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Annotation Artifacts in Natural Language Inference Data
Large-scale datasets for natural language inference are created by presenting crowd workers with a sentence (premise), and asking them to generate three new sentences (hypotheses) that it entails,Expand
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DyNet: The Dynamic Neural Network Toolkit
We describe DyNet, a toolkit for implementing neural network models based on dynamic declaration of network structure. In the static declaration strategy that is used in toolkits like Theano, CNTK,Expand
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A Dependency Parser for Tweets
We describe a new dependency parser for English tweets, TWEEBOPARSER. The parser builds on several contributions: new syntactic annotations for a corpus of tweets (TWEEBANK), with conventionsExpand
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Don't Stop Pretraining: Adapt Language Models to Domains and Tasks
Language models pretrained on text from a wide variety of sources form the foundation of today's NLP. In light of the success of these broad-coverage models, we investigate whether it is stillExpand
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Frame-Semantic Parsing with Softmax-Margin Segmental RNNs and a Syntactic Scaffold
We present a new, efficient frame-semantic parser that labels semantic arguments to FrameNet predicates. Built using an extension to the segmental RNN that emphasizes recall, our basic systemExpand
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Syntactic Scaffolds for Semantic Structures
We introduce the syntactic scaffold, an approach to incorporating syntactic information into semantic tasks. Syntactic scaffolds avoid expensive syntactic processing at runtime, only making use of aExpand
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Greedy, Joint Syntactic-Semantic Parsing with Stack LSTMs
We present a transition-based parser that jointly produces syntactic and semantic dependencies. It learns a representation of the entire algorithm state, using stack long short-term memories. OurExpand
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Learning Joint Semantic Parsers from Disjoint Data
We present a new approach to learning semantic parsers from multiple datasets, even when the target semantic formalisms are drastically different, and the underlying corpora do not overlap. We handleExpand
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Adversarial Filters of Dataset Biases
Large neural models have demonstrated human-level performance on language and vision benchmarks such as ImageNet and Stanford Natural Language Inference (SNLI). Yet, their performance degradesExpand
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Polyglot Semantic Role Labeling
Previous approaches to multilingual semantic dependency parsing treat languages independently, without exploiting the similarities between semantic structures across languages. We experiment with aExpand
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