Welcome to the page of Generationary, a project of the Sapienza NLP Group, developed with the support of the awesome MOUSSE ERC project!
Generationary is a neural seq2seq model which contextualizes a target expression in a sentence by generating an ad hoc definition.
Our work is a unified approach to computational lexical-semantic tasks, encompassing state-of-the-art Word Sense Disambiguation, Definition Modeling and Word-in-Context.
To learn more, read our paper:
Michele Bevilacqua, Marco Maru, and Roberto Navigli. 2020. Generationary or: “How We Went beyond Sense Inventories and Learned to Gloss”. Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP).
Data and Software
Download Hei++ and SamplEval here.
The code is available on request (you can contact us at bevilacqua [at] di [dot] uniroma1 [dot] it or marco [dot] maru [at] uniroma1 [dot] it).
Reference
@inproceedings{bevilacqua-etal-2020-generationary,
title = "Generationary or: {``}How We Went beyond Word Sense Inventories and Learned to Gloss{''}",
author = "Bevilacqua, Michele and
Maru, Marco and
Navigli, Roberto",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.emnlp-main.585",
pages = "7207--7221",
}
Authors
Acknowledgements
The authors gratefully acknowledge the support of the ERC Consolidator Grant MOUSSE No. 726487 under the European Union’s Horizon 2020 research and innovation programme.