@inproceedings{ziser-reichart-2018-pivot,
title = "Pivot Based Language Modeling for Improved Neural Domain Adaptation",
author = "Ziser, Yftah and
Reichart, Roi",
editor = "Walker, Marilyn and
Ji, Heng and
Stent, Amanda",
booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
month = jun,
year = "2018",
address = "New Orleans, Louisiana",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N18-1112",
doi = "10.18653/v1/N18-1112",
pages = "1241--1251",
abstract = "Representation learning with pivot-based methods and with Neural Networks (NNs) have lead to significant progress in domain adaptation for Natural Language Processing. However, most previous work that follows these approaches does not explicitly exploit the structure of the input text, and its output is most often a single representation vector for the entire text. In this paper we present the Pivot Based Language Model (PBLM), a representation learning model that marries together pivot-based and NN modeling in a structure aware manner. Particularly, our model processes the information in the text with a sequential NN (LSTM) and its output consists of a representation vector for every input word. Unlike most previous representation learning models in domain adaptation, PBLM can naturally feed structure aware text classifiers such as LSTM and CNN. We experiment with the task of cross-domain sentiment classification on 20 domain pairs and show substantial improvements over strong baselines.",
}
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%0 Conference Proceedings
%T Pivot Based Language Modeling for Improved Neural Domain Adaptation
%A Ziser, Yftah
%A Reichart, Roi
%Y Walker, Marilyn
%Y Ji, Heng
%Y Stent, Amanda
%S Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)
%D 2018
%8 June
%I Association for Computational Linguistics
%C New Orleans, Louisiana
%F ziser-reichart-2018-pivot
%X Representation learning with pivot-based methods and with Neural Networks (NNs) have lead to significant progress in domain adaptation for Natural Language Processing. However, most previous work that follows these approaches does not explicitly exploit the structure of the input text, and its output is most often a single representation vector for the entire text. In this paper we present the Pivot Based Language Model (PBLM), a representation learning model that marries together pivot-based and NN modeling in a structure aware manner. Particularly, our model processes the information in the text with a sequential NN (LSTM) and its output consists of a representation vector for every input word. Unlike most previous representation learning models in domain adaptation, PBLM can naturally feed structure aware text classifiers such as LSTM and CNN. We experiment with the task of cross-domain sentiment classification on 20 domain pairs and show substantial improvements over strong baselines.
%R 10.18653/v1/N18-1112
%U https://aclanthology.org/N18-1112
%U https://doi.org/10.18653/v1/N18-1112
%P 1241-1251
Markdown (Informal)
[Pivot Based Language Modeling for Improved Neural Domain Adaptation](https://aclanthology.org/N18-1112) (Ziser & Reichart, NAACL 2018)
ACL
- Yftah Ziser and Roi Reichart. 2018. Pivot Based Language Modeling for Improved Neural Domain Adaptation. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), pages 1241–1251, New Orleans, Louisiana. Association for Computational Linguistics.