@inproceedings{li-etal-2024-evaluating,
title = "Re-evaluating the Need for Visual Signals in Unsupervised Grammar Induction",
author = "Li, Boyi and
Corona, Rodolfo and
Mangalam, Karttikeya and
Chen, Catherine and
Flaherty, Daniel and
Belongie, Serge and
Weinberger, Kilian and
Malik, Jitendra and
Darrell, Trevor and
Klein, Dan",
editor = "Duh, Kevin and
Gomez, Helena and
Bethard, Steven",
booktitle = "Findings of the Association for Computational Linguistics: NAACL 2024",
month = jun,
year = "2024",
address = "Mexico City, Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.findings-naacl.70",
pages = "1113--1123",
abstract = "Are multimodal inputs necessary for grammar induction? Recent work has shown that multimodal training inputs can improve grammar induction. However, these improvements are based on comparisons to weak text-only baselines that were trained on relatively little textual data. To determine whether multimodal inputs are needed in regimes with large amounts of textual training data, we design a stronger text-only baseline, which we refer to as LC-PCFG. LC-PCFG is a C-PFCG that incorporates embeddings from text-only large language models (LLMs). We use a fixed grammar family to directly compare LC-PCFG to various multimodal grammar induction methods. We compare performance on four benchmark datasets. LC-PCFG provides an up to 17{\%} relative improvement in Corpus-F1 compared to state-of-the-art multimodal grammar induction methods. LC-PCFG is also more computationally efficient, providing an up to 85{\%} reduction in parameter count and $8.8\times$ reduction in training time compared to multimodal approaches. These results suggest that multimodal inputs may not be necessary for grammar induction, and emphasize the importance of strong vision-free baselines for evaluating the benefit of multimodal approaches.",
}
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<abstract>Are multimodal inputs necessary for grammar induction? Recent work has shown that multimodal training inputs can improve grammar induction. However, these improvements are based on comparisons to weak text-only baselines that were trained on relatively little textual data. To determine whether multimodal inputs are needed in regimes with large amounts of textual training data, we design a stronger text-only baseline, which we refer to as LC-PCFG. LC-PCFG is a C-PFCG that incorporates embeddings from text-only large language models (LLMs). We use a fixed grammar family to directly compare LC-PCFG to various multimodal grammar induction methods. We compare performance on four benchmark datasets. LC-PCFG provides an up to 17% relative improvement in Corpus-F1 compared to state-of-the-art multimodal grammar induction methods. LC-PCFG is also more computationally efficient, providing an up to 85% reduction in parameter count and 8.8\times reduction in training time compared to multimodal approaches. These results suggest that multimodal inputs may not be necessary for grammar induction, and emphasize the importance of strong vision-free baselines for evaluating the benefit of multimodal approaches.</abstract>
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%0 Conference Proceedings
%T Re-evaluating the Need for Visual Signals in Unsupervised Grammar Induction
%A Li, Boyi
%A Corona, Rodolfo
%A Mangalam, Karttikeya
%A Chen, Catherine
%A Flaherty, Daniel
%A Belongie, Serge
%A Weinberger, Kilian
%A Malik, Jitendra
%A Darrell, Trevor
%A Klein, Dan
%Y Duh, Kevin
%Y Gomez, Helena
%Y Bethard, Steven
%S Findings of the Association for Computational Linguistics: NAACL 2024
%D 2024
%8 June
%I Association for Computational Linguistics
%C Mexico City, Mexico
%F li-etal-2024-evaluating
%X Are multimodal inputs necessary for grammar induction? Recent work has shown that multimodal training inputs can improve grammar induction. However, these improvements are based on comparisons to weak text-only baselines that were trained on relatively little textual data. To determine whether multimodal inputs are needed in regimes with large amounts of textual training data, we design a stronger text-only baseline, which we refer to as LC-PCFG. LC-PCFG is a C-PFCG that incorporates embeddings from text-only large language models (LLMs). We use a fixed grammar family to directly compare LC-PCFG to various multimodal grammar induction methods. We compare performance on four benchmark datasets. LC-PCFG provides an up to 17% relative improvement in Corpus-F1 compared to state-of-the-art multimodal grammar induction methods. LC-PCFG is also more computationally efficient, providing an up to 85% reduction in parameter count and 8.8\times reduction in training time compared to multimodal approaches. These results suggest that multimodal inputs may not be necessary for grammar induction, and emphasize the importance of strong vision-free baselines for evaluating the benefit of multimodal approaches.
%U https://aclanthology.org/2024.findings-naacl.70
%P 1113-1123
Markdown (Informal)
[Re-evaluating the Need for Visual Signals in Unsupervised Grammar Induction](https://aclanthology.org/2024.findings-naacl.70) (Li et al., Findings 2024)
ACL
- Boyi Li, Rodolfo Corona, Karttikeya Mangalam, Catherine Chen, Daniel Flaherty, Serge Belongie, Kilian Weinberger, Jitendra Malik, Trevor Darrell, and Dan Klein. 2024. Re-evaluating the Need for Visual Signals in Unsupervised Grammar Induction. In Findings of the Association for Computational Linguistics: NAACL 2024, pages 1113–1123, Mexico City, Mexico. Association for Computational Linguistics.