Fabio Alves


Referring to what you know and do not know: Making Referring Expression Generation Models Generalize To Unseen Entities
Rossana Cunha | Thiago Castro Ferreira | Adriana Pagano | Fabio Alves
Proceedings of the 28th International Conference on Computational Linguistics

Data-to-text Natural Language Generation (NLG) is the computational process of generating natural language in the form of text or voice from non-linguistic data. A core micro-planning task within NLG is referring expression generation (REG), which aims to automatically generate noun phrases to refer to entities mentioned as discourse unfolds. A limitation of novel REG models is not being able to generate referring expressions to entities not encountered during the training process. To solve this problem, we propose two extensions to NeuralREG, a state-of-the-art encoder-decoder REG model. The first is a copy mechanism, whereas the second consists of representing the gender and type of the referent as inputs to the model. Drawing on the results of automatic and human evaluation as well as an ablation study using the WebNLG corpus, we contend that our proposal contributes to the generation of more meaningful referring expressions to unseen entities than the original system and related work. Code and all produced data are publicly available.


Does post-editing increase usability? A study with Brazilian Portuguese as target language
Sheila Castilho | Sharon O’Brien | Fabio Alves | Morgan O’Brien
Proceedings of the 17th Annual conference of the European Association for Machine Translation


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Identifying instances of processing effort in translation through heat maps: an eye-tracking study using multiple input sources
Fabio Alves | José Luiz Gonçalves | Karina Szpak
Proceedings of the First Workshop on Eye-tracking and Natural Language Processing