| 1 |
Controllable Sentence Simplification
|
|
|
|
In: LREC 2020 - 12th Language Resources and Evaluation Conference ; https://hal.inria.fr/hal-02678214 ; LREC 2020 - 12th Language Resources and Evaluation Conference, May 2020, Marseille, France ; http://www.lrec-conf.org/proceedings/lrec2020/index.html (2020)
|
|
Abstract:
Due to COVID19 pandemic, the 12th edition is cancelled. The LREC 2020 Proceedings are available at http://www.lrec-conf.org/proceedings/lrec2020/index.html ; International audience ; Text simplification aims at making a text easier to read and understand by simplifying grammar and structure while keeping the underlying information identical. It is often considered an all-purpose generic task where the same simplification is suitable for all; however multiple audiences can benefit from simplified text in different ways. We adapt a discrete parametrization mechanism that provides explicit control on simplification systems based on Sequence-to-Sequence models. As a result, users can condition the simplifications returned by a model on attributes such as length, amount of paraphrasing, lexical complexity and syntactic complexity. We also show that carefully chosen values of these attributes allow out-of-the-box Sequence-to-Sequence models to outperform their standard counterparts on simplification benchmarks. Our model, which we call ACCESS (as shorthand for AudienCe-CEntric Sentence Simplification), establishes the state of the art at 41.87 SARI on the WikiLarge test set, a +1.42 improvement over the best previously reported score.
|
|
Keyword:
[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]; [INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL]; ACCESS; Sequence-to-Sequence models; Text Simplification
|
|
URL: https://hal.inria.fr/hal-02678214/document
https://hal.inria.fr/hal-02678214
https://hal.inria.fr/hal-02678214/file/LREC_2020___Controllable_Sentence_Simplification.pdf
|
|
BASE
|
|
Hide details
|
|
|
| 2 |
ASSET: A Dataset for Tuning and Evaluation of Sentence Simplification Models with Multiple Rewriting Transformations
|
|
|
|
In: ACL 2020 - 58th Annual Meeting of the Association for Computational Linguistics ; https://hal.inria.fr/hal-02889823 ; ACL 2020 - 58th Annual Meeting of the Association for Computational Linguistics, Jul 2020, Seattle / Virtual, United States (2020)
|
|
BASE
|
|
Show details
|
|
|
| 3 |
Augmenting Transformers with KNN-Based Composite Memory for Dialog
|
|
|
|
In: EISSN: 2307-387X ; Transactions of the Association for Computational Linguistics ; https://hal.archives-ouvertes.fr/hal-02999678 ; Transactions of the Association for Computational Linguistics, The MIT Press, In press, ⟨10.1162/tacl_a_00356⟩ ; https://transacl.org/index.php/tacl (2020)
|
|
BASE
|
|
Show details
|
|
|
| 4 |
MUSS: Multilingual Unsupervised Sentence Simplification by Mining Paraphrases ...
|
|
|
|
BASE
|
|
Show details
|
|
|
| 5 |
ASSET: A Dataset for Tuning and Evaluation of Sentence Simplification Models with Multiple Rewriting Transformations ...
|
|
|
|
BASE
|
|
Show details
|
|
|
| 6 |
ASSET: A dataset for tuning and evaluation of sentence simplification models with multiple rewriting transformations
|
|
|
|
BASE
|
|
Show details
|
|
|
|
|
