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Found 5 papers, 5 papers with code
Grounding Spatial Relations in Text-Only Language Models
This paper shows that text-only Language Models (LM) can learn to ground spatial relations like "left of" or "below" if they are provided with explicit location information of objects and they are properly trained to leverage those locations.
Improving Explicit Spatial Relationships in Text-to-Image Generation through an Automatically Derived Dataset
We hypothesize that this is because explicit spatial relations rarely appear in the image captions used to train these models.
IXA/Cogcomp at SemEval-2023 Task 2: Context-enriched Multilingual Named Entity Recognition using Knowledge Bases
Named Entity Recognition (NER) is a core natural language processing task in which pre-trained language models have shown remarkable performance.
Multilingual Named Entity Recognition
named-entity-recognition
+4
Image Captioning for Effective Use of Language Models in Knowledge-Based Visual Question Answering
Our results on a visual question answering task which requires external knowledge (OK-VQA) show that our text-only model outperforms pretrained multimodal (image-text) models of comparable number of parameters.
Evaluating Multimodal Representations on Visual Semantic Textual Similarity
In the case of textual representations, inference tasks such as Textual Entailment and Semantic Textual Similarity have been often used to benchmark the quality of textual representations.
