@inproceedings{tao-etal-2021-learning,
title = "Learning to Organize a Bag of Words into Sentences with Neural Networks: An Empirical Study",
author = "Tao, Chongyang and
Gao, Shen and
Li, Juntao and
Feng, Yansong and
Zhao, Dongyan and
Yan, Rui",
editor = "Toutanova, Kristina and
Rumshisky, Anna and
Zettlemoyer, Luke and
Hakkani-Tur, Dilek and
Beltagy, Iz and
Bethard, Steven and
Cotterell, Ryan and
Chakraborty, Tanmoy and
Zhou, Yichao",
booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jun,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.naacl-main.134",
doi = "10.18653/v1/2021.naacl-main.134",
pages = "1682--1691",
abstract = "Sequential information, a.k.a., orders, is assumed to be essential for processing a sequence with recurrent neural network or convolutional neural network based encoders. However, is it possible to encode natural languages without orders? Given a bag of words from a disordered sentence, humans may still be able to understand what those words mean by reordering or reconstructing them. Inspired by such an intuition, in this paper, we perform a study to investigate how {``}order{''} information takes effects in natural language learning. By running comprehensive comparisons, we quantitatively compare the ability of several representative neural models to organize sentences from a bag of words under three typical scenarios, and summarize some empirical findings and challenges, which can shed light on future research on this line of work.",
}
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%0 Conference Proceedings
%T Learning to Organize a Bag of Words into Sentences with Neural Networks: An Empirical Study
%A Tao, Chongyang
%A Gao, Shen
%A Li, Juntao
%A Feng, Yansong
%A Zhao, Dongyan
%A Yan, Rui
%Y Toutanova, Kristina
%Y Rumshisky, Anna
%Y Zettlemoyer, Luke
%Y Hakkani-Tur, Dilek
%Y Beltagy, Iz
%Y Bethard, Steven
%Y Cotterell, Ryan
%Y Chakraborty, Tanmoy
%Y Zhou, Yichao
%S Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
%D 2021
%8 June
%I Association for Computational Linguistics
%C Online
%F tao-etal-2021-learning
%X Sequential information, a.k.a., orders, is assumed to be essential for processing a sequence with recurrent neural network or convolutional neural network based encoders. However, is it possible to encode natural languages without orders? Given a bag of words from a disordered sentence, humans may still be able to understand what those words mean by reordering or reconstructing them. Inspired by such an intuition, in this paper, we perform a study to investigate how “order” information takes effects in natural language learning. By running comprehensive comparisons, we quantitatively compare the ability of several representative neural models to organize sentences from a bag of words under three typical scenarios, and summarize some empirical findings and challenges, which can shed light on future research on this line of work.
%R 10.18653/v1/2021.naacl-main.134
%U https://aclanthology.org/2021.naacl-main.134
%U https://doi.org/10.18653/v1/2021.naacl-main.134
%P 1682-1691
Markdown (Informal)
[Learning to Organize a Bag of Words into Sentences with Neural Networks: An Empirical Study](https://aclanthology.org/2021.naacl-main.134) (Tao et al., NAACL 2021)
ACL