We provide the first exploration of sentence embeddings from text-to-text transformers (T5).
Sentence embeddings are broadly useful for language processing tasks. While T5 achieves …

Adapting large-scale pretrained language models to downstream tasks via fine-tuning is the
standard method for achieving state-of-the-art performance on NLP benchmarks. However …

The recent" Text-to-Text Transfer Transformer"(T5) leveraged a unified text-to-text format and
scale to attain state-of-the-art results on a wide variety of English-language NLP tasks. In this …

In this paper, we present the joint contribution of Unbabel and IST to the WMT 2022 Metrics
Shared Task. Our primary submission–dubbed COMET-22–is an ensemble between a …

This paper presents the results of the WMT21 Metrics Shared Task. Participants were asked
to score the outputs of the translation systems competing in the WMT21 News Translation …

Recent trends in language modeling have focused on increasing performance through
scaling, and have resulted in an environment where training language models is out of …

We present the findings of SemEval-2022 Task 11 on Multilingual Complex Named Entity
Recognition MULTICONER. Divided into 13 tracks, the task focused on methods to identify …

Abstract iSarcasmEval is the first shared task to target intended sarcasm detection: the data
for this task was provided and labelled by the authors of the texts themselves. Such an …

Multilingual pre-trained models are known to suffer from the curse of multilinguality, which
causes per-language performance to drop as they cover more languages. We address this …

State-of-the-art models in natural language processing rely on separate rigid subword
tokenization algorithms, which limit their generalization ability and adaptation to new …