OpeNTF: A Benchmark Library for Neural Team Formation
Arman Dashti, Karan Saxena, Dhwani Patel, Hossein Fani · Proceedings of the 31st ACM International Conference on Information & Knowledge Management · 2022
We contribute OpeNTF, an open-source python-based benchmark library to support neural team formation research. Team formation falls under social information retrieval (Social IR), where the right group of experts should be retrieved to solve a task, which is intractable due to the vast pool of feasible candidates with diverse skills. Even though neural networks could successfully address efficiency while maintaining efficacy, they lack standard implementation and experimental details, which calls for excessive efforts in repeating or reproducing the results in new domains. OpeNTF provides a standard and reproducible platform for neural team formation. It incorporates a host of canonical neural models along with three large-scale training datasets from varying domains. Leveraging an object-oriented structure, OpeNTF readily accommodates the addition of new neural models and training datasets. The first of its kind in neural team formation, OpeNTF also offers negative sampling heuristics that can be seamlessly integrated during model training to boost efficiency and to improve the effectiveness of inference.