ACL-EMNLP 2012 Highlights

We demonstrated 80% unsupervised part-of-speech induction accuracy in our EMNLP-2012 paper using paradigmatic representations of word context and co-occurrence modeling. Here are some interesting talks I attended at ACL-EMNLP this year.

Check out this tutorial:

Inderjeet Mani; James Pustejovsky
Qualitative Modeling of Spatial Prepositions and Motion Expressions
http://aclweb.org/supplementals/P/P12/P12-4001.Presentation.pdf

And this paper from today on learning language from navigation instructions and user behavior was interesting:

David Chen
Fast Online Lexicon Learning for Grounded Language Acquisition
http://aclweb.org/anthology-new/P/P12/P12-1045.pdf

All papers can be found on the ACL Anthology page:
http://aclweb.org/anthology-new/P/P12/

Here is a work that builds on CCM for unsupervised parsing that works better on longer sentences:

P12-2004 [bib]: Dave Golland; John DeNero; Jakob Uszkoreit
A Feature-Rich Constituent Context Model for Grammar Induction
http://aclweb.org/anthology-new/P/P12/P12-2004.pdf

Here is another interesting talk about grounded language acquisition, computer learning to follow instructions in a virtual world. They have collected nice corpora of instructions and behaviors of people following those instructions.

P12-2036 [bib]: Luciana Benotti; Martin Villalba; Tessa Lau; Julian Cerruti
Corpus-based Interpretation of Instructions in Virtual Environments
http://aclweb.org/anthology-new/P/P12/P12-2036.pdf

And here is another similar work on grounded language acquisition:

P12-1045 [bib]: David Chen
Fast Online Lexicon Learning for Grounded Language Acquisition
http://aclweb.org/anthology-new/P/P12/P12-1045.pdf

I nominate this as the best paper on computer humor generation of ACL 2012. Note the resources ConceptNet and SentiNet mentioned in this work which may be independently useful.

P12-2030 [bib]: Igor Labutov; Hod Lipson
Humor as Circuits in Semantic Networks
http://aclweb.org/anthology-new/P/P12/P12-2030.pdf

We have been working on reordering for SMT. Here is a paper that modifies distortion matrices instead to allow more flexible reorderings.

P12-1050 [bib]: Arianna Bisazza; Marcello Federico
Modified Distortion Matrices for Phrase-Based Statistical Machine Translation
http://aclweb.org/anthology-new/P12-1050.pdf

Interesting tree transformation operations.

Transforming Trees to Improve Syntactic Convergence
D. Burkett and D. Klein .
http://aclweb.org/anthology-new/D/D12/D12-1079.pdf

The following paper utilizes n-gram language models in unsupervised dependency parsing:

D12-1028 [bib]: David Mareček; Zdeněk Žabokrtský
Exploiting Reducibility in Unsupervised Dependency Parsing
http://aclweb.org/anthology-new/D/D12/D12-1028.pdf

Another reordering paper from EMNLP. Mentions a string to tree version of Moses that is publicly available.

D12-1077 [bib]: Graham Neubig; Taro Watanabe; Shinsuke Mori
Inducing a Discriminative Parser to Optimize Machine Translation Reordering
http://aclweb.org/anthology-new/D/D12/D12-1077.pdf

Must read paper from emnlp. Very likely our paradigmatic representation would do better here.

D12-1130 [bib]: Carina Silberer; Mirella Lapata
Grounded Models of Semantic Representation