DE eng

Search in the Catalogues and Directories

Page: 1 2
Hits 1 – 20 of 24

1
Can Synthetic Translations Improve Bitext Quality? ...
BASE
Show details
2
Rule-based Morphological Inflection Improves Neural Terminology Translation ...
BASE
Show details
3
Rule-based Morphological Inflection Improves Neural Terminology Translation ...
Xu, Weijia; Carpuat, Marine. - : arXiv, 2021
BASE
Show details
4
Evaluating the Evaluation Metrics for Style Transfer: A Case Study in Multilingual Formality Transfer ...
BASE
Show details
5
Beyond Noise: Mitigating the Impact of Fine-grained Semantic Divergences on Neural Machine Translation ...
BASE
Show details
6
The UMD Submission to the Explainable MT Quality Estimation Shared Task: Combining Explanation Models with Sequence Labeling ...
BASE
Show details
7
Evaluating the Evaluation Metrics for Style Transfer: A Case Study in Multilingual Formality Transfer ...
Abstract: Anthology paper link: https://aclanthology.org/2021.emnlp-main.100/ Abstract: While the field of style transfer (ST) has been growing rapidly, it has been hampered by a lack of standardized practices for automatic evaluation. In this paper, we evaluate leading ST automatic metrics on the oft-researched task of formality style transfer. Unlike previous evaluations, which focus solely on English, we expand our focus to Brazilian-Portuguese, French, and Italian, making this work the first multilingual evaluation of metrics in ST. We outline best practices for automatic evaluation in (formality) style transfer and identify several models that correlate well with human judgments and are robust across languages. We hope that this work will help accelerate development in ST, where human evaluation is often challenging to collect. ...
Keyword: Computational Linguistics; Machine Learning; Machine Learning and Data Mining; Natural Language Processing
URL: https://underline.io/lecture/37990-evaluating-the-evaluation-metrics-for-style-transfer-a-case-study-in-multilingual-formality-transfer
https://dx.doi.org/10.48448/wawp-tb64
BASE
Hide details
8
How Does Distilled Data Complexity Impact the Quality and Confidence of Non-Autoregressive Machine Translation? ...
BASE
Show details
9
EDITOR: an Edit-Based Transformer with Repositioning for Neural Machine Translation with Soft Lexical Constraints ...
BASE
Show details
10
A Non-Autoregressive Edit-Based Approach to Controllable Text Simplification ...
BASE
Show details
11
Detecting Fine-Grained Cross-Lingual Semantic Divergences without Supervision by Learning to Rank ...
BASE
Show details
12
Incorporating Terminology Constraints in Automatic Post-Editing ...
BASE
Show details
13
EDITOR: an Edit-Based Transformer with Repositioning for Neural Machine Translation with Soft Lexical Constraints ...
Xu, Weijia; Carpuat, Marine. - : arXiv, 2020
BASE
Show details
14
Controlling Neural Machine Translation Formality with Synthetic Supervision ...
Niu, Xing; Carpuat, Marine. - : arXiv, 2019
BASE
Show details
15
Controlling Text Complexity in Neural Machine Translation ...
Agrawal, Sweta; Carpuat, Marine. - : arXiv, 2019
BASE
Show details
16
Identifying Semantic Divergences Across Languages
Vyas, Yogarshi. - 2019
BASE
Show details
17
Formality Style Transfer Within and Across Languages with Limited Supervision
Niu, Xing. - 2019
BASE
Show details
18
Identifying Semantic Divergences in Parallel Text without Annotations ...
BASE
Show details
19
Bi-Directional Neural Machine Translation with Synthetic Parallel Data ...
BASE
Show details
20
Multi-Task Neural Models for Translating Between Styles Within and Across Languages ...
Niu, Xing; Rao, Sudha; Carpuat, Marine. - : arXiv, 2018
BASE
Show details

Page: 1 2

Catalogues
0
0
2
0
0
0
0
Bibliographies
1
0
0
0
0
0
0
0
0
Linked Open Data catalogues
0
Online resources
0
0
0
0
Open access documents
22
0
0
0
0
© 2013 - 2024 Lin|gu|is|tik | Imprint | Privacy Policy | Datenschutzeinstellungen ändern