TY - JOUR
T1 - How you describe procurement calls matters
T2 - Predicting outcome of public procurement using call descriptions
AU - Acikalin, Utku Umur
AU - Gorgun, Mustafa Kaan
AU - Kutlu, Mucahid
AU - Tas, Bedri Kamil Onur
N1 - Publisher Copyright:
© The Author(s), 2023.
PY - 2023/8/10
Y1 - 2023/8/10
N2 - A competitive and cost-effective public procurement (PP) 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 PP notices, covering 22 different languages. We investigate fine-tuning multilingual transformer models and propose two approaches: (1) multilayer perceptron (MLP) models with transformer embeddings for each business sector in which the training data are filtered based on the procurement category and (2) a k-nearest neighbor (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 the outcomes of PP calls.
AB - A competitive and cost-effective public procurement (PP) 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 PP notices, covering 22 different languages. We investigate fine-tuning multilingual transformer models and propose two approaches: (1) multilayer perceptron (MLP) models with transformer embeddings for each business sector in which the training data are filtered based on the procurement category and (2) a k-nearest neighbor (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 the outcomes of PP calls.
KW - Multilinguality
KW - Text classification
UR - http://www.scopus.com/inward/record.url?scp=85171798403&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85171798403&partnerID=8YFLogxK
UR - https://www.mendeley.com/catalogue/31a282d0-d09c-383d-b882-d8c0622aee38/
U2 - 10.1017/S135132492300030X
DO - 10.1017/S135132492300030X
M3 - Article
AN - SCOPUS:85171798403
SN - 1351-3249
SP - 1
EP - 22
JO - Natural Language Engineering
JF - Natural Language Engineering
ER -