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Are multimodal inputs necessary for grammar induction? Recent work has shownthat multimodal training inputs can improve grammar induction. However, theseimprovements are based on comparisons to weak text-only baselines that weretrained on relatively little textual data. To determine whether multimodalinputs are needed in regimes with large amounts of textual training data, wedesign a stronger text-only baseline, which we refer to as LC-PCFG. LC-PCFG isa C-PFCG that incorporates em-beddings from text-only large language models(LLMs). We use a fixed grammar family to directly compare LC-PCFG to variousmulti-modal grammar induction methods. We compare performance on four benchmarkdatasets. LC-PCFG provides an up to 17% relative improvement in Corpus-F1compared to state-of-the-art multimodal grammar induction methods. LC-PCFG isalso more computationally efficient, providing an up to 85% reduction inparameter count and 8.8x reduction in training time compared to multimodalapproaches. These results suggest that multimodal inputs may not be necessaryfor grammar induction, and emphasize the importance of strong vision-freebaselines for evaluating the benefit of multimodal approaches.

Re-evaluating the Need for Multimodal Signals in Unsupervised Grammar Induction