1 |
Neural Token Segmentation for High Token-Internal Complexity ...
|
|
|
|
BASE
|
|
Show details
|
|
2 |
Morphological Reinflection with Multiple Arguments: An Extended Annotation schema and a Georgian Case Study ...
|
|
|
|
BASE
|
|
Show details
|
|
10 |
Applying the Transformer to Character-level Transduction
|
|
|
|
In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume (2021)
|
|
Abstract:
The transformer has been shown to outperform recurrent neural network-based sequence-to-sequence models in various word-level NLP tasks. Yet for character-level transduction tasks, e.g. morphological inflection generation and historical text normalization, there are few works that outperform recurrent models using the transformer. In an empirical study, we uncover that, in contrast to recurrent sequence-to-sequence models, the batch size plays a crucial role in the performance of the transformer on character-level tasks, and we show that with a large enough batch size, the transformer does indeed outperform recurrent models. We also introduce a simple technique to handle feature-guided character-level transduction that further improves performance. With these insights, we achieve state-of-the-art performance on morphological inflection and historical text normalization. We also show that the transformer outperforms a strong baseline on two other character-level transduction tasks: grapheme-to-phoneme conversion and transliteration.
|
|
URL: https://hdl.handle.net/20.500.11850/518998 https://doi.org/10.3929/ethz-b-000518998
|
|
BASE
|
|
Hide details
|
|
11 |
Telling BERT's Full Story: from Local Attention to Global Aggregation
|
|
|
|
In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume (2021)
|
|
BASE
|
|
Show details
|
|
12 |
Disambiguatory Signals are Stronger in Word-initial Positions
|
|
|
|
In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume (2021)
|
|
BASE
|
|
Show details
|
|
14 |
Asking It All: Generating Contextualized Questions for any Semantic Role ...
|
|
|
|
BASE
|
|
Show details
|
|
15 |
The Possible, the Plausible, and the Desirable: Event-Based Modality Detection for Language Processing ...
|
|
|
|
BASE
|
|
Show details
|
|
16 |
The Possible, the Plausible, and the Desirable: Event-Based Modality Detection for Language Processing ...
|
|
|
|
BASE
|
|
Show details
|
|
17 |
Formae reformandae: for a reorganisation of verb form annotation in Universal Dependencies illustrated by the specific case of Latin
|
|
Cecchini, Flavio Massimiliano (orcid:0000-0001-9029-1822). - : Association for Computational Linguistics, 2021. : country:BGR, 2021. : place:Sofia, 2021
|
|
BASE
|
|
Show details
|
|
18 |
RelWalk - A Latent Variable Model Approach to Knowledge Graph Embedding.
|
|
|
|
BASE
|
|
Show details
|
|
|
|