Neural Logic Machines

This is an important paper in the development of neural reasoning capabilities which should reduce the brittleness of purely symbolic approaches:  Neural Logic Machine

The potential reasoning capabilities, such as with regard to multi-step inference, as in problem solving and theorem proving, are most interesting, but there are important contemporary applications in machine learning and question answering.  I’ll just provide a few hightlights from the paper on the latter and some more points and references on the former below.

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Entailment-driven Extracting and Editing for Conversational Machine Reading

When I wrote Are Vitamins Subject to Sales Tax, I was addressing the process of translating knowledge expressed in formal documents, like laws, regulations, and contracts, into logic suitable for inference using the Linguist.

Recently, one of my favorite researchers working in natural language processing and reasoning, Luke Zettlemoyer, is among the authors of Entailment-driven Extracting and Editing for Conversational
Machine Reading
.  This is a very nice turn towards knowledge extraction and inference that improves on superficial reasoning by textual entailment (RTE).

I recommend this paper, which relates to BERT, which is among my current favorites in deep learning for NL/QA.  Here is an image from the paper, FYI:

Entailment-driven Extracting and Editing for Conversational Machine Reading