Computing servers for NLP applications

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This RTI application aims to equip the RALI lab on Computational Linguistics and Applications (Recherche appliquée en linguistique informatique) with adequate computation resources necessary for our research. The RALI lab is specialized in natural language processing (NLP) and applications. Our research addresses both fundamental and application questions related to NLP: how can we understand languages and how can we use natural languages in different applications. Our research work covers a large number of topics such as information retrieval (search engine), information extraction, question answering, dialogue systems, recommendation systems, social media mining, medical information retrieval and question answering, and so on. All these applications are highly in demand in industry, and more generally in our society. The RALI lab has been performing high-quality research work, widely recognized by the peers. We publish at the very top conferences and journals in this domain and our research work has made significant impact in the community (e.g. Test of Time honorable mention of ACM SIGIR in information retrieval). However, our current equipment is no longer adequate to support our research in NLP and applications. The domain has made a drastic move in recent years toward methods based on deep learning and deep neural networks, that exploit huge amounts of textual data and large pre-trained models (from 0.34 billions parameter for BERT to 175 billion for GPT-3). Intensive computation on GPU servers is required to benefit from these models. The NLP domain is becoming more competitive than ever, in the sense that not only innovative ideas, but also powerful computation resources, are required. While most top NLP labs have updated their computer servers to meet the need, the RALI lab is well behind: we only have a small number of workstations with GPU cores shared by 3 faculty members, 3 research associates and over 40 graduate students, most of whom use neural models and require GPUs. The poor equipment is causing serious delays in our research and publications. The requested equipment - GPU server and storage server, will allow us to remain competitive in NLP research and offer adequate environment for HQP training. The equipment will also help us attract more good students, especially female students and students from visible minorities, for whom the computation environment is an important criterion when choosing a lab for their studies.