We present UDify, a multilingual multi-task model capable of accurately predicting universal
part-of-speech, morphological features, lemmas, and dependency trees simultaneously for …

Ancient languages preserve the cultures and histories of the past. However, their study is
fraught with difficulties, and experts must tackle a range of challenging text-based tasks, from …

We present Latin BERT, a contextual language model for the Latin language, trained on
642.7 million words from a variety of sources spanning the Classical era to the 21st century …

Abstract The 2019 Shared Task at the Conference for Computational Language Learning
(CoNLL) was devoted to Meaning Representation Parsing (MRP) across frameworks. Five …

Recent advances in NLP have led to the creation of powerful language models for many
languages including Ancient Greek and Latin. While prior work on Classical languages …

Language models based on the Transformer architecture have achieved state-of-the-art
performance on a wide range of NLP tasks such as text classification, question-answering …

Multilingual sequence labeling is a task of predicting label sequences using a single unified
model for multiple languages. Compared with relying on multiple monolingual models, using …

Multimodal emotion recognition of videos of human oration, commonly called opinion
videos, has a wide scope of applications across all domains. Here, the speakers express …

Sensitivity of deep-neural models to input noise is known to be a challenging problem. In
NLP, model performance often deteriorates with naturally occurring noise, such as spelling …

Having access to high-quality grammatical annotations is important for downstream tasks in
NLP as well as for corpus-based research. In this paper, we describe experiments with the …