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Semantic textual similarity is one of the open research challenges in thefield of Natural Language Processing. Extensive research has been carried outin this field and near-perfect results are achieved by recent transformer-basedmodels in existing benchmark datasets like the STS dataset and the SICKdataset. In this paper, we study the sentences in these datasets and analyzethe sensitivity of various word embeddings with respect to the complexity ofthe sentences. We build a complex sentences dataset comprising of 50 sentencepairs with associated semantic similarity values provided by 15 humanannotators. Readability analysis is performed to highlight the increase incomplexity of the sentences in the existing benchmark datasets and those in theproposed dataset. Further, we perform a comparative analysis of the performanceof various word embeddings and language models on the existing benchmarkdatasets and the proposed dataset. The results show the increase in complexityof the sentences has a significant impact on the performance of the embeddingmodels resulting in a 10-20% decrease in Pearson's and Spearman's correlation.

Comparative analysis of word embeddings in assessing semantic similarity of complex sentences