Text-to-Image Generation for Vocabulary Learning Using the Keyword Method

• URL: http://arxiv.org/abs/2501.17099v1
• Date: Tue, 28 Jan 2025 17:39:50 GMT
• Title: Text-to-Image Generation for Vocabulary Learning Using the Keyword Method
• Authors: Nuwan T. Attygalle, Matjaž Kljun, Aaron Quigley, Klen čOpič Pucihar, Jens Grubert, Verena Biener, Luis A. Leiva, Juri Yoneyama, Alice Toniolo, Angela Miguel, Hirokazu Kato, Maheshya Weerasinghe,
• Abstract summary: The 'keyword method' is an effective technique for learning vocabulary of a foreign language. It involves creating a memorable visual link between what a word means and what its pronunciation in a foreign language sounds like. We developed an application that combines the keyword method with text-to-image generators to externalise the memorable visual links into visuals.
• Score: 9.862827991755076
• License:
• Abstract: The 'keyword method' is an effective technique for learning vocabulary of a foreign language. It involves creating a memorable visual link between what a word means and what its pronunciation in a foreign language sounds like in the learner's native language. However, these memorable visual links remain implicit in the people's mind and are not easy to remember for a large set of words. To enhance the memorisation and recall of the vocabulary, we developed an application that combines the keyword method with text-to-image generators to externalise the memorable visual links into visuals. These visuals represent additional stimuli during the memorisation process. To explore the effectiveness of this approach we first run a pilot study to investigate how difficult it is to externalise the descriptions of mental visualisations of memorable links, by asking participants to write them down. We used these descriptions as prompts for text-to-image generator (DALL-E2) to convert them into images and asked participants to select their favourites. Next, we compared different text-to-image generators (DALL-E2, Midjourney, Stable and Latent Diffusion) to evaluate the perceived quality of the generated images by each. Despite heterogeneous results, participants mostly preferred images generated by DALL-E2, which was used also for the final study. In this study, we investigated whether providing such images enhances the retention of vocabulary being learned, compared to the keyword method only. Our results indicate that people did not encounter difficulties describing their visualisations of memorable links and that providing corresponding images significantly improves memory retention.

Related papers

• Visual Analytics for Efficient Image Exploration and User-Guided Image Captioning [35.47078178526536]
  Recent advancements in pre-trained large-scale language-image models have ushered in a new era of visual comprehension. This paper tackles two well-known issues within the realm of visual analytics: (1) the efficient exploration of large-scale image datasets and identification of potential data biases within them; (2) the evaluation of image captions and steering of their generation process.
  arXiv  Detail & Related papers  (2023-11-02T06:21:35Z)
• An Image is Worth Multiple Words: Discovering Object Level Concepts using Multi-Concept Prompt Learning [8.985668637331335]
  Textural Inversion learns a singular text embedding for a new "word" to represent image style and appearance. We introduce Multi-Concept Prompt Learning (MCPL), where multiple unknown "words" are simultaneously learned from a single sentence-image pair. Our approach emphasises learning solely from textual embeddings, using less than 10% of the storage space compared to others.
  arXiv  Detail & Related papers  (2023-10-18T19:18:19Z)
• Visually-Aware Context Modeling for News Image Captioning [54.31708859631821]
  News Image Captioning aims to create captions from news articles and images. We propose a face-naming module for learning better name embeddings. We use CLIP to retrieve sentences that are semantically close to the image.
  arXiv  Detail & Related papers  (2023-08-16T12:39:39Z)
• SmartPhone: Exploring Keyword Mnemonic with Auto-generated Verbal and Visual Cues [2.8047215329139976]
  We propose an end-to-end pipeline for auto-generating verbal and visual cues for keyword mnemonics. Our approach, an end-to-end pipeline for auto-generating verbal and visual cues, can automatically generate highly memorable cues.
  arXiv  Detail & Related papers  (2023-05-11T20:58:10Z)
• Universal Multimodal Representation for Language Understanding [110.98786673598015]
  This work presents new methods to employ visual information as assistant signals to general NLP tasks. For each sentence, we first retrieve a flexible number of images either from a light topic-image lookup table extracted over the existing sentence-image pairs. Then, the text and images are encoded by a Transformer encoder and convolutional neural network, respectively.
  arXiv  Detail & Related papers  (2023-01-09T13:54:11Z)
• Remember What You have drawn: Semantic Image Manipulation with Memory [84.74585786082388]
  We propose a memory-based Image Manipulation Network (MIM-Net) to generate realistic and text-conformed manipulated images. To learn a robust memory, we propose a novel randomized memory training loss. Experiments on the four popular datasets show the better performance of our method compared to the existing ones.
  arXiv  Detail & Related papers  (2021-07-27T03:41:59Z)
• Align before Fuse: Vision and Language Representation Learning with Momentum Distillation [52.40490994871753]
  We introduce a contrastive loss to representations BEfore Fusing (ALBEF) through cross-modal attention. We propose momentum distillation, a self-training method which learns from pseudo-targets produced by a momentum model. ALBEF achieves state-of-the-art performance on multiple downstream vision-language tasks.
  arXiv  Detail & Related papers  (2021-07-16T00:19:22Z)
• Learning to Represent Image and Text with Denotation Graph [32.417311523031195]
  We propose learning representations from a set of implied, visually grounded expressions between image and text. We show that state-of-the-art multimodal learning models can be further improved by leveraging automatically harvested structural relations.
  arXiv  Detail & Related papers  (2020-10-06T18:00:58Z)
• On Vocabulary Reliance in Scene Text Recognition [79.21737876442253]
  Methods perform well on images with words within vocabulary but generalize poorly to images with words outside vocabulary. We call this phenomenon "vocabulary reliance" We propose a simple yet effective mutual learning strategy to allow models of two families to learn collaboratively.
  arXiv  Detail & Related papers  (2020-05-08T11:16:58Z)
• Learning Representations by Predicting Bags of Visual Words [55.332200948110895]
  Self-supervised representation learning targets to learn convnet-based image representations from unlabeled data. Inspired by the success of NLP methods in this area, in this work we propose a self-supervised approach based on spatially dense image descriptions.
  arXiv  Detail & Related papers  (2020-02-27T16:45:25Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.