[PDF] Top 20 Supervised Learning of Universal Sentence Representations from Natural Language Inference Data
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Supervised Learning of Universal Sentence Representations from Natural Language Inference Data
... 433K sentence pairs, MultiNLI improves upon SNLI in its coverage: it contains ten distinct genres of written and spoken English, covering most of the complexity of the ...our sentence encoder on other ... See full document
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InferLite: Simple Universal Sentence Representations from Natural Language Inference Data
... deviates from InferSent in that it does not use any recurrent con- nections and can generalize to multiple pre-trained word ...Glove representations (Pennington et ...to learning generic binary ... See full document
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Learning Universal Sentence Representations with Mean Max Attention Autoencoder
... learn universal sentence repre- sentations, previous methods focus on com- plex recurrent neural networks or supervised ...unlabelled data, we obtain high- quality representations of ... See full document
10
Learning Natural Language Inference with LSTM
... ford Natural Language Inference (SNLI) corpus for the purpose of encouraging more learning-centered approaches to ...570K sentence pairs with three labels: entailment, contradiction and ... See full document
10
Semantic Sentence Matching with Densely-Connected Recurrent and Co-Attentive Information
... the natural language inference task over SNLI and MultiNLI ...word representations from language models as an ex- ternel ... See full document
8
SentEval: An Evaluation Toolkit for Universal Sentence Representations
... provided sentence represen- tations ...tence representations. For the natural language inference tasks, where we are given two sentences u and v, we pro- vide the classifier with the ... See full document
6
Learning Condensed Feature Representations from Large Unsupervised Data Sets for Supervised Learning
... decade, supervised learning has become a standard way to train the models of many natural language processing (NLP) ...unsupervised data to supplement supervised ...word ... See full document
6
Discourse Marker Augmented Network with Reinforcement Learning for Natural Language Inference
... Natural Language Inference (NLI), also known as Recognizing Textual Entailment (RTE), is one of the most important prob- lems in natural language ...knowledge from some important ... See full document
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The RepEval 2017 Shared Task: Multi Genre Natural Language Inference with Sentence Representations
... network sentence represen- tation learning models on the Multi- Genre Natural Language Inference cor- pus (MultiNLI) recently introduced by Williams et ...tract sentence features ... See full document
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A large annotated corpus for learning natural language inference
... in language can be reduced to questions of entailment and contradiction in ...of sentence pairs labeled for entailment, contradiction, and ...the representations learned by a neural network model on ... See full document
11
Towards Generalizable Sentence Embeddings
... transfer learning as well as zero-shot and one-shot ...transfer from the task of natural language inference to paraphrase detection and paraphrase ...labelled data is in- ... See full document
10
Learning Representations for Weakly Supervised Natural Language Processing Tasks
... task data for source-domain labeled data (Sections 15–18 of the WSJ portion of the Penn Treebank, labeled with chunk tags) (Tjong, Sang, and Buchholz ...test data, we used biochemistry journal ... See full document
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Using Soft Constraints in Joint Inference for Clinical Concept Recognition
... Roth and Yih (2004, 2007) suggested the use of integer programs to model joint inference in a fully supervised setting. Our paper follows their concep- tual approach. However, they used only hard con- ... See full document
7
Learning Structured Natural Language Representations for Semantic Parsing
... intermediate representations for the test instances and evaluated the learned represen- tations against ...predicted representations and the human ...predicted representations have the same structure ... See full document
12
Dual Supervised Learning for Natural Language Understanding and Generation
... estimate data distri- bution, the data properties should be considered, because different data types have different struc- tural ...example, natural language has sequential structures ... See full document
6
Annotation Artifacts in Natural Language Inference Data
... Neutral. Modifiers (tall, sad, popular) and su- perlatives (first, favorite, most) are affiliated with the neutral class. These modifiers are perhaps a product of a simple strategy for introducing in- formation that is ... See full document
6
Proceedings of the Eighth Joint Conference on Lexical and Computational Semantics (*SEM 2019)
... task-independent language understanding: building machine learning models that can learn to do most of the hard work of language understanding before they see a single example of the language ... See full document
16
Medical application using nlp over cloud
... POS tagger: also called the parts of speech tagger is used to tag each word of the sentence input with the corresponding parts of speech. Tagging of each part-of-speech is more difficult than just having a list of ... See full document
5
Natural Language Inference from Multiple Premises
... LSTM In our experiments, we found that the conditional LSTM (Hochreiter and Schmidhuber, 1997) model of Rockt¨aschel et al. (2016) outper- formed a Siamese LSTM network (e.g. Bow- man et al. (2015)), so we report results ... See full document
10
Supervised and Unsupervised Learning for Sentence Compression
... (compressed) sentence is simply its prob- ability as a ...sentences from which to create useful language models, so we both make this simplifying assump- ... See full document
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