Large, pre-trained language models (PLMs) such as BERT and GPT have drastically
changed the Natural Language Processing (NLP) field. For numerous NLP tasks …

This survey article proposes a clearer view of Natural Language Reasoning (NLR) in the
field of Natural Language Processing (NLP), both conceptually and practically …

How to efficiently serve ever-larger trained natural language models in practice has become
exceptionally challenging even for powerful cloud servers due to their prohibitive …

Recent work in natural language processing (NLP) has yielded appealing results from
scaling model parameters and training data; however, using only scale to improve …

Recent work has exhibited the surprising cross-lingual abilities of multilingual BERT (M-
BERT)--surprising since it is trained without any cross-lingual objective and with no aligned …

Entity matching (EM) finds data instances that refer to the same real-world entity. In this
paper we examine applying deep learning (DL) to EM, to understand DL's benefits and …

Beginning with McCarthy's Advice Taker (1959), AI has pursued the goal of providing a
system with explicit, general knowledge and having the system reason over that knowledge …

Remarkable progress has been made on automated reasoning with natural text, by using
Language Models (LMs) and methods such as Chain-of-Thought and Selection-Inference …

Our goal, in the context of open-domain textual question-answering (QA), is to explain
answers by showing the line of reasoning from what is known to the answer, rather than …

We propose a hypothesis only baseline for diagnosing Natural Language Inference (NLI).
Especially when an NLI dataset assumes inference is occurring based purely on the …