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Commonsense Knowledge in Machine Intelligence
Niket Tandon, Aparna S. Varde, Gerard de MeloSIGMOD Record • 2017 There is growing conviction that the future of computing depends on our ability to exploit big data on theWeb to enhance intelligent systems. This includes encyclopedic knowledge for factual details, common sense for human-like reasoning and natural language…Men Also Like Shopping: Reducing Gender Bias Amplification using Corpus-level Constraints
Jieyu Zhao, Tianlu Wang, Mark Yatskar, Vicente Ordóñez, Kai-Wei ChangEMNLP • 2017 Language is increasingly being used to define rich visual recognition problems with supporting image collections sourced from the web. Structured prediction models are used in these tasks to take advantage of correlations between co-occurring labels and…Answering Complex Questions Using Open Information Extraction
Tushar Khot, Ashish Sabharwal, and Peter ClarkACL • 2017 While there has been substantial progress in factoid question-answering (QA), answering complex questions remains challenging, typically requiring both a large body of knowledge and inference techniques. Open Information Extraction (Open IE) provides a way to…Learning a Neural Semantic Parser from User Feedback
Srinivasan Iyer, Ioannis Konstas, Alvin Cheung, Jayant Krishnamurthy, and Luke ZettlemoyerACL • 2017 We present an approach to rapidly and easily build natural language interfaces to databases for new domains, whose performance improves over time based on user feedback, and requires minimal intervention. To achieve this, we adapt neural sequence models to…WebChild 2.0: Fine-Grained Commonsense Knowledge Distillation
Niket Tandon, Gerard de Melo, and Gerhard WeikumACL • 2017 Despite important progress in the area of intelligent systems, most such systems still lack commonsense knowledge that appears crucial for enabling smarter, more human-like decisions. In this paper, we present a system based on a series of algorithms to…Automatic Selection of Context Configurations for Improved Class-Specific Word Representations
Ivan Vulic, Roy Schwartz, Ari Rappoport, Roi Reichart, and Anna KorhonenCoNLL • 2017 This paper is concerned with identifying contexts useful for training word representation models for different word classes such as adjectives (A), verbs (V), and nouns (N). We introduce a simple yet effective framework for an automatic selection of class…Crowdsourcing Multiple Choice Science Questions
Johannes Welbl, Nelson F. Liu, and Matt GardnerEMNLP • Workshop on Noisy User-generated Text • 2017 We present a novel method for obtaining high-quality, domain-targeted multiple choice questions from crowd workers. Generating these questions can be difficult without trading away originality, relevance or diversity in the answer options. Our method…Distilling Task Knowledge from How-To Communities
Cuong Xuan Chu, Niket Tandon, and Gerhard WeikumWWW • 2017 Knowledge graphs have become a fundamental asset for search engines. A fair amount of user queries seek information on problem-solving tasks such as building a fence or repairing a bicycle. However, knowledge graphs completely lack this kind of how-to…Domain-Targeted, High Precision Knowledge Extraction
Bhavana Dalvi, Niket Tandon, and Peter ClarkTACL • 2017 Our goal is to construct a domain-targeted, high precision knowledge base (KB), containing general (subject,predicate,object) statements about the world, in support of a downstream question-answering (QA) application. Despite recent advances in information…End-to-end Neural Coreference Resolution
Kenton Lee, Luheng He, Mike Lewis, and Luke ZettlemoyerEMNLP • 2017 We introduce the first end-to-end coreference resolution model and show that it significantly outperforms all previous work without using a syntactic parser or handengineered mention detector. The key idea is to directly consider all spans in a document as…