective approach to learn bilingual word embeddings. In this work, we propose a multi-step framework of linear …

A robust self-learning method for fully unsupervised cross-lingual mappings of word embeddings

2018 · Communities in ADDI (University of the Basque Country)

Recent work has managed to learn cross-lingual word embeddings without parallel data by mapping monolingual embeddings to a shared space through adversarial training. However, their evaluation has focused on favorable conditions, using comparable corpora or …

SemEval-2017 Task 1: Semantic Textual Similarity - Multilingual and Cross-lingual Focused Evaluation

2017 · HAL (Le Centre pour la Communication Scientifique Directe)

Semantic Textual Similarity (STS) measures the meaning similarity of sentences. Applications include machine translation (MT), summarization, generation, question answering (QA), short answer grading, semantic search, dialog and conversational systems. The STS shared task is a …

Recent Advances in Natural Language Processing via Large Pre-trained Language Models: A Survey

2023 · ACM Computing Surveys

Large, pre-trained language models (PLMs) such as BERT and GPT have drastically changed the Natural Language Processing (NLP) field. For numerous NLP tasks, approaches leveraging PLMs have achieved state-of-the-art performance. The key idea is to …