Language models (LMs) are the hottest topic in the NLP research right now. The most prominent examples are BERT and GPT-2 but new LMs are published every month trained on humongous volumes of text. Are LMs capable of encoding knowledge in a way similar to knowledge graphs? Petroni et al study this problem comparing language models with knowledge graphs on Question Answering and NLG tasks where factual knowledge is required, e.g., a question is posed by inserting a MASK token instead of an answer. Turns out LMs demonstrate similar to KGs performance on very simple questions such as "Adolphe Adam died in [Paris]" .

The Conference on Empirical Methods in Natural Language Processing (EMNLP) is one of the top natural language processing conferences in the world. In 2019, it is to be held in Hong Kong, China. There were 1,813 long paper submissions, of which 465 were accepted and 1,063 short paper submissions, of which 218 were accepted. A large number of these papers also published their code ( code download link). To help the community quickly catch up on the work presented in this conference, Paper Digest Team processed all accepted papers, and generated one highlight sentence (typically the main topic) for each paper.