PoMo: Generating Entity-Specific Post-Modifiers in Context

04/05/2019
by   Jun Seok Kang, et al.
0

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 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.

READ FULL TEXT
research
11/06/2020

The ApposCorpus: A new multilingual, multi-domain dataset for factual appositive generation

News articles, image captions, product reviews and many other texts ment...
research
11/07/2016

Presenting a New Dataset for the Timeline Generation Problem

The timeline generation task summarises an entity's biography by selecti...
research
12/06/2021

Impact of Target Word and Context on End-to-End Metonymy Detection

Metonymy is a figure of speech in which an entity is referred to by anot...
research
03/19/2022

Automatic Detection of Entity-Manipulated Text using Factual Knowledge

In this work, we focus on the problem of distinguishing a human written ...
research
09/28/2020

Injecting Entity Types into Entity-Guided Text Generation

Recent successes in deep generative modeling have led to significant adv...
research
09/10/2021

Controlled Neural Sentence-Level Reframing of News Articles

Framing a news article means to portray the reported event from a specif...
research
03/19/2022

Clickbait Spoiling via Question Answering and Passage Retrieval

We introduce and study the task of clickbait spoiling: generating a shor...

Please sign up or login with your details

Forgot password? Click here to reset