ERASER: A Benchmark to Evaluate Rationalized NLP Models

Nazneen Rajani · #research

Many NLP applications today deploy state-of-the-art deep neural networks that are essentially black-boxes. One of the goals of Explainable AI (XAI) is to have AI models reveal why and how they make their predictions so that these predictions are interpretable by a human. But work in this direction has been

The Natural Language Decathlon

Bryan McCann · #research

Deep learning has significantly improved state-of-the-art performance for natural language processing tasks like machine translation, summarization, question answering, and text classification.

Interpretable Counting for Visual Question Answering

Alex Trott · #research

Learning to answer open-ended questions about images, a task known as visual question answering (VQA), has received much attention over the last several years. VQA has been put forth as a benchmark for complete scene understanding and flexible reasoning, two fundamental goals of AI.

Improving end-to-end Speech Recognition Models

Yingbo Zhou · #research

Speech recognition has been successfully depolyed on various smart devices, and is changing the way we interact with them. Traditional phonetic-based recognition approaches require training of separate components such as pronouciation, acoustic and language model.

How to Talk to Your Database

Victor Zhong · #research

A vast amount of today’s information is stored in relational databases. These databases provide the foundation of systems such as medical records, financial markets, and electronic commerce.