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AAAI 2021 Workshop on Towards Robust, Secure and Efficient Machine Learning2021Adversarial attack on question answering systems over tabular data (TableQA) can help evaluate to what extent TableQA systems can understand natural language questions and reason with tables. However, generating natural language adversarial questions is difficult, because even a single character swap could lead to huge semantic difference in human perception. In this paper, we propose SAGE (Semantically
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NewSum EMNLP 2021 Workshop on New Frontiers in Summarization2021We consider the problem of topic-focused abstractive summarization, where the goal is to generate an abstractive summary focused on a particular topic, a phrase of one or multiple words. We hypothesize that the task of generating topic-focused summaries can be improved by showing the model what it must not focus on. We introduce a deep reinforcement learning approach to topic-focused abstractive summarization
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IEEE BigData 20212021Natural language understanding (NLU) is one of the most critical components in goal-oriented dialog systems and enables innovative Big Data applications such as intelligent voice assistants (IVA) and chatbots. While recent advances in deep learning-based NLU models have achieved significant improvements in terms of accuracy, most existing works are monolingual or bilingual. In this work, we propose and
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Information Retrieval Journal2021A key application of conversational search is reining a user’s search intent by asking a series of clarification questions, aiming to improve the relevance of search results. Training and evaluating such conversational systems currently requires human participation, making it infeasible to examine a wide range of user behaviors. To support robust training/evaluation of such systems, we propose a simulation
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MDPI Applied Sciences2021Open-book question answering is a subset of question answering (QA) tasks where the system aims to find answers in a given set of documents (open-book) and common knowledge about a topic. This article proposes a solution for answering natural language questions from a corpus of Amazon Web Services (AWS) technical documents with no domain-specific labeled data (zero-shot). These questions have a yes–no–none
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