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DeepPath: A Reinforcement Learning Method for Knowledge Graph Reasoning
TLDR
A novel reinforcement learning framework for learning multi-hop relational paths is described, which uses a policy-based agent with continuous states based on knowledge graph embeddings, which reasons in a KG vector-space by sampling the most promising relation to extend its path. Expand
"Liar, Liar Pants on Fire": A New Benchmark Dataset for Fake News Detection
TLDR
This paper presents liar: a new, publicly available dataset for fake news detection, and designs a novel, hybrid convolutional neural network to integrate meta-data with text to improve a text-only deep learning model. Expand
VaTeX: A Large-Scale, High-Quality Multilingual Dataset for Video-and-Language Research
TLDR
This work presents a new large-scale multilingual video description dataset, VATEX, which contains over 41,250 videos and 825,000 captions in both English and Chinese and demonstrates that the spatiotemporal video context can be effectively utilized to align source and target languages and thus assist machine translation. Expand
Reinforced Cross-Modal Matching and Self-Supervised Imitation Learning for Vision-Language Navigation
TLDR
A novel Reinforced Cross-Modal Matching (RCM) approach that enforces cross-modal grounding both locally and globally via reinforcement learning (RL), and a Self-Supervised Imitation Learning (SIL) method to explore unseen environments by imitating its own past, good decisions is introduced. Expand
TabFact: A Large-scale Dataset for Table-based Fact Verification
TLDR
A large-scale dataset with 16k Wikipedia tables as the evidence for 118k human-annotated natural language statements, which are labeled as either ENTAILED or REFUTED is constructed and two different models are designed: Table-BERT and Latent Program Algorithm (LPA). Expand
No Metrics Are Perfect: Adversarial Reward Learning for Visual Storytelling
TLDR
Though automatic evaluation indicates slight performance boost over state-of-the-art (SOTA) methods in cloning expert behaviors, human evaluation shows that this approach achieves significant improvement in generating more human-like stories than SOTA systems. Expand
One-Shot Relational Learning for Knowledge Graphs
TLDR
This work proposes a one-shot relational learning framework, which utilizes the knowledge distilled by embedding models and learns a matching metric by considering both the learned embeddings and one-hop graph structures. Expand
Semantically Conditioned Dialog Response Generation via Hierarchical Disentangled Self-Attention
TLDR
A multi-layer hierarchical graph is exploited to build a hierarchical disentangled self-attention network, where each act is represented as a root-to-leaf route on the graph, and combinatorially many dialog act semantics can be modeled to control the neural response generation. Expand
Robust Distant Supervision Relation Extraction via Deep Reinforcement Learning
TLDR
A deep reinforcement learning strategy is explored to generate the false-positive indicator, where it is argued that incorrectly-labeled candidate sentences must be treated with a hard decision, rather than being dealt with soft attention weights. Expand
KBGAN: Adversarial Learning for Knowledge Graph Embeddings
TLDR
Inspired by generative adversarial networks (GANs), this framework uses one knowledge graph embedding model as a negative sample generator to assist the training of the desired model, which acts as the discriminator in GANs. Expand
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