[CL+NLP Lunch] [SHORT NOICE] CL+NLP lunch, Oct.26 Monday

[waleed ammar]

*Clarification: this talk will be held* *11am on Monday (CL+NLP
lunch),* *followed
by Leonid's talk at noon (ML lunch). We get to have lunch twice this
Monday.*

On Fri, Oct 23, 2015 at 4:30 PM, Kazuya Kawakami <kkawakam at andrew.cmu.edu>
wrote:

> Hi All
>
> Please join us for the next CL+NLP lunch at noon on Monday Oct 26th,
> where Jiang Guo will be speaking about Cross-lingual Transfer Parsing.
> Lunch will be provided!
>
> To arrange meetings with Jiang, please see the following document.
> (
> https://docs.google.com/document/d/1i4s181AWQGY1SJup76BZGgsjpquAH2ZrVe7tHWISPYc/edit
> )
>
>
> -----------------------------------------
> ML+NLP lunch
> Monday, Oct 26th at 11:00am
> GHC 7101
>
> Speaker: Jiang Guo, Johns Hopkins University
>
> [TITLE]
> Representation Learning for Cross-lingual Transfer Parsing
>
> [ABSTRACT]
> Cross-lingual model transfer has been a promising approach for inducing
> dependency parsers for low-resource languages where annotated treebanks are
> not available. The major obstacles for the model transfer approach are
> two-fold:
>
> 1. Lexical features are not directly transferable across languages.
>
> 2. Target language-specific syntactic structures are difficult to be
> recovered.
>
> In this talk, I will provide a representation learning framework to
> address these challenges. By evaluating on the Google universal dependency
> treebanks (v2.0), our best models yield an absolute improvement of 6.53% in
> averaged labeled attachment score, as compared with delexicalized
> multi-source transfer models. We also significantly outperform the
> state-of-the-art transfer system proposed most recently.
>
>
> [SHORT BIO]
> Jiang Guo is a joint Ph.D student at Johns Hopkins University and at
> Harbin Institute of Technology. His research interests are in the areas of
> natural language processing, machine learning, with special interests on
> distributed representation learning and its applications on NLP tasks
> (mostly structure prediction problems). His long-term goal is developing
> efficient and effective algorithms and softwares for NLP and machine
> learning applications.
>
> -----------------------------------------
>
>
> Up comming talk will be on 17th Nov. 12:00-13:00 by Ndapa Nakashole.
>
> Best regards,
> Kazuya
>



-- 
Waleed Ammar
Carnegie Mellon University
http://www.cs.cmu.edu/~wammar/
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