[AI Seminar] ai-seminar-announce Digest, Vol 76, Issue 4

[Adams Wei Yu]

A gentle reminder that the talk would be tomorrow (Tuesday) noon.

On Sat, Sep 16, 2017 at 9:00 AM, <ai-seminar-announce-request at cs.cmu.edu>
wrote:

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