<div dir="ltr"><span id="docs-internal-guid-c3945870-5cbb-cc98-1e4e-75acc5455e82"><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:3pt;text-align:center"><span style="font-size:34.6667px;font-family:Arial;color:rgb(0,0,0);vertical-align:baseline;white-space:pre-wrap;background-color:transparent">1st Workshop on Representation Learning for NLP: Call for Papers</span></p><br><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span style="font-size:14.6667px;font-family:Arial;color:rgb(0,0,0);vertical-align:baseline;white-space:pre-wrap;background-color:transparent">The 1st Workshop on Representation Learning for NLP (</span><a href="https://sites.google.com/site/repl4nlp2016/" style="text-decoration:none"><span style="font-size:14.6667px;font-family:Arial;text-decoration:underline;vertical-align:baseline;white-space:pre-wrap;background-color:transparent">https://sites.google.com/site/repl4nlp2016/</span></a><span style="font-size:14.6667px;font-family:Arial;color:rgb(0,0,0);vertical-align:baseline;white-space:pre-wrap;background-color:transparent">) invites papers of a theoretical or experimental nature on all relevant topics. Relevant topics for the workshop include, but are not limited to, the following areas (in alphabetical order):</span></p><br><ul style="margin-top:0pt;margin-bottom:0pt"><li dir="ltr" style="list-style-type:disc;font-size:14.6667px;font-family:Arial;color:rgb(0,0,0);vertical-align:baseline;background-color:transparent"><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span style="font-size:14.6667px;vertical-align:baseline;white-space:pre-wrap;background-color:transparent">Analysis of language using eigenvalue, singular value and tensor decompositions</span></p></li><li dir="ltr" style="list-style-type:disc;font-size:14.6667px;font-family:Arial;color:rgb(0,0,0);vertical-align:baseline;background-color:transparent"><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span style="font-size:14.6667px;vertical-align:baseline;white-space:pre-wrap;background-color:transparent">Distributional compositional semantics</span></p></li><li dir="ltr" style="list-style-type:disc;font-size:14.6667px;font-family:Arial;color:rgb(0,0,0);vertical-align:baseline;background-color:transparent"><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span style="font-size:14.6667px;vertical-align:baseline;white-space:pre-wrap;background-color:transparent">Integration of distributional representations with other models</span></p></li><li dir="ltr" style="list-style-type:disc;font-size:14.6667px;font-family:Arial;color:rgb(0,0,0);vertical-align:baseline;background-color:transparent"><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span style="font-size:14.6667px;vertical-align:baseline;white-space:pre-wrap;background-color:transparent">Knowledge base embedding</span></p></li><li dir="ltr" style="list-style-type:disc;font-size:14.6667px;font-family:Arial;color:rgb(0,0,0);vertical-align:baseline;background-color:transparent"><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span style="font-size:14.6667px;vertical-align:baseline;white-space:pre-wrap;background-color:transparent">Language modeling for automatic speech recognition, statistical machine translation, and information retrieval</span></p></li><li dir="ltr" style="list-style-type:disc;font-size:14.6667px;font-family:Arial;color:rgb(0,0,0);vertical-align:baseline;background-color:transparent"><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span style="font-size:14.6667px;vertical-align:baseline;white-space:pre-wrap;background-color:transparent">Language modeling for logical and natural reasoning</span></p></li><li dir="ltr" style="list-style-type:disc;font-size:14.6667px;font-family:Arial;color:rgb(0,0,0);vertical-align:baseline;background-color:transparent"><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span style="font-size:14.6667px;vertical-align:baseline;white-space:pre-wrap;background-color:transparent">Latent-variable and representation learning for language</span></p></li><li dir="ltr" style="list-style-type:disc;font-size:14.6667px;font-family:Arial;color:rgb(0,0,0);vertical-align:baseline;background-color:transparent"><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span style="font-size:14.6667px;vertical-align:baseline;white-space:pre-wrap;background-color:transparent">Multi-modal learning for distributional representations</span></p></li><li dir="ltr" style="list-style-type:disc;font-size:14.6667px;font-family:Arial;color:rgb(0,0,0);vertical-align:baseline;background-color:transparent"><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span style="font-size:14.6667px;vertical-align:baseline;white-space:pre-wrap;background-color:transparent">Neural networks and deep learning in NLP</span></p></li><li dir="ltr" style="list-style-type:disc;font-size:14.6667px;font-family:Arial;color:rgb(0,0,0);vertical-align:baseline;background-color:transparent"><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span style="font-size:14.6667px;vertical-align:baseline;white-space:pre-wrap;background-color:transparent">The role of syntax in compositional models</span></p></li><li dir="ltr" style="list-style-type:disc;font-size:14.6667px;font-family:Arial;color:rgb(0,0,0);vertical-align:baseline;background-color:transparent"><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span style="font-size:14.6667px;vertical-align:baseline;white-space:pre-wrap;background-color:transparent">Spectral learning and the method of moments in NLP</span></p></li><li dir="ltr" style="list-style-type:disc;font-size:14.6667px;font-family:Arial;color:rgb(0,0,0);vertical-align:baseline;background-color:transparent"><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span style="font-size:14.6667px;vertical-align:baseline;white-space:pre-wrap;background-color:transparent">Language embeddings and their applications</span></p></li></ul><br><h2 dir="ltr" style="line-height:1.38;margin-top:18pt;margin-bottom:6pt"><span style="font-size:21.3333px;font-family:Arial;color:rgb(0,0,0);font-weight:400;vertical-align:baseline;white-space:pre-wrap;background-color:transparent">Important Dates</span></h2><br><ul style="margin-top:0pt;margin-bottom:0pt"><li dir="ltr" style="list-style-type:disc;font-size:14.6667px;font-family:Arial;color:rgb(0,0,0);vertical-align:baseline;background-color:transparent"><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span style="font-size:14.6667px;vertical-align:baseline;white-space:pre-wrap;background-color:transparent">Deadline for submission: 8 May 2016</span></p></li><li dir="ltr" style="list-style-type:disc;font-size:14.6667px;font-family:Arial;color:rgb(0,0,0);vertical-align:baseline;background-color:transparent"><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span style="font-size:14.6667px;vertical-align:baseline;white-space:pre-wrap;background-color:transparent">Notification of acceptance: 5 June 2016</span></p></li><li dir="ltr" style="list-style-type:disc;font-size:14.6667px;font-family:Arial;color:rgb(0,0,0);vertical-align:baseline;background-color:transparent"><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span style="font-size:14.6667px;vertical-align:baseline;white-space:pre-wrap;background-color:transparent">Deadline for camera-ready version: 22 June 2016</span></p></li><li dir="ltr" style="list-style-type:disc;font-size:14.6667px;font-family:Arial;color:rgb(0,0,0);vertical-align:baseline;background-color:transparent"><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span style="font-size:14.6667px;vertical-align:baseline;white-space:pre-wrap;background-color:transparent">Early registration deadline (ACL'16): To be announced.</span></p></li><li dir="ltr" style="list-style-type:disc;font-size:14.6667px;font-family:Arial;color:rgb(0,0,0);vertical-align:baseline;background-color:transparent"><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span style="font-size:14.6667px;vertical-align:baseline;white-space:pre-wrap;background-color:transparent">Workshop: 11 August 2016</span></p></li></ul><h1 dir="ltr" style="line-height:1.38;margin-top:20pt;margin-bottom:6pt"><span style="font-size:26.6667px;font-family:Arial;color:rgb(0,0,0);font-weight:400;vertical-align:baseline;white-space:pre-wrap;background-color:transparent">Submissions</span></h1><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span style="font-size:14.6667px;font-family:Arial;color:rgb(0,0,0);vertical-align:baseline;white-space:pre-wrap;background-color:transparent">We solicit three categories of papers: regular workshop papers, extended abstracts and cross-submissions. Only regular workshop papers will be included in the proceedings as archival publications. All submissions should be in PDF format and made through the Softconf website set up for this workshop: <a href="https://www.softconf.com/acl2016/repl4nlp2016/">https://www.softconf.com/acl2016/repl4nlp2016/</a>.</span></p><br><h2 dir="ltr" style="line-height:1.38;margin-top:18pt;margin-bottom:6pt"><span style="font-size:21.3333px;font-family:Arial;color:rgb(0,0,0);font-weight:400;vertical-align:baseline;white-space:pre-wrap;background-color:transparent">Regular Workshop Paper</span></h2><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span style="font-size:14.6667px;font-family:Arial;color:rgb(0,0,0);vertical-align:baseline;white-space:pre-wrap;background-color:transparent">Authors should submit a long paper of up to 8 pages, with up to 2 additional pages for references, following the ACL 2016 formatting requirements (see the ACL 2016 Call For Papers for reference: <a href="http://acl2016.org/index.php?article_id=9">http://acl2016.org/index.php?article_id=9</a>). The reported research should be substantially original. Accepted papers will be presented as posters. Selected papers may also be presented orally at the discretion of the committee. Reviewing will be double-blind, and thus no author information should be included in the papers; self-reference that identifies the authors should be avoided or anonymized. Accepted papers will appear in the workshop proceedings, where no distinction will be made on the basis of mode of presentation.</span></p><br><h2 dir="ltr" style="line-height:1.38;margin-top:18pt;margin-bottom:6pt"><span style="font-size:21.3333px;font-family:Arial;color:rgb(0,0,0);font-weight:400;vertical-align:baseline;white-space:pre-wrap;background-color:transparent">Extended Abstracts</span></h2><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span style="font-size:14.6667px;font-family:Arial;color:rgb(0,0,0);vertical-align:baseline;white-space:pre-wrap;background-color:transparent">Preliminary but interesting ideas or results that have not been published before may be submitted as extended abstracts, with length of 2 to 4 pages plus references, following the ACL 2016 formatting requirements (see the ACL 2016 Call For Papers for reference: <a href="http://acl2016.org/index.php?article_id=9">http://acl2016.org/index.php?article_id=9</a>). Reviewing will be double-blind, and thus no author information should be included in the papers; self-reference that identifies the authors should be avoided or anonymized. Accepted abstracts will be presented as posters, but will not be included in the workshop proceedings.</span></p><br><h2 dir="ltr" style="line-height:1.38;margin-top:18pt;margin-bottom:6pt"><span style="font-size:21.3333px;font-family:Arial;color:rgb(0,0,0);font-weight:400;vertical-align:baseline;white-space:pre-wrap;background-color:transparent">Cross Submissions</span></h2><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span style="font-size:14.6667px;font-family:Arial;color:rgb(0,0,0);vertical-align:baseline;white-space:pre-wrap;background-color:transparent">In addition to unpublished work, we also solicit papers on related topics that have appeared in a non-NLP venue (e.g., workshop or conference papers at NIPS or ICML). These papers will be presented as posters, but do not count as RepL4NLP workshop papers and will not be included in the proceedings.  Interested authors need to submit their papers in PDF format through the same Softconf website at <a href="https://www.softconf.com/acl2016/repl4nlp2016/">https://www.softconf.com/acl2016/repl4nlp2016/</a> with a note on the original venue. Papers in this category do not need to follow the ACL format and the selection is solely determined by the organizing committee.</span></p><br><h1 dir="ltr" style="line-height:1.38;margin-top:20pt;margin-bottom:6pt"><span style="font-size:26.6667px;font-family:Arial;color:rgb(0,0,0);font-weight:400;vertical-align:baseline;white-space:pre-wrap;background-color:transparent">Best Paper Prizes</span></h1><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span style="font-size:14.6667px;font-family:Arial;color:rgb(0,0,0);vertical-align:baseline;white-space:pre-wrap;background-color:transparent">Thanks to generous support from our sponsors: Google DeepMind, Microsoft Research, and Facebook, we will be awarding a prize of $300 to the three best regular workshop paper submissions as selected by our program committee, to be presented at the workshop.</span></p><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span style="font-size:14.6667px;font-family:Arial;color:rgb(0,0,0);vertical-align:baseline;white-space:pre-wrap;background-color:transparent"> </span></p><br><br></span></div>