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.

Hey welcome back! It’s a brand new week. And if you are unaware, we tapped into the gold mine of arXiv’s new code link feature (mentioned in last week’s newsletter). Recently, arXiv collaborated with…

Loads of research came out this week! But FYI, we couldn't fit every story in this newsletter for space-saving reasons, so if you want complete coverage, follow our twitter, and as always, if you enjoy the read, please give it a and share with your enemies. And….yesterday, another update was made to the Super Duper NLP Repo and the Big Bad NLP Database: we added 10 datasets and 5 new notebooks. Highlights include the DialogRE dataset which may be the first human-annotated dialogue-based relation extraction dataset. Legend has it there's a bitcoin wallet worth $690 million that hackers have been attempting to crack for the past 2 years according to cybersecurity expert Alon Gal.

Includes colab notebooks in part 2 that goes over message-passing GCN's. Probably the most used type of graph out there.

DeLighT transformer library gives us a new look at the most popular model in NLP -- the transformer. The new architecture helps reduce parameter size in addition to making models deeper. Which means this new architecture can match or achieve better results with the traditional transformer architecture but is much lighter. As of right now, the architecture can help with language modeling and machine translation. According to the authors, more tasks are on the way.

Visit Nox's YouTube channel if you are interested in understanding how they cracked previous Cicada puzzles ante-Liber Primus.

Let's get our James Bond swag on shall we? Defense departments worldwide are betting on AI to deliver the next generation advanced military technology, and the US is no different. In the US of A, this strategy is being orchestrated by the Joint Artificial Intelligence Center (JAIC), a department under the umbrella of the Department of Defense (DoD) led by Acting Director Nand Mulchandani. And he recently gave his first press conference. NLP will play a bigger role in the future of JAIC strategy .

Awesome new GitHub that comes with a full stack backend (fastAPI) and front-end (Streamlit). You can serve this framework out the box and it's a good choice to begin with if you want to venture out of the flask WSGI framework and get more into the ASGI side of things. When discussing conversational AI, RASA Co-Founder Alan Nichol discusses the 5 levels of chatbot tech that we all should be striving for, especially for the holy grail: Level 5 aka HAL 9000. The 5 levels of conversational AI are observed both via the end-user and the developer perspective. Interesting read to get a road map of where RASA thinks chatbots are heading and what is required to get HAL on Elon's Starship.