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PoMo

PoMo: Generating Entity-Specific Post-Modifiers in Context

LUNR Lab.

PoMo: Post-Modifier dataset

Overview

PoMo is the dataset introduced in our paper PoMo: Generating Entity-Specific Post-Modifiers in Context from NAACL 2019.

Our paper(arXiv version) has been released here: https://arxiv.org/abs/1904.03111

Post-Modifier Generation Task

Post-modifier is a short phrase that comes after an entity in a sentence to describe the entity in detail. It can be found easily in many news articles. For example, in the below sentence, the MIT professor and antiwar activist is the post-modifier of Noam Chomsky.

Noam Chomsky, the MIT professor and antiwar activist, said Dr. Melman helped mobilize what once was weak and scattered resistance to war and other military operations.

We formulate post-modifier generation task as a data-to-text generation problem, where the data is the context (a sentence without a post-modifier) and the set of known facts about the target entity. The text to be generated is a post-modifier that is relevant to the rest of the information conveyed in the text. Below example shows the input and output of the task.

Image of post-modifier generation task

Download

The dataset can be downloaded from https://github.com/StonyBrookNLP/PoMo

Citation

Please use the following bibtex entry:

@inproceedings{Kang2019PoMo,
  title={PoMo: Generating Entity-Specific Post-Modifiers in Context},
  author={Jun Seok Kang and Robert L. Logan IV and Zewei Chu and Yang Chen and Dheeru Dua and Kevin Gimpel and Sameer Singh and Niranjan Balasubramanian},
  booktitle={NAACL-HLT},
  year={2019}
}

Dataset Information

Data Sources

We used various data sources to construct PoMo.

Contributors