Looking at the Overlooked: An Analysis on the Word-Overlap Bias in Natural Language Inference

It has been shown that NLI models are usually biased with respect to the word-overlap between premise and hypothesis; they take this feature as a primary cue for predicting the entailment label. In this paper, we focus on an overlooked aspect of the overlap bias in NLI models: the reverse word-overlap bias. Our experimental results demonstrate that current NLI models are highly biased towards the non-entailment label on instances with low overlap, and the existing debiasing methods, which are reportedly successful on existing challenge datasets, are generally ineffective in addressing this category of bias. We investigate the reasons for the emergence of the overlap bias and the role of minority examples in its mitigation. For the former, we find that the word-overlap bias does not stem from pre-training, and for the latter, we observe that in contrast to the accepted assumption, eliminating minority examples does not affect the generalizability of debiasing methods with respect to the overlap bias.Comment: Accepted at EMNLP 202

Solving of Location-Allocation-Routing Model of Reverse Supply Chain for End-of-Life Vehicles Considering Sustainability Dimensions Under Uncertainty Conditions

Abstract In recent years, the concept of reverse logistics has been paid attention by many researchers due to the importance of environmental laws as well as the importance of utilizing from worn-out goods for re-production. In the process of reverse logistics, a systematic manufacturer accepts items such as recycling, reproduction and land filling for products that reach the endpoint of consumption. It is very necessary to address the issue of reverse logistics network and its effective management and guidance. According to the studies, taking into account the uncertainty conditions is one of the most effective factors of modeling reverse logistics network. In reverse logistics, parameters such as capacity of centers, demand, cost and quality are uncertain. With considering the above mentioned issues, the purpose of present study was to develop a mixed fuzzy integer linear planning model for reverse logistics network of EOL vehicles in order to minimize the cost of establishing and constructing facilities, as well as minimizing transportation and material costs between facilities, minimizing environmental impacts, and maximizing social responsibility with taking into account the uncertainty conditions and the multi-product mode. Due to the NP-HARD nature of understudy problem, the Whale optimization algorithm (WOA) and NSGA-II algorithm were used to solve the model, which results of these two modes were comprised based on quality indicators, dispersion and uniformity and solution time of problem