What are the units of text that we want to model? From bytes to multi-word expressions, text
can be analyzed and generated at many granularities. Until recently, most natural language …

Byte-pair encoding (BPE) is widely used in NLP for performing subword tokenization. It
uncovers redundant patterns for compressing the data, and hence alleviates the sparsity …

We introduce FLOTA (Few Longest Token Approximation), a simple yet effective method to
improve the tokenization of pretrained language models (PLMs). FLOTA uses the …

More than 43% of the languages spoken in the world are endangered, and language loss
currently occurs at an accelerated rate because of globalization and neocolonialism. Saving …

We introduce DivEMT, the first publicly available post-editing study of Neural Machine
Translation (NMT) over a typologically diverse set of target languages. Using a strictly …

Text Classification methods have been improving at an unparalleled speed in the last
decade thanks to the success brought about by deep learning. Historically, state-of-the-art …

This paper presents DeepSPIN's submissions to the SIGMORPHON 2022 Shared Task on
Morpheme Segmentation. We make three submissions, all to the word-level subtask. First …

Theoretical work in morphological typology offers the possibility of measuring morphological
diversity on a continuous scale. However, literature in Natural Language Processing (NLP) …

The complex and specialized terminology of financial language in Portuguese-speaking
markets create significant challenges for natural language processing (NLP) applications …

Pre-trained multilingual language models (eg, mBERT, XLM-RoBERTa) have significantly
advanced the state-of-the-art for zero-shot cross-lingual information extraction. These …