Over the last couple of decades, community question‐answering sites (CQAs) have been a
topic of much academic interest. Scholars have often leveraged traditional machine learning …

Text embeddings are commonly evaluated on a small set of datasets from a single task not
covering their possible applications to other tasks. It is unclear whether state-of-the-art …

We present a holistic approach to building a robust and useful natural language
classification system for real-world content moderation. The success of such a system relies …

Deep neural networks excel at learning from labeled data and achieve state-of-the-art
resultson a wide array of Natural Language Processing tasks. In contrast, learning from …

There are two approaches for pairwise sentence scoring: Cross-encoders, which perform full-
attention over the input pair, and Bi-encoders, which map each input independently to a …

Unsupervised domain adaptive video action recognition aims to recognize actions of a
target domain using a model trained with only out-of-domain (source) annotations. The …

Gorman and Bedrick (2019) argued for using random splits rather than standard splits in
NLP experiments. We argue that random splits, like standard splits, lead to overly optimistic …

Hybrid quantum-classical classifiers promise to positively impact critical aspects of natural
language processing tasks, particularly classification-related ones. Among the possibilities …

In this work we explore Unsupervised Domain Adaptation (UDA) of pretrained language
models for downstream tasks. We introduce UDALM, a fine-tuning procedure, using a mixed …

Task-oriented dialog systems increasingly rely on deep learning-based slot filling models,
usually needing extensive labeled training data for target domains. Often, however, little to …