81 |
SIGMORPHON 2020 Shared Task 0: Typologically Diverse Morphological Inflection ...
|
|
|
|
BASE
|
|
Show details
|
|
83 |
SIGTYP 2020 Shared Task: Prediction of Typological Features ...
|
|
|
|
BASE
|
|
Show details
|
|
86 |
It’s Easier to Translate out of English than into it: Measuring Neural Translation Difficulty by Cross-Mutual Information ...
|
|
|
|
BASE
|
|
Show details
|
|
89 |
Generalized Entropy Regularization or: There’s Nothing Special about Label Smoothing ...
|
|
|
|
BASE
|
|
Show details
|
|
94 |
Investigating Cross-Linguistic Adjective Ordering Tendencies with a Latent-Variable Model ...
|
|
|
|
BASE
|
|
Show details
|
|
98 |
Please Mind the Root: Decoding Arborescences for Dependency Parsing
|
|
|
|
In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) (2020)
|
|
Abstract:
The connection between dependency trees and spanning trees is exploited by the NLP community to train and to decode graph-based dependency parsers. However, the NLP literature has missed an important difference between the two structures: only one edge may emanate from the root in a dependency tree. We analyzed the output of state-of-the-art parsers on many languages from the Universal Dependency Treebank: although these parsers are often able to learn that trees which violate the constraint should be assigned lower probabilities, their ability to do so unsurprisingly de-grades as the size of the training set decreases.In fact, the worst constraint-violation rate we observe is 24%. Prior work has proposed an inefficient algorithm to enforce the constraint, which adds a factor of n to the decoding runtime. We adapt an algorithm due to Gabow and Tarjan (1984) to dependency parsing, which satisfies the constraint without compromising the original runtime.
|
|
URL: https://doi.org/10.3929/ethz-b-000462321 https://hdl.handle.net/20.500.11850/462321
|
|
BASE
|
|
Hide details
|
|
99 |
Measuring the Similarity of Grammatical Gender Systems by Comparing Partitions
|
|
|
|
In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) (2020)
|
|
BASE
|
|
Show details
|
|
100 |
Investigating Cross-Linguistic Adjective Ordering Tendencies with a Latent-Variable Model
|
|
|
|
In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) (2020)
|
|
BASE
|
|
Show details
|
|
|
|