| 21 |
Predicting Declension Class from Form and Meaning
|
|
|
|
In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (2020)
|
|
BASE
|
|
Show details
|
|
|
| 22 |
A Tale of a Probe and a Parser
|
|
|
|
In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (2020)
|
|
BASE
|
|
Show details
|
|
|
| 23 |
Intrinsic Probing through Dimension Selection
|
|
|
|
In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) (2020)
|
|
BASE
|
|
Show details
|
|
|
| 24 |
Information-Theoretic Probing for Linguistic Structure
|
|
|
|
In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (2020)
|
|
BASE
|
|
Show details
|
|
|
| 25 |
Pareto Probing: Trading Off Accuracy for Complexity ...
|
|
|
|
Abstract:
The question of how to probe contextual word representations for linguistic structure in a way that is both principled and useful has seen significant attention recently in the NLP literature. In our contribution to this discussion, we argue for a probe metric that reflects the fundamental trade-off between probe complexity and performance: the Pareto hypervolume. To measure complexity, we present a number of parametric and non-parametric metrics. Our experiments using Pareto hypervolume as an evaluation metric show that probes often do not conform to our expectations---e.g., why should the non-contextual fastText representations encode more morpho-syntactic information than the contextual BERT representations? These results suggest that common, simplistic probing tasks, such as part-of-speech labeling and dependency arc labeling, are inadequate to evaluate the linguistic structure encoded in contextual word representations. This leads us to propose full dependency parsing as a probing task. In support of ... : Tiago Pimentel and Naomi Saphra contributed equally to this work. Camera ready version of EMNLP 2020 publication. Code available in https://github.com/rycolab/pareto-probing ...
|
|
Keyword:
Computation and Language cs.CL; FOS Computer and information sciences; Machine Learning cs.LG
|
|
URL: https://arxiv.org/abs/2010.02180
https://dx.doi.org/10.48550/arxiv.2010.02180
|
|
BASE
|
|
Hide details
|
|
|
| 28 |
Measuring the Similarity of Grammatical Gender Systems by Comparing Partitions ...
|
|
|
|
BASE
|
|
Show details
|
|
|
| 30 |
Definiteness across languages
|
|
|
|
In: Language Science Press; (2019)
|
|
BASE
|
|
Show details
|
|
|
| 31 |
Definiteness across languages
|
|
|
|
In: Language Science Press; (2019)
|
|
BASE
|
|
Show details
|
|
|
| 32 |
Definiteness across languages
|
|
|
|
In: Language Science Press; (2019)
|
|
BASE
|
|
Show details
|
|
|
| 33 |
Definiteness across languages
|
|
|
|
In: Language Science Press; (2019)
|
|
BASE
|
|
Show details
|
|
|
| 34 |
Definiteness across languages
|
|
|
|
In: Language Science Press; (2019)
|
|
BASE
|
|
Show details
|
|
|
| 35 |
Definiteness across languages
|
|
|
|
In: Language Science Press; (2019)
|
|
BASE
|
|
Show details
|
|
|
| 36 |
Definiteness across languages
|
|
|
|
In: Language Science Press; (2019)
|
|
BASE
|
|
Show details
|
|
|
| 37 |
On the Idiosyncrasies of the Mandarin Chinese Classifier System ...
|
|
|
|
BASE
|
|
Show details
|
|
|
| 39 |
Definiteness across languages
|
|
|
|
In: Language Science Press; (2019)
|
|
BASE
|
|
Show details
|
|
|
| 40 |
Definiteness across languages
|
|
|
|
In: Language Science Press; (2019)
|
|
BASE
|
|
Show details
|
|
|
|
|
