Introducing Unbabel-COMET v2.0: Improved Models and Metrics for Better Machine Translation Evaluation
Unbabel announces the release of open-source Unbabel-COMET v2.0, with improved models and metrics for better MT evaluation.
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NLP and MT
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Transparency and Excellence: The Driving Forces Behind Quality Evaluation in Machine Translation
Almost every translation platform and LSP claims to offer high-quality translations at scale, but how many of them have the ability to prove it? Find out more.
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WAGS: A Beautiful English-Italian Benchmark Supporting Word Alignment Evaluation on Rare Words
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Unbabel's Submission to the WMT2019 APE Shared Task: BERT-Based Encoder-Decoder for Automatic Post-Editing
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Unbabel's Participation in the WMT19 Translation Quality Estimation Shared Task
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Unbabel's Participation in the WMT17 Translation Quality Estimation Shared Task
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Unbabel's Participation in the WMT16 Word-Level Translation Quality Estimation Shared Task
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Translator2Vec: Understanding and Representing Human Post-Editors
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Towards Dynamic Computation Graphs via Sparse Latent Structure
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Toward a Full-scale Neural Machine Translation in Production: The Booking.com Use Case
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The Interact Project and Crisis MT
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SparseMAP: Differentiable Sparse Structured Inference
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Sparse Sequence-to-Sequence Models
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Sparse and Constrained Attention for Neural Machine Translation
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Semi-Supervised Learning of Sequence Models with Method of Moments
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Semantic Textual Similarity in Quality Estimation
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Selective Attention for Context-Aware Neural Machine Translation
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Scheduled Sampling for Transformers
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Pushing the Limits of Translation Quality Estimation
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Proceedings of the Joint Workshop on Multiword Expressions and WordNet
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