Summer 2017 Schedule

Date	Title
7.18.17	Bill Foland thesis defense - Natural Language Understanding: Deep Learning for Abstract Meaning Representation Abstract In the last few years there have been major improvements in the performance of hard natural language processing tasks due to the application of artificial neural network models. These models replace complex hand-engineered systems for extracting and representing the meaning of human language with systems which learn features based on processing examples of language. In this dissertation, I present deep neural networks for semantic role labeling, and then for Abstract Meaning Representation parsing, and a novel Distributed Abstract Meaning Representation, or DAMR. I then describe a model used to create fixed vector representations of sentence meaning from DAMR. Finally, I use natural language inference to test the quality of the meaning content of these fixed vectors.
7.19.17	Jan Hajic - the ACL 2017 UD shared task Abstract: Universal Dependencies (UD) is a project that is developing cross-linguistically consistent treebank annotation for many languages, with the goal of facilitating multilingual parser development, cross-lingual learning, and parsing research from a language typology perspective. The annotation scheme is based on an evolution of (universal) Stanford dependencies (de Marneffe, Manning et al., 2006-2014), Google universal part-of-speech tags (Petrov et al., 2012), and the Interset interlingua for morphosyntactic tagsets (Zeman, 2008). The general philosophy is to provide a universal inventory of categories and guidelines to facilitate consistent annotation of similar constructions across languages, while allowing language-specific extensions when necessary. First release was in 2014, with bi-annual updates and additions (now at 65 treebanks). To prove the point, the UD initiative, building on the 10-year history of CoNLL Shared Tasks on parsing, organized a shared task centered around the UD treebanks (Hajič et al., 2017). The focus of the 2017 task was to develop syntactic dependency parsers that can work in a real-world setting, starting from raw text, and that can work over many typologically different languages, even surprise languages for which there is little or no training data, by exploiting a common syntactic annotation standard. For the Shared Task, the Universal Dependencies version 2 (UD v2) annotation scheme was used, which is the latest version.