<div dir="ltr">A gentle reminder that the talk would be tomorrow (Tuesday) noon.</div><div class="gmail_extra"><br><div class="gmail_quote">On Sat, Sep 16, 2017 at 9:00 AM,  <span dir="ltr"><<a href="mailto:ai-seminar-announce-request@cs.cmu.edu" target="_blank">ai-seminar-announce-request@cs.cmu.edu</a>></span> wrote:<br><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex">Send ai-seminar-announce mailing list submissions to<br>
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Today's Topics:<br>
<br>
   1.  AI Seminar sponsored by Apple -- Bhuwan Dhingra --       September<br>
      19 (Adams Wei Yu)<br>
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Message: 1<br>
Date: Sat, 16 Sep 2017 02:32:21 -0700<br>
From: Adams Wei Yu <<a href="mailto:weiyu@cs.cmu.edu">weiyu@cs.cmu.edu</a>><br>
To: <a href="mailto:ai-seminar-announce@cs.cmu.edu">ai-seminar-announce@cs.cmu.edu</a><br>
Cc: Bhuwan Dhingra <<a href="mailto:bdhingra@andrew.cmu.edu">bdhingra@andrew.cmu.edu</a>><br>
Subject: [AI Seminar] AI Seminar sponsored by Apple -- Bhuwan Dhingra<br>
        --      September 19<br>
Message-ID:<br>
        <<a href="mailto:CABzq7eqJ28iuidzxr-4ZtZgLdqExDu1Azpjpxzpgc0%2BE6YVbbw@mail.gmail.com">CABzq7eqJ28iuidzxr-<wbr>4ZtZgLdqExDu1Azpjpxzpgc0+<wbr>E6YVbbw@mail.gmail.com</a>><br>
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<br>
Dear faculty and students,<br>
<br>
We look forward to seeing you next Tuesday, September 19, at noon in NSH<br>
3305 for AI Seminar sponsored by Apple. To learn more about the seminar<br>
series, please visit the AI Seminar webpage<br>
<<a href="http://www.cs.cmu.edu/~aiseminar/" rel="noreferrer" target="_blank">http://www.cs.cmu.edu/~<wbr>aiseminar/</a>>.<br>
<br>
On Tuesday, Bhuwan Dhingra <<a href="http://www.cs.cmu.edu/~bdhingra/" rel="noreferrer" target="_blank">http://www.cs.cmu.edu/~<wbr>bdhingra/</a>> will give the<br>
following talk:<br>
<br>
Title: Neural architectures for reading and reasoning over documents<br>
<br>
Abstract: Reading and understanding natural language text is important for<br>
AI applications which need to extract information from unstructured<br>
sources. Models designed for this task must deal with complex linguistic<br>
phenomena such as paraphrasing, co-reference, logical entailment, syntactic<br>
and semantic dependencies, and so on. In this talk I will show how<br>
architectural biases, motivated from such phenomena, can be built into<br>
neural network models to boost machine reading performance.<br>
<br>
The first half of the talk will focus on the Gated-Attention (GA) Reader<br>
model for learning fine-grained alignments between natural language queries<br>
and documents. The output of this model is a query-focused representation<br>
of the tokens in the document, which is used to extract the answer to the<br>
query. The second half of the talk will focus on extensions which utilize<br>
prior knowledge in the form of linguistic annotations to model long term<br>
dependencies in the document. Modeling long-term dependencies is the first<br>
step towards the more ambitious goal of reasoning over distinct parts of a<br>
document. Finally, I will discuss some of the key directions for future<br>
research, in terms of both improving the models and utilizing them for<br>
concrete applications.<br>
<br>
This is joint work with Zhilin Yang, Hanxiao Liu, Russ Salakhutdinov and<br>
William Cohen.<br>
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End of ai-seminar-announce Digest, Vol 76, Issue 4<br>
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