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Neural Relation Extraction with Selective Attention over Instances
TLDR
A sentence-level attention-based model for relation extraction that employs convolutional neural networks to embed the semantics of sentences and dynamically reduce the weights of those noisy instances. Expand
Modeling Relation Paths for Representation Learning of Knowledge Bases
TLDR
This model considers relation paths as translations between entities for representation learning, and addresses two key challenges: (1) Since not all relation paths are reliable, it design a path-constraint resource allocation algorithm to measure the reliability of relation paths and (2) represents relation paths via semantic composition of relation embeddings. Expand
Representation Learning of Knowledge Graphs with Entity Descriptions
TLDR
Experimental results on real-world datasets show that, the proposed novel RL method for knowledge graphs outperforms other baselines on the two tasks, especially under the zero-shot setting, which indicates that the method is capable of building representations for novel entities according to their descriptions. Expand
Joint Learning of Character and Word Embeddings
TLDR
A character-enhanced word embedding model (CWE) is presented to address the issues of character ambiguity and non-compositional words, and the effectiveness of CWE on word relatedness computation and analogical reasoning is evaluated. Expand
Improving the Transformer Translation Model with Document-Level Context
TLDR
This work extends the Transformer model with a new context encoder to represent document-level context, which is then incorporated into the original encoder and decoder, and introduces a two-step training method to take full advantage of abundant sentence-level parallel corpora and limited document- level parallel Corpora. Expand
Adversarial Training for Unsupervised Bilingual Lexicon Induction
TLDR
This work shows that cross-lingual connection can actually be established without any form of supervision, by formulating the problem as a natural adversarial game, and investigating techniques that are crucial to successful training. Expand
Earth Mover’s Distance Minimization for Unsupervised Bilingual Lexicon Induction
TLDR
This paper proposes to minimize their earth mover’s distance, a measure of divergence between distributions, by viewing word embedding spaces as distributions, and demonstrates the success on the unsupervised bilingual lexicon induction task. Expand
Discrete Collaborative Filtering
TLDR
This paper proposes a principled CF hashing framework called Discrete Collaborative Filtering (DCF), which directly tackles the challenging discrete optimization that should have been treated adequately in hashing, and devise a computationally efficient algorithm with a rigorous convergence proof of DCF. Expand
Visualizing and Understanding Neural Machine Translation
TLDR
This work proposes to use layer-wise relevance propagation (LRP) to compute the contribution of each contextual word to arbitrary hidden states in the attention-based encoder-decoder framework and shows that visualization with LRP helps to interpret the internal workings of NMT and analyze translation errors. Expand
Image-embodied Knowledge Representation Learning
TLDR
Experimental results demonstrate that the proposed Image-embodied Knowledge Representation Learning models outperform all baselines on both tasks, which indicates the significance of visual information for knowledge representations and the capability of the models in learning knowledge representations with images. Expand
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