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Found 22 papers, 3 papers with code
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.
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).
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.
Tag Assisted Neural Machine Translation of Film Subtitles
We implemented a neural machine translation system that uses automatic sequence tagging to improve the quality of translation.
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.
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.
mcBERT: Momentum Contrastive Learning with BERT for Zero-Shot Slot Filling
Zero-shot slot filling has received considerable attention to cope with the problem of limited available data for the target domain.
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.
POSTECH Submission on Duolingo Shared Task
In this paper, we propose a transfer learning based simultaneous translation model by extending BART.
Modeling Inter-Speaker Relationship in XLNet for Contextual Spoken Language Understanding
We propose two methods to capture relevant history information in a multi-turn dialogue by modeling inter-speaker relationship for spoken language understanding (SLU).
Transformer-based Automatic Post-Editing with a Context-Aware Encoding Approach for Multi-Source Inputs
Recent approaches to the Automatic Post-Editing (APE) research have shown that better results are obtained by multi-source models, which jointly encode both source (src) and machine translation output (mt) to produce post-edited sentence (pe).
Transformer-based Automatic Post-Editing Model with Joint Encoder and Multi-source Attention of Decoder
This paper describes POSTECH{'}s submission to the WMT 2019 shared task on Automatic Post-Editing (APE).
Decay-Function-Free Time-Aware Attention to Context and Speaker Indicator for Spoken Language Understanding
To capture salient contextual information for spoken language understanding (SLU) of a dialogue, we propose time-aware models that automatically learn the latent time-decay function of the history without a manual time-decay function.
Multi-encoder Transformer Network for Automatic Post-Editing
This paper describes the POSTECH{'}s submission to the WMT 2018 shared task on Automatic Post-Editing (APE).
Self-Attention-Based Message-Relevant Response Generation for Neural Conversation Model
Using a sequence-to-sequence framework, many neural conversation models for chit-chat succeed in naturalness of the response.
Multiple Range-Restricted Bidirectional Gated Recurrent Units with Attention for Relation Classification
Most of neural approaches to relation classification have focused on finding short patterns that represent the semantic relation using Convolutional Neural Networks (CNNs) and those approaches have generally achieved better performances than using Recurrent Neural Networks (RNNs).
Improving Term Frequency Normalization for Multi-topical Documents, and Application to Language Modeling Approaches
Although these document characteristics should be differently handled, all previous methods of term frequency normalization have ignored these differences and have used a simplified length-driven approach which decreases the term frequency by only the length of a document, causing an unreasonable penalization.
