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 dataset
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.

Source: http://arxiv.org/abs/1705.02364v1

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