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Natural Language Processing

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Temporal effects on NLP models

I will briefly introduce my work at Adobe Research, then talk about recent work with my student Oshin Agarwal.

 

"Think before you speak": Language Generation with Planning

 

Guarding Against Spurious Correlations in Natural Language Understanding

 

Challenges and Opportunities in Evaluating Progress in NLP

Abstract: The past few years have seen remarkable advances in NLP, as evidenced both by continued and rapid gains on benchmark tasks, as well as by the increasing prominence of real NLP systems in the wild.  In assessing such progress, however, it is important to ask not only what system achieves the best performance, but how it achieves that level of performance, how much we can trust the evaluation, and what the consequences of deploying such a system might be.

 

A Prioritization Model for Suicidality Risk Assessment

Understanding and Generating Human Language

 

Mental Health as an NLP Problem

 

Xiaozhong Liu (School of Informatics, Computing, and Engineering; Indiana University, Bloomington)

Natural Language Processing and Text Mining with User Behavior Data

Cache Transition Systems for Semantic Parsing

Abstract: We describe a transition system that generalizes standard transition-based dependency parsing techniques to generate a graph rather than a tree.  Our system includes a cache with fixed size m, and we characterize the relationship between the parameter m and the class of graphs that can be produced through the graph-theoretic concept of tree decomposition.  We train a sequence-to-sequence neural model based on this system to parse text into Abstract Meaning Representation (AMR).

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