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
|
|
95 |
Morphologically Aware Word-Level Translation ...
|
|
|
|
Abstract:
We propose a novel morphologically aware probability model for bilingual lexicon induction, which jointly models lexeme translation and inflectional morphology in a structured way. Our model exploits the basic linguistic intuition that the lexeme is the key lexical unit of meaning, while inflectional morphology provides additional syntactic information. This approach leads to substantial performance improvements - 19% average improvement in accuracy across 6 language pairs over the state of the art in the supervised setting and 16% in the weakly supervised setting. As another contribution, we highlight issues associated with modern BLI that stem from ignoring inflectional morphology, and propose three suggestions for improving the task. ... : COLING 2020 ...
|
|
Keyword:
Computation and Language cs.CL; FOS Computer and information sciences
|
|
URL: https://dx.doi.org/10.48550/arxiv.2011.07593 https://arxiv.org/abs/2011.07593
|
|
BASE
|
|
Hide 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)
|
|
BASE
|
|
Show 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
|
|
|
|