Universal Aesthetics (Multimodal Focus)

Methods

Data

As for the convergence of language models, we need both plain texts and aesthetic texts. For simplicity, we reuse this text-image dataset, which is also used in Huh et al.'s paper, and then add another poem dataset.

Peoms

For poems, we use the Poems dataset from Kaggle. We find this dataset ideal for this project because of the following reasons:

• As the plain-text dataset contains 1,024 entries, it provides enough poems to yield a substantial amount of data.
• It categorizes the poems into 135 types based on their form (haiku, sonnet, etc.), which could facilitate our further studies.

However, this dataset still needs to be cleaned before usage. We identify two problems with the raw dataset. First, some poems contain copyright notices at the end, which introduce noise into subsequent processing. However, because the copyright information is clearly marked with a special mark ©️, it can be easily removed through rule-based filtering. Second, although most poems are in English, a small portion is not. Since the plain-text dataset contains exclusively English texts, we should also remove the non-English poems from this dataset.

Afterward is an unknown term in future
Before that we face the present,
Coming at well future depends on present;
Dismissing hazardous future
Endeavor best early at present.
Copyright © Muzahidul Reza | 29 November,2017

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