P2P is a multi-agent framework that automatically generates HTML-rendered academic posters from papers, backed by a 30k instruction dataset and a 121-pair evaluation benchmark.
Multilingual Machine Translation Systems from Microsoft for WMT21 Shared Task
1 Pith paper cite this work. Polarity classification is still indexing.
abstract
This report describes Microsoft's machine translation systems for the WMT21 shared task on large-scale multilingual machine translation. We participated in all three evaluation tracks including Large Track and two Small Tracks where the former one is unconstrained and the latter two are fully constrained. Our model submissions to the shared task were initialized with DeltaLM\footnote{\url{https://aka.ms/deltalm}}, a generic pre-trained multilingual encoder-decoder model, and fine-tuned correspondingly with the vast collected parallel data and allowed data sources according to track settings, together with applying progressive learning and iterative back-translation approaches to further improve the performance. Our final submissions ranked first on three tracks in terms of the automatic evaluation metric.
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P2P: Automated Paper-to-Poster Generation and Fine-Grained Benchmark
P2P is a multi-agent framework that automatically generates HTML-rendered academic posters from papers, backed by a 30k instruction dataset and a 121-pair evaluation benchmark.