---
title: "ALaSca: an Automated approach for Large-Scale Lexical Substitution"
authors:
  - "Caterina Lacerra"
  - "Tommaso Pasini"
  - "Rocco Tripodi"
  - "Roberto Navigli"
doi: "10.24963/ijcai.2021/528"
url: "https://ijcai.org/proceedings/2021/528"
pdf: "https://ijcai.org/proceedings/2021/0528.pdf"
venue: "Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence"
year: 2021
pages: "3836-3842"
track: "Main Track"
keywords:
  - "Natural Language Processing: Natural Language Semantics"
  - "Natural Language Processing: Resources and Evaluation"
---

# ALaSca: an Automated approach for Large-Scale Lexical Substitution

Caterina Lacerra, Tommaso Pasini, Rocco Tripodi, Roberto Navigli

*Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence*, pages 3836-3842. DOI: [10.24963/ijcai.2021/528](https://doi.org/10.24963/ijcai.2021/528)

## Abstract

The lexical substitution task aims at finding suitable replacements for words in context. It has proved to be useful in several areas, such as word sense induction and text simplification, as well as in more practical applications such as writing-assistant tools.  However, the paucity of annotated data has forced researchers to apply mainly unsupervised approaches,  limiting the applicability of large pre-trained models and thus hampering the potential benefits of supervised approaches to the task. In this paper, we mitigate this issue by proposing ALaSca, a novel approach to automatically creating large-scale datasets for  English lexical substitution.  ALaSca allows examples to be produced for potentially any word in a language vocabulary and to cover most of the meanings it lists.  Thanks to this,  we can unleash the full potential of neural architectures and finetune them on the lexical substitution task. Indeed,  when using our data, a  transformer-based model performs substantially better than when using manually annotated data only. We release  ALaSca at  https://sapienzanlp.github.io/alasca/.