Deep Learning, Structure and Innate Priors | Abigail See
Earlier this month, I had the exciting opportunity to moderate a discussion between Professors Yann LeCun and Christopher Manning, titled “What innate priors should we build into the architecture of deep learning systems?” The event was a special installment of AI Salon, a discussion series held within the Stanford AI Lab that often features expert guests. This discussion topic – about the structu
Four deep learning trends from ACL 2017 | Abigail See
Click here for Part One. This is the second of a two-part post in which I describe four broad research trends that I observed at ACL 2017. In Part One I explored the shifting assumptions we make about language, both at the sentence and the word level, and how these shifts are prompting both a comeback of linguistic structure and a re-evaluation of word embeddings. In this part, I will discuss two
Four deep learning trends from ACL 2017 | Abigail See
This is a two-part post. Click here for Part Two. Click here to read a shortened version of this post translated to Chinese. Introduction “NLP is booming”, declared Joakim Nivre at the presidential address of ACL 2017, which I attended in Vancouver earlier this month. As evidenced by the throngs of attendees, interest in NLP is at an all-time high – an increase that is chiefly due to the successes
Taming Recurrent Neural Networks for Better Summarization | Abigail See
This blog post is about the ACL 2017 paper Get To The Point: Summarization with Pointer-Generator Networks by Abigail See, Peter J Liu, and Christopher Manning. [paper] [code] The internet age has brought unfathomably massive amounts of information to the fingertips of billions – if only we had time to read it. Though our lives have been transformed by ready access to limitless data, we also find
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