Papers with Code 아카이브

Multimodal Text Prediction

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하위 태스크: 0
평가표: 1
보존된 평가 기록: 1

Multimodal text prediction is a type of natural language processing that involves predicting the next word or sequence of words in a sentence, given multiple modalities or types of input. In traditional text prediction, the prediction is based solely on the context of the sentence, such as the words that precede the target word. In multimodal text prediction, additional modalities, such as images, audio, or user behavior, are also used to inform the prediction.

For example, in a multimodal text prediction system for captioning images, the system may use both the content of the image and the words that have been typed so far to generate the next word in the caption. The image may provide additional context or information about the content of the caption, while the typed words may provide information about the style or tone of the caption.

Multimodal text prediction can be achieved using a variety of techniques, including deep learning models and statistical models. These models can be trained on large datasets of text and multimodal inputs to learn the relationships between the different types of data and improve the accuracy of the predictions.

Multimodal text prediction has many applications, including chatbots, virtual assistants, and predictive text input for mobile devices. By incorporating additional modalities into the prediction process, multimodal text prediction systems can provide more accurate and useful predictions, improving the overall user experience.

데이터셋

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SciGraphQA

SciGraphQA is a large-scale, open-domain dataset focused on generating multi-turn conversational question-answering dialogues centered around understanding and describing scientific graphs and figures. It contains over 300,000 samples derived from academic research papers in computer science and machine learning domains. Each sample in ScFiGraphQA consists of a scientific graph image sourced from papers on ArXiv, accompanied by rich textual context including the paper's title, abstract, figure caption, and a paragraph from the paper referencing the figure. Using this comprehensive context, the dataset employs a to produce multi-turn question-answer dialogues aimed at explaining the given graph in an interactive, conversational format. On average, each sample contains 2-3 turns of question-answer exchange. The key motivation behind SciGraphQA is providing a large-scale resource to support research and development of multi-modal AI systems that can engage in informative, open-ended conversations about graphs and data visualizations. The multi-turn dialogue format presents a more natural and interactive setting compared to standard visual question answering datasets that use fixed sets of standalone questions. Potential use cases of SciGraphQA include pre-training and benchmarking multi-modal conversational models for scientific graph comprehension, building AI assistants that can discuss data insights, and developing aids to help individuals understand complex figures and diagrams interactively. The academic source material also provides a way to evaluate model capabilities on expert-level graphs spanning diverse topics and complex visual encodings.

MultiSubs

MultiSubs is a dataset of multilingual subtitles gathered from the OPUS OpenSubtitles dataset, which in turn was sourced from opensubtitles.org. We have supplemented some text fragments (visually salient nouns in this release) within the subtitles with web images, where the word sense of the fragment has been disambiguated using a cross-lingual approach. We have introduced a fill-in-the-blank task and a lexical translation task to demonstrate the utility of the dataset. Please refer to our paper for a more detailed description of the dataset and tasks. Multisubs will benefit research on visual grounding of words especially in the context of free-form sentence. Josiah Wang, Pranava Madhyastha, Josiel Figueiredo, Chiraag Lala, Lucia Specia (2021). MultiSubs: A Large-scale Multimodal and Multilingual Dataset. CoRR, abs/2103.01910. Available at: [https://arxiv.org/abs/2103.01910] (https://arxiv.org/abs/2103.01910)

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.