Sentence
3396 papers with code • 0 benchmarks • 0 datasets
Benchmarks
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Libraries
Use these libraries to find Sentence models and implementationsMost implemented papers
Neural Machine Translation by Jointly Learning to Align and Translate
Neural machine translation is a recently proposed approach to machine translation.
Convolutional Neural Networks for Sentence Classification
PaddlePaddle/PaddleNLP
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EMNLP 2014
We report on a series of experiments with convolutional neural networks (CNN) trained on top of pre-trained word vectors for sentence-level classification tasks.
Show and Tell: A Neural Image Caption Generator
karpathy/neuraltalk
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CVPR 2015
Experiments on several datasets show the accuracy of the model and the fluency of the language it learns solely from image descriptions.
Sequence to Sequence Learning with Neural Networks
bentrevett/pytorch-seq2seq
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NeurIPS 2014
Our method uses a multilayered Long Short-Term Memory (LSTM) to map the input sequence to a vector of a fixed dimensionality, and then another deep LSTM to decode the target sequence from the vector.
Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks
UKPLab/sentence-transformers
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IJCNLP 2019
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.
A Structured Self-attentive Sentence Embedding
jadore801120/attention-is-all-you-need-pytorch
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This paper proposes a new model for extracting an interpretable sentence embedding by introducing self-attention.
ALBERT: A Lite BERT for Self-supervised Learning of Language Representations
google-research/ALBERT
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ICLR 2020
Increasing model size when pretraining natural language representations often results in improved performance on downstream tasks.
Language Models are Few-Shot Learners
By contrast, humans can generally perform a new language task from only a few examples or from simple instructions - something which current NLP systems still largely struggle to do.
Effective Approaches to Attention-based Neural Machine Translation
philipperemy/keras-attention-mechanism
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EMNLP 2015
Our ensemble model using different attention architectures has established a new state-of-the-art result in the WMT'15 English to German translation task with 25. 9 BLEU points, an improvement of 1. 0 BLEU points over the existing best system backed by NMT and an n-gram reranker.
Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation
NVIDIA/DeepLearningExamples
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To improve parallelism and therefore decrease training time, our attention mechanism connects the bottom layer of the decoder to the top layer of the encoder.
