How You Describe Procurement Calls Matters: Predicting Outcome of Public Procurement Using Call Descriptions

Acikalin UU, Gorgun MK, Kutlu M, Tas BKO. “How You Describe Procurement Calls Matters: Predicting Competition and Cost-Effectiveness of European Union Public Procurement Calls Using Their Description”

Abstract: A competitive and cost-effective public procurement process is essential for the effective use of public resources. In this work, we explore whether descriptions of procurement calls can be used to predict their outcomes. In particular, we focus on predicting four well-known economic metrics: i) the number of offers, ii) whether only a single offer is received, iii) whether a foreign firm is awarded the contract, and iv) whether the contract price exceeds the expected price. We extract the European Union's multilingual public procurement notices, covering 22 different languages. We investigate fine-tuning multilingual transformer models and propose two approaches: 1) multilayer perceptron models (MLP) with transformer embeddings for each business sector in which the training data is filtered based on the procurement category and 2) a KNN based approach fine-tuned using Triplet Networks. The fine-tuned MBERT model outperforms all other models in predicting calls with a single offer and foreign contract awards, whereas our MLP based filtering approach yields state-of-the-art results in predicting contracts in which the contract price exceeds the expected price. Furthermore, our KNN based approach outperforms all the baselines in all tasks and our other proposed models in predicting the number of offers. Moreover, we investigate cross-lingual and multilingual training for our tasks and observe that multilingual training improves prediction accuracy in all our tasks. Overall, our experiments suggest that notice descriptions play an important role in outcomes of public procurement calls.