We introduce FLOTA (Few Longest Token Approximation), a simple yet effective method to improve the tokenization of pretrained language models (PLMs).
Building on current work on multilingual hate speech (e. g., Ousidhoum et al. (2019)) and hate speech reduction (e. g., Sap et al. (2020)), we present XTREMESPEECH, a new hate speech dataset containing 20, 297 social media passages from Brazil, Germany, India and Kenya.
A cascade of tasks are required to automatically generate an abstractive summary of the typical information-rich radiology report.
Interleaved texts, where posts belonging to different threads occur in a sequence, commonly occur in online chat posts, so that it can be time-consuming to quickly obtain an overview of the discussions.
We introduce the first generic text representation model that is completely nonsymbolic, i. e., it does not require the availability of a segmentation or tokenization method that attempts to identify words or other symbolic units in text.
A key characteristic of work on deep learning and neural networks in general is that it relies on representations of the input that support generalization, robust inference, domain adaptation and other desirable functionalities.