Rethinking Skip-thought: A Neighborhood based Approach

Shuai Tang, Hailin Jin, Chen Fang, Zhaowen Wang, Virginia de Sa


Abstract
We study the skip-thought model with neighborhood information as weak supervision. More specifically, we propose a skip-thought neighbor model to consider the adjacent sentences as a neighborhood. We train our skip-thought neighbor model on a large corpus with continuous sentences, and then evaluate the trained model on 7 tasks, which include semantic relatedness, paraphrase detection, and classification benchmarks. Both quantitative comparison and qualitative investigation are conducted. We empirically show that, our skip-thought neighbor model performs as well as the skip-thought model on evaluation tasks. In addition, we found that, incorporating an autoencoder path in our model didn’t aid our model to perform better, while it hurts the performance of the skip-thought model.
Anthology ID:
W17-2625
Volume:
Proceedings of the 2nd Workshop on Representation Learning for NLP
Month:
August
Year:
2017
Address:
Vancouver, Canada
Venue:
RepL4NLP
SIG:
SIGREP
Publisher:
Association for Computational Linguistics
Note:
Pages:
211–218
Language:
URL:
https://aclanthology.org/W17-2625
DOI:
10.18653/v1/W17-2625
Bibkey:
Cite (ACL):
Shuai Tang, Hailin Jin, Chen Fang, Zhaowen Wang, and Virginia de Sa. 2017. Rethinking Skip-thought: A Neighborhood based Approach. In Proceedings of the 2nd Workshop on Representation Learning for NLP, pages 211–218, Vancouver, Canada. Association for Computational Linguistics.
Cite (Informal):
Rethinking Skip-thought: A Neighborhood based Approach (Tang et al., RepL4NLP 2017)
Copy Citation:
PDF:
https://preview.aclanthology.org/ingestion-script-update/W17-2625.pdf
Poster:
 W17-2625.Poster.pdf
Data
BookCorpusMPQA Opinion CorpusSICK