We introduce GEM, a living benchmark for natural language Generation (NLG), its Evaluation, and Metrics. Measuring progress in NLG relies on a constantly evolving ecosystem of automated metrics, datasets, and human evaluation standards. Due to this moving target, new models often still evaluate on divergent anglo-centric corpora with wellestablished, but flawed, metrics. This disconnect makes it challenging to identify the limitations of current models and opportunities for progress. Addressing this limitation, GEM provides an environment in which models can easily be applied to a wide set of tasks and in which evaluation strategies can be tested. Regular updates to the benchmark will help NLG research become more multilingual and evolve the challenge alongside models. This paper serves as the description of the data for which we are organizing a shared task at our ACL 2021 Workshop and to which we invite the entire NLG community to participate.

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Knowledge of protein-protein interaction sites provides an important base for deciphering novel drug targets and applications of enzyme-based studies. But on account of biological complexity and transient forms, determination of these sites is a challenge in biology. Various computational approaches are being explored for relevant prediction based on available protein sequencestructure information. Here we propose a novel method SPRINGS (Sequence-based predictor of PRotein-protein interactING Sites) for identification of interaction sites based on sequences. It uses protein evolutionary information, averaged cumulative hydropathy and predicted relative solvent accessibility from amino acid chains in artificial neural network architecture with a promising performance for protein-protein interactions sites based research and applications. KEYWORDS