APE-QUEST: an MT Quality Gate
The APE-QUEST project (2018--2020) sets up a quality gate and crowdsourcing workflow for the eTranslation system of EC’s Connecting Europe Facility to improve translation quality in specific domains. It packages these services as a translation portal for machine-to-machine and machine-to-human scenarios.
Code (0)
등록된 구현이 없습니다.
Tasks
TranslationSimilar Papers 제목 키워드 기반
Support-BERT: Predicting Quality of Question-Answer Pairs in MSDN using Deep Bidirectional Transformer
Quality of questions and answers from community support websites (e.g. Microsoft Developers Network, Stackoverflow, Github, etc.) is difficult to define and a prediction model of quality questions and answers is even mor…
Community Question AnsweringQuestion AnsweringTransfer LearningDeepQR: Neural-based Quality Ratings for Learnersourced Multiple-Choice Questions
Automated question quality rating (AQQR) aims to evaluate question quality through computational means, thereby addressing emerging challenges in online learnersourced question repositories. Existing methods for AQQR rel…
Contrastive LearningMultiple-choiceSTaR-GATE: Teaching Language Models to Ask Clarifying Questions
When prompting language models to complete a task, users often leave important aspects unsaid. While asking questions could resolve this ambiguity (GATE; Li et al., 2023), models often struggle to ask good questions. We …
Language ModelingLanguage ModellingControllable Open-ended Question Generation with A New Question Type Ontology
We investigate the less-explored task of generating open-ended questions that are typically answered by multiple sentences. We first define a new question type ontology which differentiates the nuanced nature of question…
DiversityQuestion GenerationQuestion-GenerationVocal Bursts Type PredictionQUEST: Quality-aware Semi-supervised Table Extraction for Business Documents
Automating table extraction (TE) from business documents is critical for industrial workflows but remains challenging due to sparse annotations and error-prone multi-stage pipelines. While semi-supervised learning (SSL) …
Pseudo LabelTable Extraction