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Found 17 papers, 1 papers with code
Dealing with the Paradox of Quality Estimation
In quality estimation (QE), the quality of translation can be predicted by referencing the source sentence and the machine translation (MT) output without access to the reference sentence.
Priming Ancient Korean Neural Machine Translation
In recent years, there has been an increasing need for the restoration and translation of historical languages.
Empirical Analysis of Noising Scheme based Synthetic Data Generation for Automatic Post-editing
This study has several limitations, considering the data acquisition, because there is no official dataset for most language pairs.
BTS: Back TranScription for Speech-to-Text Post-Processor using Text-to-Speech-to-Text
Automatic speech recognition (ASR) is arguably the most critical component of such systems, as errors in speech recognition propagate to the downstream components and drastically degrade the user experience.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
A Dog Is Passing Over The Jet? A Text-Generation Dataset for Korean Commonsense Reasoning and Evaluation
However, Korean pretrained language models still struggle to generate a short sentence with a given condition based on compositionality and commonsense reasoning (i. e., generative commonsense reasoning).
Focus on FoCus: Is FoCus focused on Context, Knowledge and Persona?
As a result of the experiment, we present that the FoCus model could not correctly blend the knowledge according to the input dialogue and that the dataset design is unsuitable for the multi-turn conversation.
Toward Practical Automatic Speech Recognition and Post-Processing: a Call for Explainable Error Benchmark Guideline
To effectively address this, it is imperative to consider both the speech-level, crucial for recognition accuracy, and the text-level, critical for user-friendliness.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Synthetic Alone: Exploring the Dark Side of Synthetic Data for Grammatical Error Correction
Data-centric AI approach aims to enhance the model performance without modifying the model and has been shown to impact model performance positively.
Towards Diverse and Effective Question-Answer Pair Generation from Children Storybooks
In this paper, we propose a QAG framework that enhances QA type diversity by producing different interrogative sentences and implicit/explicit answers.
Self-Improving-Leaderboard(SIL): A Call for Real-World Centric Natural Language Processing Leaderboards
Leaderboard systems allow researchers to objectively evaluate Natural Language Processing (NLP) models and are typically used to identify models that exhibit superior performance on a given task in a predetermined setting.
QUAK: A Synthetic Quality Estimation Dataset for Korean-English Neural Machine Translation
With the recent advance in neural machine translation demonstrating its importance, research on quality estimation (QE) has been steadily progressing.
A Self-Supervised Automatic Post-Editing Data Generation Tool
Data building for automatic post-editing (APE) requires extensive and expert-level human effort, as it contains an elaborate process that involves identifying errors in sentences and providing suitable revisions.
A New Tool for Efficiently Generating Quality Estimation Datasets
Building of data for quality estimation (QE) training is expensive and requires significant human labor.
Automatic Knowledge Augmentation for Generative Commonsense Reasoning
Generative commonsense reasoning is the capability of a language model to generate a sentence with a given concept-set that is based on commonsense knowledge.
How should human translation coexist with NMT? Efficient tool for building high quality parallel corpus
This paper proposes a tool for efficiently constructing high-quality parallel corpora with minimizing human labor and making this tool publicly available.
Empirical Analysis of Korean Public AI Hub Parallel Corpora and in-depth Analysis using LIWC
To the best of our knowledge, this study is the first to use LIWC to analyze parallel corpora in the field of NMT.
Should we find another model?: Improving Neural Machine Translation Performance with ONE-Piece Tokenization Method without Model Modification
We derive an optimal subword tokenization result for Korean-English machine translation by conducting a case study that combines the subword tokenization method, morphological segmentation, and vocabulary method.
