We introduce SciSpace Copilot, an AI research assistant that helps in understanding and reading research papers faster by providing a plethora of features. Answering questions from a document has recently become popular using the Retrieval Augmented Generation (RAG) approach. Our tool uses an advanced question-answering pipeline to get accurate answers and also provide exact citations for the same. We provide many more valuable features on scientific text, including generating explanations, generating summaries, adding notes and highlights, and finding related papers from our 200 million corpus. Our tool supports 100+ languages, making research more accessible across language barriers. Thousands of users use SciSpace Copilot on a daily basis by uploading their articles to understand research faster and better. Our tool can be accessed at this link: https://typeset.io.

Social media such as Twitter has provided a platform for users to gather and share information and stay updated with the news. However, restriction on the length, informal grammar and vocabulary of the posts pose challenges to perform classification from textual content alone. We propose models based on the Hawkes process (HP) which can naturally incorporate additional cues such as the temporal features and past labels of the posts, along with the textual features for improving short text classification. In particular, we propose a discriminative approach to model text in HP, where the text features parameterize the base intensity and the triggering kernel of the intensity function. This allows textual content to determine influence from past posts and consequently determine the intensity function and class label. Another major contribution is to model the kernel as a neural network function of both time and text, permitting more complex influence functions for Hawkes process. This will maintain the interpretability of Hawkes process models along with the improved function learning capability of the neural networks. The proposed HP models can easily consider pretrained word embeddings to represent text for classification. Experiments on the rumour stance classification problems in social media demonstrate the effectiveness of the proposed HP models.

There has been a massive rise in the use of Community Question and Answering (CQA) forums to get solutions to various technical and non-technical queries. One common problem faced in CQA is the small number of experts, which leaves many questions unanswered. This paper addresses the challenging problem of predicting the best answerer for a new question and thereby recommending the best expert for the same. Although there are work in the literature that aim to find possible answerers for questions posted in CQA, very few algorithms exist for finding the best answerer whose answer will satisfy the information need of the original Poster. For finding answerers, existing approaches mostly use features based on content and tags associated with the questions. There are few approaches that additionally consider the users' history. In this paper, we propose an approach that considers a comprehensive set of features including but not limited to text representation, tag based similarity as well as multiple user-based features that target users' availability, agility as well as expertise for predicting the best answerer for a given question. We also include features that give incentives to users who answer less but more important questions over those who answer a lot of questions of less importance. A learning to rank algorithm is used to find the weight of each feature. Experiments conducted on a real dataset from Stack Exchange show the efficacy of the proposed method in terms of multiple evaluation metrics for accuracy, robustness and real time performance.