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A Joint Many-Task Model: Growing a Neural Network for Multiple NLP Tasks
Transfer and multi-task learning have traditionally focused on either a single source-target pair or very few, similar tasks. Ideally, the linguistic levels of morphology, syntax and semantics wouldExpand
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Tree-to-Sequence Attentional Neural Machine Translation
Most of the existing Neural Machine Translation (NMT) models focus on the conversion of sequential data and do not directly use syntactic information. We propose a novel end-to-end syntactic NMTExpand
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Find or Classify? Dual Strategy for Slot-Value Predictions on Multi-Domain Dialog State Tracking
Dialog State Tracking (DST) is a core component in task-oriented dialog systems. Existing approaches for DST usually fall into two categories, i.e, the picklist-based and span-based. From one hand,Expand
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Learning to Retrieve Reasoning Paths over Wikipedia Graph for Question Answering
Answering questions that require multi-hop reasoning at web-scale necessitates retrieving multiple evidence documents, one of which often has little lexical or semantic relationship to the question.Expand
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Simple Customization of Recursive Neural Networks for Semantic Relation Classification
In this paper, we present a recursive neural network (RNN) model that works on a syntactic tree. Our model differs from previous RNN models in that the model allows for an explicit weighting ofExpand
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Topic detection using paragraph vectors to support active learning in systematic reviews
Graphical abstract
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Task-Oriented Learning of Word Embeddings for Semantic Relation Classification
We present a novel learning method for word embeddings designed for relation classification. Our word embeddings are trained by predicting words between noun pairs using lexical relation-specificExpand
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Jointly Learning Word Representations and Composition Functions Using Predicate-Argument Structures
We introduce a novel compositional language model that works on PredicateArgument Structures (PASs). Our model jointly learns word representations and their composition functions using bagof-wordsExpand
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Multilingual Extractive Reading Comprehension by Runtime Machine Translation
Despite recent work in Reading Comprehension (RC), progress has been mostly limited to English due to the lack of large-scale datasets in other languages. In this work, we introduce the first RCExpand
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Neural Machine Translation with Source-Side Latent Graph Parsing
This paper presents a novel neural machine translation model which jointly learns translation and source-side latent graph representations of sentences. Unlike existing pipelined approaches usingExpand
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