Sexism is prevalent in today's society, both offline and online, and poses a credible threat to social equality with respect to gender. According to ambivalent sexism theory (Glick and Fiske, 1996), it comes in two forms: Hostile and Benevolent. While hostile sexism is characterized by an explicitly negative attitude, benevolent sexism is more subtle. Previous works on computationally detecting sexism present online are restricted to identifying the hostile form. Our objective is to investigate the less pronounced form of sexism demonstrated online. We achieved this by creating and analyzing a dataset of tweets that exhibit benevolent sexism. We classified tweets into 'Hostile', 'Benevolent' or 'Others' class depending on the kind of sexism they exhibit, by using Support Vector Machines (SVM), sequenceto-sequence models and FastText classifier. We achieved the best F1-score using FastText classifier. Our work aims to analyze and understand the much prevalent ambivalent sexism in social media.

Recent advancements in deep learning techniques have transformed the area of semantic text matching. However, most state-of-the-art models are designed to operate with short documents such as tweets, user reviews, comments, etc. These models have fundamental limitations when applied to long-form documents such as scientific papers, legal documents, and patents. When handling such long documents, there are three primary challenges: (i) the presence of different contexts for the same word throughout the document, (ii) small sections of contextually similar text between two documents, but dissimilar text in the remaining parts (this defies the basic understanding of ”similarity”), and (iii) the coarse nature of a single global similarity measure which fails to capture the heterogeneity of the document content. In this paper, we describe CoLDE : Co ntrastive L ong D ocument E ncoder – a transformer-based framework that addresses these challenges and allows for interpretable comparisons of long documents. CoLDE uses unique positional embeddings and a multi-headed chunkwise attention layer in conjunction with a supervised contrastive learning framework to capture similarity at three different levels: (i) high-level similarity scores between a pair of documents, (ii) similarity scores between different sections within and across documents, and (iii) similarity scores between different chunks in the same document and across other documents. These fine-grained similarity scores aid in better interpretability. We evaluate CoLDE on three long document datasets namely, ACL Anthology publications, Wikipedia articles, and USPTO patents. Besides outperforming the state-of-the-art methods on the document matching task, CoLDE is also robust to changes in document length and text perturbations and provides interpretable results. The code for the proposed model is publicly available at https://github.com/InterDigitalInc/CoLDE.

Recent advancements in deep learning techniques have transformed the area of semantic text matching. However, most of the state-ofthe-art models are designed to operate with short documents such as tweets, user reviews, comments, etc., and have fundamental limitations when applied to long-form documents such as scientific papers, legal documents, and patents. When handling such long documents, there are three primary challenges: (i) The presence of different contexts for the same word throughout the document, (ii) Small sections of contextually similar text between two documents, but dissimilar text in the remaining parts -this defies the basic understanding of "similarity", and (iii) The coarse nature of a single global similarity measure which fails to capture the heterogeneity of the document content. In this paper, we describe CoLDE: Contrastive Long Document Encoder -a transformer-based framework that addresses these challenges and allows for interpretable comparisons of long documents. CoLDE uses unique positional embeddings and a multi-headed chunkwise attention layer in conjunction with a contrastive learning framework to capture similarity at three different levels: (i) high-level similarity scores between a pair of documents, (ii) similarity scores between different sections within and across documents, and (iii) similarity scores between different chunks in the same document and also other documents. These fine-grained similarity scores aid in better interpretability. We evaluate CoLDE on three long document datasets namely, ACL Anthology publications, Wikipedia articles, and USPTO patents. Besides outperforming the state-of-the-art methods on the document comparison task, CoLDE also proves interpretable and robust to changes in document length and text perturbations.

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