| Challenge: | a visually grounded neural syntax learner is an approach for learning syntactic representations without any supervision. |
| Approach: | They propose a visually grounded neural syntax learner that acquires syntax by looking at images and reading captions. |
| Outcome: | The proposed model outperforms unsupervised approaches on the MSCOCO data set . it is more stable with choice of initialization and amount of training data, the authors show . |
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Visually Grounded Compound PCFGs (2020.emnlp-main)
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| Challenge: | Existing work on visual groundings for language understanding has been drawing much attention. |
| Approach: | They propose to use an extension of probabilistic context-free grammar model to do fully-differentiable end-to-end visually grounded learning. |
| Outcome: | The proposed model outperforms the previous grounded model and significantly outperformed the previous model on the MSCOCO test captions. |
What is Learned in Visually Grounded Neural Syntax Acquisition (2020.acl-main)
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| Challenge: | Visual features are promising for learning bootstrap textual models, but blackbox learning models make it difficult to isolate the specific contribution of visual components. |
| Approach: | They propose to use alignments between phrases and images as a learning signal for syntax acquisition. |
| Outcome: | The proposed model performs better than the previous model, but it is significantly less expressive. |
Grounded PCFG Induction with Images (2020.aacl-main)
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| Challenge: | Recent work in unsupervised parsing has tried to incorporate visual information into learning, but results suggest that these models need linguistic bias to compete against models that only rely on text. |
| Approach: | They propose to use visual information from images for labeled parsing and compare them to existing models which only use text. |
| Outcome: | The proposed models achieve state-of-the-art results on multilingual induction datasets even without help from linguistic knowledge or pretrained image encoders. |
On the Transferability of Visually Grounded PCFGs (2023.findings-emnlp)
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| Challenge: | Existing studies on visually grounded grammar induction have not evaluated text domains that are different from the training domain. |
| Approach: | They extend visually grounded grammar induction model to transfer across text domains . they find that benefits transfer to text in a domain similar to the training domain . |
| Outcome: | The proposed model can transfer across text domains but fails to transfer to remote domains. |
The Limitations of Limited Context for Constituency Parsing (2021.acl-long)
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| Challenge: | a language model that is syntax-aware can produce better samples, authors say . a recent study shows that neural approaches to syntax can perform unsupervised syntactic parsing . |
| Approach: | They propose to incorporate syntax into neural approaches in NLP to produce better samples . they find that the first time neural approaches were able to perform unsupervised syntactic parsing . |
| Outcome: | The proposed models can perform unsupervised syntactic parsing, but they are lagging behind . the proposed models are based on a sandbox of probabilistic context-free-grammars . |
VLURes: Benchmarking Long-Text Grounding and Cross-Lingual Robustness in Vision Language Models (2026.findings-acl)
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| Challenge: | ***VLURes** provides a practical testbed for long-text grounding and multilingual robustness in web-realistic agent settings. |
| Approach: | They propose a multilingual benchmark for evaluating vision-language models under long-text grounding. |
| Outcome: | ***VLURes** provides a testbed for long-text grounding and multilingual robustness in web-realistic agent settings. |
iParaphrasing: Extracting Visually Grounded Paraphrases via an Image (C18-1)
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| Challenge: | iParaphrasing extracts visually grounded paraphrases, which are different phrasal expressions describing the same visual concept in an image. |
| Approach: | They propose a task to extract visually grounded paraphrases from images . they propose to model the similarity between the extracted VGPs using existing methods . |
| Outcome: | The proposed task extracts visually grounded paraphrases from images . the proposed method has the potential to improve multimodal language and image tasks . |
Lexicon-Level Contrastive Visual-Grounding Improves Language Modeling (2024.findings-acl)
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| Challenge: | Neural language models (LMs) are trained on orders of magnitude more language data than human language learners receive, but without supervision from other sensory modalities that play a crucial role in human learning. |
| Approach: | They propose a grounded language learning procedure that leverages visual supervision to improve textual representations. |
| Outcome: | The proposed procedure outperforms standard language-only models in terms of learning efficiency in small and developmentally plausible data regimes and improves perplexity by around 5% on multiple language modeling tasks compared to other models trained on the same amount of text data. |
Visual Grounding Helps Learn Word Meanings in Low-Data Regimes (2024.naacl-long)
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| Challenge: | Modern neural language models (LMs) require distinctly un-human-like ways to achieve these results. |
| Approach: | They train a diverse set of LM architectures with and without auxiliary visual supervision on datasets of varying scales. |
| Outcome: | The proposed models exhibit better learning of syntactic categories, lexical relations, semantic features, word similarity and alignment with human neural representations. |
Textual Supervision for Visually Grounded Spoken Language Understanding (2020.findings-emnlp)
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| Challenge: | a new approach to spoken language understanding extracts semantic information directly from speech without relying on transcriptions. |
| Approach: | They propose to use textual supervision to train visually-grounded models of spoken language understanding without relying on transcriptions. |
| Outcome: | The proposed model improves when enough text is available, the study shows . compared with pipeline-based models, the pipeline approach performs better when enough data is available . |