@inproceedings{shi-etal-2018-tree,
title = "On Tree-Based Neural Sentence Modeling",
author = "Shi, Haoyue and
Zhou, Hao and
Chen, Jiaze and
Li, Lei",
editor = "Riloff, Ellen and
Chiang, David and
Hockenmaier, Julia and
Tsujii, Jun{'}ichi",
booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
month = oct # "-" # nov,
year = "2018",
address = "Brussels, Belgium",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D18-1492",
doi = "10.18653/v1/D18-1492",
pages = "4631--4641",
abstract = "Neural networks with tree-based sentence encoders have shown better results on many downstream tasks. Most of existing tree-based encoders adopt syntactic parsing trees as the explicit structure prior. To study the effectiveness of different tree structures, we replace the parsing trees with trivial trees (i.e., binary balanced tree, left-branching tree and right-branching tree) in the encoders. Though trivial trees contain no syntactic information, those encoders get competitive or even better results on all of the ten downstream tasks we investigated. This surprising result indicates that explicit syntax guidance may not be the main contributor to the superior performances of tree-based neural sentence modeling. Further analysis show that tree modeling gives better results when crucial words are closer to the final representation. Additional experiments give more clues on how to design an effective tree-based encoder. Our code is open-source and available at \url{https://github.com/ExplorerFreda/TreeEnc}.",
}
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<abstract>Neural networks with tree-based sentence encoders have shown better results on many downstream tasks. Most of existing tree-based encoders adopt syntactic parsing trees as the explicit structure prior. To study the effectiveness of different tree structures, we replace the parsing trees with trivial trees (i.e., binary balanced tree, left-branching tree and right-branching tree) in the encoders. Though trivial trees contain no syntactic information, those encoders get competitive or even better results on all of the ten downstream tasks we investigated. This surprising result indicates that explicit syntax guidance may not be the main contributor to the superior performances of tree-based neural sentence modeling. Further analysis show that tree modeling gives better results when crucial words are closer to the final representation. Additional experiments give more clues on how to design an effective tree-based encoder. Our code is open-source and available at https://github.com/ExplorerFreda/TreeEnc.</abstract>
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%0 Conference Proceedings
%T On Tree-Based Neural Sentence Modeling
%A Shi, Haoyue
%A Zhou, Hao
%A Chen, Jiaze
%A Li, Lei
%Y Riloff, Ellen
%Y Chiang, David
%Y Hockenmaier, Julia
%Y Tsujii, Jun’ichi
%S Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
%D 2018
%8 oct nov
%I Association for Computational Linguistics
%C Brussels, Belgium
%F shi-etal-2018-tree
%X Neural networks with tree-based sentence encoders have shown better results on many downstream tasks. Most of existing tree-based encoders adopt syntactic parsing trees as the explicit structure prior. To study the effectiveness of different tree structures, we replace the parsing trees with trivial trees (i.e., binary balanced tree, left-branching tree and right-branching tree) in the encoders. Though trivial trees contain no syntactic information, those encoders get competitive or even better results on all of the ten downstream tasks we investigated. This surprising result indicates that explicit syntax guidance may not be the main contributor to the superior performances of tree-based neural sentence modeling. Further analysis show that tree modeling gives better results when crucial words are closer to the final representation. Additional experiments give more clues on how to design an effective tree-based encoder. Our code is open-source and available at https://github.com/ExplorerFreda/TreeEnc.
%R 10.18653/v1/D18-1492
%U https://aclanthology.org/D18-1492
%U https://doi.org/10.18653/v1/D18-1492
%P 4631-4641
Markdown (Informal)
[On Tree-Based Neural Sentence Modeling](https://aclanthology.org/D18-1492) (Shi et al., EMNLP 2018)
ACL
- Haoyue Shi, Hao Zhou, Jiaze Chen, and Lei Li. 2018. On Tree-Based Neural Sentence Modeling. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 4631–4641, Brussels, Belgium. Association for Computational Linguistics.