Learning Universal Sentence Representations with Mean-Max Attention Autoencoder

Minghua Zhang, Yunfang Wu, Weikang Li, Wei Li


Abstract
In order to learn universal sentence representations, previous methods focus on complex recurrent neural networks or supervised learning. In this paper, we propose a mean-max attention autoencoder (mean-max AAE) within the encoder-decoder framework. Our autoencoder rely entirely on the MultiHead self-attention mechanism to reconstruct the input sequence. In the encoding we propose a mean-max strategy that applies both mean and max pooling operations over the hidden vectors to capture diverse information of the input. To enable the information to steer the reconstruction process dynamically, the decoder performs attention over the mean-max representation. By training our model on a large collection of unlabelled data, we obtain high-quality representations of sentences. Experimental results on a broad range of 10 transfer tasks demonstrate that our model outperforms the state-of-the-art unsupervised single methods, including the classical skip-thoughts and the advanced skip-thoughts+LN model. Furthermore, compared with the traditional recurrent neural network, our mean-max AAE greatly reduce the training time.
Anthology ID:
D18-1481
Volume:
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
Month:
October-November
Year:
2018
Address:
Brussels, Belgium
Venue:
EMNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
4514–4523
Language:
URL:
https://aclanthology.org/D18-1481
DOI:
10.18653/v1/D18-1481
Bibkey:
Cite (ACL):
Minghua Zhang, Yunfang Wu, Weikang Li, and Wei Li. 2018. Learning Universal Sentence Representations with Mean-Max Attention Autoencoder. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 4514–4523, Brussels, Belgium. Association for Computational Linguistics.
Cite (Informal):
Learning Universal Sentence Representations with Mean-Max Attention Autoencoder (Zhang et al., EMNLP 2018)
Copy Citation:
PDF:
https://aclanthology.org/D18-1481.pdf
Code
 Zminghua/SentEncoding
Data
BookCorpusMPQA Opinion CorpusSST