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Extracting Scientific Figures with Distantly Supervised Neural Networks
Noah Siegel, Nicholas Lourie, Russell Power and Waleed AmmarJCDL • 2018 Non-textual components such as charts, diagrams and tables provide key information in many scientific documents, but the lack of large labeled datasets has impeded the development of data-driven methods for scientific figure extraction. In this paper, we…Natural Language to Structured Query Generation via Meta-Learning
Po-Sen Huang, Chenglong Wang, Rishabh Singh, Wen-tau Yih, Xiaodong HeNAACL • 2018 In conventional supervised training, a model is trained to fit all the training examples. However, having a monolithic model may not always be the best strategy, as examples could vary widely. In this work, we explore a different learning protocol that treats…Neural Motifs: Scene Graph Parsing with Global Context
Rowan Zellers, Mark Yatskar, Sam Thomson, Yejin ChoiCVPR • 2018 We investigate the problem of producing structured graph representations of visual scenes. Our work analyzes the role of motifs: regularly appearing substructures in scene graphs. We present new quantitative insights on such repeated structures in the Visual…Neural Poetry Translation
Marjan Ghazvininejad, Yejin Choi and Kevin KnightNAACL • 2018 We present the first neural poetry translation system. Unlike previous works that often fail to produce any translation for fixed rhyme and rhythm patterns, our system always translates a source text to an English poem. Human evaluation ranks translation…SeGAN: Segmenting and Generating the Invisible
Kiana Ehsani, Roozbeh Mottaghi, Ali FarhadiCVPR • 2018 Objects often occlude each other in scenes; Inferring their appearance beyond their visible parts plays an important role in scene understanding, depth estimation, object interaction and manipulation. In this paper, we study the challenging problem of…SoPa: Bridging CNNs, RNNs, and Weighted Finite-State Machines
Roy Schwartz, Sam Thomson and Noah A. SmithACL • 2018 Recurrent and convolutional neural networks comprise two distinct families of models that have proven to be useful for encoding natural language utterances. In this paper we present SoPa, a new model that aims to bridge these two approaches. SoPa combines…Structured Set Matching Networks for One-Shot Part Labeling
Jonghyun Choi, Jayant Krishnamurthy, Aniruddha Kembhavi, Ali FarhadiCVPR • 2018 Diagrams often depict complex phenomena and serve as a good test bed for visual and textual reasoning. However, understanding diagrams using natural image understanding approaches requires large training datasets of diagrams, which are very hard to obtain…Tracking State Changes in Procedural Text: A Challenge Dataset and Models for Process Paragraph Comprehension
Bhavana Dalvi, Lifu Huang, Niket Tandon, Wen-tau Yih, Peter ClarkNAACL • 2018 We present a new dataset and models for comprehending paragraphs about processes (e.g., photosynthesis), an important genre of text describing a dynamic world. The new dataset, ProPara, is the first to contain natural (rather than machine-generated) text…VISIR: Visual and Semantic Image Label Refinement
Sreyasi Nag Chowdhury, Niket Tandon, Hakan Ferhatosmanoglu, Gerhard WeikumWSDM • 2018 The social media explosion has populated the Internet with a wealth of images. There are two existing paradigms for image retrieval: 1)content-based image retrieval (BIR), which has traditionally used visual features for similarity search (e.g., SIFT features…What Happened? Leveraging VerbNet to Predict the Effects of Actions in Procedural Text
Peter Clark, Bhavana Dalvi, Niket TandonarXiv • 2018 Our goal is to answer questions about paragraphs describing processes (e.g., photosynthesis). Texts of this genre are challenging because the effects of actions are often implicit (unstated), requiring background knowledge and inference to reason about the…