Sentence
3422 papers with code • 0 benchmarks • 0 datasets
Benchmarks
These leaderboards are used to track progress in Sentence
Libraries
Use these libraries to find Sentence models and implementationsMost implemented papers
Bidirectional LSTM-CRF Models for Sequence Tagging
It can also use sentence level tag information thanks to a CRF layer.
CIDEr: Consensus-based Image Description Evaluation
We propose a novel paradigm for evaluating image descriptions that uses human consensus.
Universal Sentence Encoder
facebookresearch/InferSent
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For both variants, we investigate and report the relationship between model complexity, resource consumption, the availability of transfer task training data, and task performance.
SimCSE: Simple Contrastive Learning of Sentence Embeddings
princeton-nlp/SimCSE
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EMNLP 2021
This paper presents SimCSE, a simple contrastive learning framework that greatly advances state-of-the-art sentence embeddings.
Supervised Learning of Universal Sentence Representations from Natural Language Inference Data
facebookresearch/InferSent
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EMNLP 2017
Many modern NLP systems rely on word embeddings, previously trained in an unsupervised manner on large corpora, as base features.
A Neural Conversational Model
We find that this straightforward model can generate simple conversations given a large conversational training dataset.
A Sensitivity Analysis of (and Practitioners' Guide to) Convolutional Neural Networks for Sentence Classification
brightmart/text_classification
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IJCNLP 2017
Convolutional Neural Networks (CNNs) have recently achieved remarkably strong performance on the practically important task of sentence classification (kim 2014, kalchbrenner 2014, johnson 2014).
Show and Tell: Lessons learned from the 2015 MSCOCO Image Captioning Challenge
Automatically describing the content of an image is a fundamental problem in artificial intelligence that connects computer vision and natural language processing.
Text Summarization with Pretrained Encoders
nlpyang/PreSumm
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IJCNLP 2019
For abstractive summarization, we propose a new fine-tuning schedule which adopts different optimizers for the encoder and the decoder as a means of alleviating the mismatch between the two (the former is pretrained while the latter is not).
Generating Sentences from a Continuous Space
PaddlePaddle/PaddleNLP
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CONLL 2016
The standard recurrent neural network language model (RNNLM) generates sentences one word at a time and does not work from an explicit global sentence representation.
