# Semantic Similarity Edit

77 papers with code · Natural Language Processing

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# Improving Language Understanding by Generative Pre-Training

We demonstrate that large gains on these tasks can be realized by generative pre-training of a language model on a diverse corpus of unlabeled text, followed by discriminative fine-tuning on each specific task.

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# A Hybrid Neural Network Model for Commonsense Reasoning

An HNN consists of two component models, a masked language model and a semantic similarity model, which share a BERT-based contextual encoder but use different model-specific input and output layers.

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# Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks

However, it requires that both sentences are fed into the network, which causes a massive computational overhead: Finding the most similar pair in a collection of 10, 000 sentences requires about 50 million inference computations (~65 hours) with BERT.

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# ERNIE: Enhanced Representation through Knowledge Integration

19 Apr 2019nghuyong/ERNIE-Pytorch

We present a novel language representation model enhanced by knowledge called ERNIE (Enhanced Representation through kNowledge IntEgration).

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# No Fuss Distance Metric Learning using Proxies

Traditionally, for this problem supervision is expressed in the form of sets of points that follow an ordinal relationship -- an anchor point $x$ is similar to a set of positive points $Y$, and dissimilar to a set of negative points $Z$, and a loss defined over these distances is minimized.

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# Photographic Text-to-Image Synthesis with a Hierarchically-nested Adversarial Network

This paper presents a novel method to deal with the challenging task of generating photographic images conditioned on semantic image descriptions.

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# Portuguese Word Embeddings: Evaluating on Word Analogies and Natural Language Tasks

Word embeddings have been found to provide meaningful representations for words in an efficient way; therefore, they have become common in Natural Language Processing sys- tems.

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# Counter-fitting Word Vectors to Linguistic Constraints

In this work, we present a novel counter-fitting method which injects antonymy and synonymy constraints into vector space representations in order to improve the vectors' capability for judging semantic similarity.

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# Augmenting Neural Response Generation with Context-Aware Topical Attention

Our model is built upon the basic Seq2Seq model by augmenting it with a hierarchical joint attention mechanism that incorporates topical concepts and previous interactions into the response generation.

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# Large-Scale Multi-Domain Belief Tracking with Knowledge Sharing

Robust dialogue belief tracking is a key component in maintaining good quality dialogue systems.

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