# PoMo: Generating Entity-Specific Post-Modifiers in Context

arXiv: https://arxiv.org/abs/1904.03111 · HTML: https://mlsanity.com/paper/1904.03111/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Apr 2019.

## Built on by (most cited first, 4 of 4)

- [FRUIT: Faithfully Reflecting Updated Information in Text](https://mlsanity.com/paper/2112.08634/) (2021)
- [KILM: Knowledge Injection into Encoder-Decoder Language Models](https://mlsanity.com/paper/2302.09170/) (2023)
- [IGA : An Intent-Guided Authoring Assistant](https://mlsanity.com/paper/2104.07000/) (2021)
- [Creating Custom Event Data Without Dictionaries: A Bag-of-Tricks](https://mlsanity.com/paper/2304.01331/) (2023)

## Abstract (arXiv)

We introduce entity post-modifier generation as an instance of a collaborative writing task. Given a sentence about a target entity, the task is to automatically generate a post-modifier phrase that provides contextually relevant information about the entity. For example, for the sentence, "Barack Obama, _______, supported the #MeToo movement.", the phrase "a father of two girls" is a contextually relevant post-modifier. To this end, we build PoMo, a post-modifier dataset created automatically from news articles reflecting a journalistic need for incorporating entity information that is relevant to a particular news event. PoMo consists of more than 231K sentences with post-modifiers and associated facts extracted from Wikidata for around 57K unique entities. We use crowdsourcing to show that modeling contextual relevance is necessary for accurate post-modifier generation. We adapt a number of existing generation approaches as baselines for this dataset. Our results show there is large room for improvement in terms of both identifying relevant facts to include (knowing which claims are relevant gives a >20% improvement in BLEU score), and generating appropriate post-modifier text for the context (providing relevant claims is not sufficient for accurate generation). We conduct an error analysis that suggests promising directions for future research.