Natural language processing (NLP) has recently gained much attention for representing and
analyzing human language computationally. It has spread its applications in various fields …

Automatic Question Answering (QA) has been successfully applied in various domains such
as search engines and chatbots. Biomedical QA (BQA), as an emerging QA task, enables …

As humans, we navigate a multimodal world, building a holistic understanding from all our
senses. We introduce MERLOT Reserve, a model that represents videos jointly over time …

While task-specific finetuning of pretrained networks has led to significant empirical
advances in NLP, the large size of networks makes finetuning difficult to deploy in multi-task …

Motivation Biomedical text mining is becoming increasingly important as the number of
biomedical documents rapidly grows. With the progress in natural language processing …

The classic supervised machine learning paradigm is based on learning in isolation, a
single predictive model for a task using a single dataset. This approach requires a large …

Open Domain Question Answering (QA) is evolving from complex pipelined systems to end-
to-end deep neural networks. Specialized neural models have been developed for …

Contextual embeddings, such as ELMo and BERT, move beyond global word
representations like Word2Vec and achieve ground-breaking performance on a wide range …

The fine-tuning of pre-trained language models has a great success in many NLP fields. Yet,
it is strikingly vulnerable to adversarial examples, eg, word substitution attacks using only …

Contextualized word embeddings derived from pre-trained language models (LMs) show
significant improvements on downstream NLP tasks. Pre-training on domain-specific …