# call for papers

PIE-QG: Paraphrased Information Extraction for Unsupervised Question Generation from Small Corpora » Supervised Question Answering systems (QA systems) rely on domain-specific human-labeled data for training. Unsupervised QA systems generate their own question-answer training pairs, typically using secondary knowledge sources to achieve this outcome. Our approach (called PIE-QG) uses Open Information Extraction (OpenIE) to generate synthetic training questions from paraphrased passages and uses the question-answer pairs as training data for a language model for a state-of-the-art QA system based on BERT. Triples in the form of <subject, predicate, object> are extracted from each passage, and questions are formed with subjects (or objects) and predicates while objects (or subjects) are considered as answers. Experimenting on five extractive QA datasets demonstrates that our technique achieves on-par performance with existing state-of-the-art QA systems with the benefit of being trained on an order of magnitude fewer documents and without any recourse to external reference data sources. Arxiv.org

QG-net | Proceedings of the Fifth Annual ACM Conference on Learning at Scale » Jun 26, 2018… This paper introduces QG-Net, a recurrent neural network-based model specifically designed for automatically generating quiz questions from … Dl.acm.org

Bridging the Gap Between Indexing and Retrieval for Differentiable Search Index with Query Generation » The Differentiable Search Index (DSI) is an emerging paradigm for information retrieval. Unlike traditional retrieval architectures where index and retrieval are two different and separate components, DSI uses a single transformer model to perform both indexing and retrieval. In this paper, we identify and tackle an important issue of current DSI models: the data distribution mismatch that occurs between the DSI indexing and retrieval processes. Specifically, we argue that, at indexing, current DSI methods learn to build connections between the text of long documents and the identifier of the documents, but then retrieval of document identifiers is based on queries that are commonly much shorter than the indexed documents. This problem is further exacerbated when using DSI for cross-lingual retrieval, where document text and query text are in different languages. To address this fundamental problem of current DSI models, we propose a simple yet effective indexing framework for DSI, called DSI-QG. When ind Arxiv.org

Graduation Questionnaire (GQ) » All Schools Summary Reports present national data from the GQ, combining student responses from all participating medical schools into a single report. Aamc.org

Papers with Code - Question Generation » The goal of **Question Generation** is to generate a valid and fluent question according to a given passage and the target answer. Question Generation can be used in many scenarios, such as automatic tutoring systems, improving the performance of Question Answering models and enabling chatbots to lead a conversation. <span class="description-source">Source: [Generating Highly Relevant Questions ](https://arxiv.org/abs/1910.03401)</span> Paperswithcode.com