In this paper, we show how universal sentence representations trained using the supervised data of the Stanford Natural Language Inference dataset can consistently outperform unsupervised methods like SkipThought vectors (Kiros et al., 2015) on a wide range of transfer tasks. Supervised Learning of Universal Sentence Representations from Natural Language Inference Data (EMNLP 2017) [1] A. Conneau, D. Kiela, H. Schwenk, L. Barrault, A. Bordes, Supervised Learning of Universal Sentence Representations from Natural Language Inference Data @InProceedings{conneau …

Several attempts at learning unsupervised representations of sentences have not reached satisfactory enough performance to be widely adopted. Request PDF | Supervised Learning of Universal Sentence Representations from Natural Language Inference Data | Many modern NLP systems rely … This is a brief summary of paper for me to study and organize it, Supervised Learning of Universal Sentence Representations from Natural Language Inference Data.Conneau et al. Self Supervised Representation Learning in NLP 5 minute read While Computer Vision is making amazing progress on self-supervised learning only in the last few years, self-supervised learning has been a first-class citizen in NLP research for quite a while.

Reference. Please consider citing if you found this code useful.. Supervised Learning of Universal Sentence Representations from Natural Language Inference Data Alexis Conneau, Douwe Kiela, Holger Schwenk, Loïc Barrault, and Antoine Bordes EMNLP 2017, pages 681-691. Supervised Learning of Universal Sentence Representations from Natural Language Inference Data 1. In this paper, we show how universal sentence representations trained using the supervised data of the Stanford Natural Language Inference datasets can consistently outperform unsupervised methods like SkipThought vectors (Kiros et al., 2015) on a wide range of transfer tasks. Bibliographic details on Supervised Learning of Universal Sentence Representations from Natural Language Inference Data.

We hypothesize that the semantic nature of NLI makes it a good candidate for learning universal sentence embeddings in a supervised way. EMNLP 2017 • facebookresearch/InferSent • Many modern NLP systems rely on word embeddings, previously trained in an unsupervised manner on large corpora, as base features. In this paper, we show how universal sentence representations trained using the supervised data of the Stanford Natural Language Inference datasets can consistently outperform unsupervised methods like SkipThought vectors on a wide range of transfer tasks.