Guillermo Garrido


2020

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FELIX: Flexible Text Editing Through Tagging and Insertion
Jonathan Mallinson | Aliaksei Severyn | Eric Malmi | Guillermo Garrido
Findings of the Association for Computational Linguistics: EMNLP 2020

We present FELIX – a flexible text-editing approach for generation, designed to derive maximum benefit from the ideas of decoding with bi-directional contexts and self-supervised pretraining. In contrast to conventional sequenceto-sequence (seq2seq) models, FELIX is efficient in low-resource settings and fast at inference time, while being capable of modeling flexible input-output transformations. We achieve this by decomposing the text-editing task into two sub-tasks: tagging to decide on the subset of input tokens and their order in the output text and insertion to in-fill the missing tokens in the output not present in the input. The tagging model employs a novel Pointer mechanism, while the insertion model is based on a Masked Language Model (MLM). Both of these models are chosen to be non-autoregressive to guarantee faster inference. FELIX performs favourably when compared to recent text-editing methods and strong seq2seq baselines when evaluated on four NLG tasks: Sentence Fusion, Machine Translation Automatic Post-Editing, Summarization, and Text Simplification

2013

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HEADY: News headline abstraction through event pattern clustering
Enrique Alfonseca | Daniele Pighin | Guillermo Garrido
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

2012

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Temporally Anchored Relation Extraction
Guillermo Garrido | Anselmo Peñas | Bernardo Cabaleiro | Álvaro Rodrigo
Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Pattern Learning for Relation Extraction with a Hierarchical Topic Model
Enrique Alfonseca | Katja Filippova | Jean-Yves Delort | Guillermo Garrido
Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

2011

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Detecting Compositionality Using Semantic Vector Space Models Based on Syntactic Context. Shared Task System Description
Guillermo Garrido | Anselmo Peñas
Proceedings of the Workshop on Distributional Semantics and Compositionality