• Corpus ID: 1512061

Adversarial Evaluation for Models of Natural Language

@article{Smith2012AdversarialEF,
  title={Adversarial Evaluation for Models of Natural Language},
  author={Noah A. Smith},
  journal={ArXiv},
  year={2012},
  volume={abs/1207.0245}
}
We now have a rich and growing set of modeling tools and algorithms for inducing linguistic structure from text that is less than fully annotated. In this paper, we discuss some of the weaknesses of our current methodology. We present a new abstract framework for evaluating natural language processing (NLP) models in general and unsupervised NLP models in particular. The central idea is to make explicit certain adversarial roles among researchers, so that the different roles in an evaluation… 

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References

SHOWING 1-10 OF 13 REFERENCES
Guiding Unsupervised Grammar Induction Using Contrastive Estimation
TLDR
It is shown that, using the same features, log-linear dependency grammar models trained using CE can drastically outperform EMtrained generative models on the task of matching human linguistic annotations (the MATCHLINGUIST task).
Reading Tea Leaves: How Humans Interpret Topic Models
TLDR
New quantitative methods for measuring semantic meaning in inferred topics are presented, showing that they capture aspects of the model that are undetected by previous measures of model quality based on held-out likelihood.
CoNLL-X Shared Task on Multilingual Dependency Parsing
TLDR
How treebanks for 13 languages were converted into the same dependency format and how parsing performance was measured is described and general conclusions about multi-lingual parsing are drawn.
Machine Learning that Matters
TLDR
This work presents six Impact Challenges to explicitly focus the field of machine learning's energy and attention, and discusses existing obstacles that must be addressed.
Latent Dirichlet Allocation
Large Language Models in Machine Translation
TLDR
Systems, methods, and computer program products for machine translation are provided for backoff score determination as a function of a backoff factor and a relative frequency of a corresponding backoff n-gram in the corpus.
The Omphalos Context-Free Grammar Learning Competition
TLDR
The Omphalos Context-Free Grammar Learning Competition held as part of the International Colloquium on Grammatical Inference 2004 is described, including a new measure of the complexity of inferring context-free grammars, used to rank the competition problems.
Predicting Risk from Financial Reports with Regression
TLDR
This work applies well-known regression techniques to a large corpus of freely available financial reports, constructing regression models of volatility for the period following a report, rivaling past volatility in predicting the target variable.
Relations among Notions of Security for Public-Key Encryption Schemes
TLDR
The goals of privacy and non-malleability are considered, each under chosen plaintext attack and two kinds of chosen ciphertext attack, and a new definition of non-Malleability is proposed which the author believes is simpler than the previous one.
Why do Nigerian Scammers Say They are From Nigeria?
TLDR
It is shown that as victim density decreases the fraction of viable users than can be profitably attacked drops dramatically, and suggests that only by finding large numbers of victims can he learn how to accurately distinguish the two.
...
...