Vikas Raunak
5 papers in the PaperMetrix corpus
Papers by this author
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NL-Augmenter 🦎 → 🐍 A Framework for Task-Sensitive Natural Language Augmentation
2023 · Northern European Journal of Language Technology
Data augmentation is an important method for evaluating the robustness of and enhancing the diversity of training data for natural language processing (NLP) models. In this paper, we present NL-Augmenter, a new participatory Python-based natural …
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Leveraging GPT-4 for Automatic Translation Post-Editing
2023 · arXiv (Cornell University)
While Neural Machine Translation (NMT) represents the leading approach to Machine Translation (MT), the outputs of NMT models still require translation post-editing to rectify errors and enhance quality under critical settings. In this work, we …
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Evaluating Metrics for Document-context Evaluation in Machine Translation
2023
We describe our submission of a new metric, SLIDE (Raunak et al., 2023), to the WMT 2023 metrics task.SLIDE is a reference-free qualityestimation metric that works by constructing a fixed sentence-length window over the documents …
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The GEM Benchmark: Natural Language Generation, its Evaluation and Metrics
2021
Sebastian Gehrmann, Tosin Adewumi, Karmanya Aggarwal, Pawan Sasanka Ammanamanchi, Anuoluwapo Aremu, Antoine Bosselut, Khyathi Raghavi Chandu, Miruna-Adriana Clinciu, Dipanjan Das, Kaustubh Dhole, Wanyu Du, Esin Durmus, Ondřej Dušek, Chris Chinenye Emezue, Varun Gangal, Cristina Garbacea, …
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How Good Are GPT Models at Machine Translation? A Comprehensive Evaluation
2023 · arXiv (Cornell University)
Generative Pre-trained Transformer (GPT) models have shown remarkable capabilities for natural language generation, but their performance for machine translation has not been thoroughly investigated. In this paper, we present a comprehensive evaluation of GPT models …