Supervised Learning of Universal Sentence Representations from Natural Language Inference Data
Alexis Conneau, Douwe Kiela, Holger Schwenk, Loïc Barrault, Antoine Bordes
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Many modern NLP systems rely on word embeddings, previously trained in an unsupervised manner on large corpora, as base features. Efforts to obtain embeddings for larger chunks of text, such as sentences, have however not been so successful. Several attempts at learning unsupervised representations of sentences have not reached satisfactory enough performance to be widely adopted. 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. Much like how computer vision uses ImageNet to obtain features, which can then be transferred to other tasks, our work tends to indicate the suitability of natural language inference for transfer learning to other NLP tasks. Our encoder is publicly available.
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Many modern NLP systems rely on word embeddings, previously trained in an unsupervised manner on large corpora, as base features.
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Research context
127
Citations
36
References
Tasks
Computer science, Inference, Sentence, Transfer of learning, Unsupervised learning, Word (group theory), Natural language, Encoder
Methods
Transformer
Domains
Artificial intelligence, Natural language processing, Machine learning
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