Language model pretraining has led to significant performance gains but careful comparison
between different approaches is challenging. Training is computationally expensive, often …

This paper shows that pretraining multilingual language models at scale leads to significant
performance gains for a wide range of cross-lingual transfer tasks. We train a Transformer-…

This paper discusses the fourth year of the ``Sentiment Analysis in Twitter Task''. SemEval-2016
Task 4 comprises five subtasks, three of which represent a significant departure from …

State-of-the-art natural language processing systems rely on supervision in the form of
annotated data to learn competent models. These models are generally trained on data in a …

The advent of large language models trained on code (code LLMs) has led to significant
progress in language-to-code generation. State-of-the-art approaches in this area combine LLM …

A large array of pretrained models are available to the biomedical NLP (BioNLP) community.
Finding the best model for a particular task can be difficult and time-consuming. For many …

We study the problem of multilingual masked language modeling, ie the training of a single
model on concatenated text from multiple languages, and present a detailed study of several …

We aim to shed light on the state-of-the-art in NP coreference resolution by teasing apart the
differences in the MUC and ACE task definitions, the assumptions made in evaluation …

Graphical models are often used “inappropriately,” with approximations in the topology,
inference, and prediction. Yet it is still common to train their parameters to approximately …

Within the area of general-purpose finegrained subjectivity analysis, opinion topic identification
has, to date, received little attention due to both the difficulty of the task and the lack of …