This repository is for the paper UAlberta at SemEval-2023 Task 1: Context Augmentation and Translation for Multilingual Visual Word Sense Disambiguation. In Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023), pages 2043–2051, Toronto, Canada. Association for Computational Linguistics.
Use this script to disambiguate target words in context.
Installation:
- Install pytorch following instructions from the official website.
- Install dependencies
bash consec/setup.sh
- Download the ConSec "consec_wngt_best.ckpt" checkpoint from here
- Move "consec_wngt_best.ckpt" to consec/checkpoints
- Run disambigute.py in the following way
PYTHONPATH=$(PWD)/consec python3 disambiguate.py --data_path data/trial.data.txt --output consec.tsv
where --data_path is the SE23 dataset path and --output is the path where you would like to save the output
- Michael Ogezi - [email protected]
@inproceedings{ogezi-etal-2023-ualberta,
title = "{UA}lberta at {S}em{E}val-2023 Task 1: Context Augmentation and Translation for Multilingual Visual Word Sense Disambiguation",
author = "Ogezi, Michael and
Hauer, Bradley and
Omarov, Talgat and
Shi, Ning and
Kondrak, Grzegorz",
editor = {Ojha, Atul Kr. and
Do{\u{g}}ru{\"o}z, A. Seza and
Da San Martino, Giovanni and
Tayyar Madabushi, Harish and
Kumar, Ritesh and
Sartori, Elisa},
booktitle = "Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.semeval-1.281",
doi = "10.18653/v1/2023.semeval-1.281",
pages = "2043--2051",
abstract = "We describe the systems of the University of Alberta team for the SemEval-2023 Visual Word Sense Disambiguation (V-WSD) Task. We present a novel algorithm that leverages glosses retrieved from BabelNet, in combination with text and image encoders. Furthermore, we compare language-specific encoders against the application of English encoders to translated texts. As the contexts given in the task datasets are extremely short, we also experiment with augmenting these contexts with descriptions generated by a language model. This yields substantial improvements in accuracy. We describe and evaluate additional V-WSD methods which use image generation and text-conditioned image segmentation. Some of our experimental results exceed those of our official submissions on the test set. Our code is publicly available at \url{https://github.com/UAlberta-NLP/v-wsd}.",
}