NLP News Cypher

As you may already have experienced it, your next NLP project may require you to work with knowledge-intensive tasks such as open-domain question answering or fact-checking. Benchmarking these knowledge intensive tasks can be difficult because these tasks require a huge knowledge source to feed off of (and things can get even harder when you have various knowledge sources to work with). As a result, a new benchmark from Facebook AI gives researchers a centralized baseline to start their research and benchmark model performance for these tough tasks, and it's called KILT. It leverages an interface across tasks that are grounded on a single knowledge source: the 2019/08/01 Wikipedia snapshot containing 5.9M articles. Here are the tasks you'll work with in KILT: fact checking, open-domain question answering, slot filling, entity linking, and dialogue.