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Found 8 papers, 2 papers with code
Transformer-based Screenplay Summarization Using Augmented Learning Representation with Dialogue Information
Moreover, in an attempt to improve the model architecture of previous studies, we replace LSTM with Transformer.
POSTECH-ETRI’s Submission to the WMT2020 APE Shared Task: Automatic Post-Editing with Cross-lingual Language Model
This paper describes POSTECH-ETRI’s submission to WMT2020 for the shared task on automatic post-editing (APE) for 2 language pairs: English-German (En-De) and English-Chinese (En-Zh).
Noising Scheme for Data Augmentation in Automatic Post-Editing
In our experiment, we implemented a noising module that simulates four types of post-editing errors, and we introduced this module into a Transformer-based multi-source APE model.
Quality Estimation Using Dual Encoders with Transfer Learning
This paper describes POSTECH’s quality estimation systems submitted to Task 2 of the WMT 2021 quality estimation shared task: Word and Sentence-Level Post-editing Effort.
Denoising Table-Text Retrieval for Open-Domain Question Answering
Previous studies in table-text open-domain question answering have two common challenges: firstly, their retrievers can be affected by false-positive labels in training datasets; secondly, they may struggle to provide appropriate evidence for questions that require reasoning across the table.
Bring More Attention to Syntactic Symmetry for Automatic Postediting of High-Quality Machine Translations
Automatic postediting (APE) is an automated process to refine a given machine translation (MT).
Towards Semi-Supervised Learning of Automatic Post-Editing: Data-Synthesis by Infilling Mask with Erroneous Tokens
Semi-supervised learning that leverages synthetic training data has been widely adopted in the field of Automatic post-editing (APE) to overcome the lack of human-annotated training data.
Adaptation of Back-translation to Automatic Post-Editing for Synthetic Data Generation
Automatic Post-Editing (APE) aims to correct errors in the output of a given machine translation (MT) system.
