Show, Write, and Retrieve: Entity-aware Article Generation and Retrieval

• URL: http://arxiv.org/abs/2112.05917v3
• Date: Fri, 20 Oct 2023 20:44:27 GMT
• Title: Show, Write, and Retrieve: Entity-aware Article Generation and Retrieval
• Authors: Zhongping Zhang, Yiwen Gu, Bryan A. Plummer
• Abstract summary: We propose an ENtity-aware article GeneratIoN and rEtrieval framework, to explicitly incorporate named entities into language models. We conducted experiments on three public datasets: GoodNews, VisualNews, and WikiText. Our results demonstrate that our model can boost both article generation and article retrieval performance, with a 4-5 perplexity improvement in article generation and a 3-4% boost in recall@1 in article retrieval.
• Score: 18.270878909735256
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Article comprehension is an important challenge in natural language processing with many applications such as article generation or image-to-article retrieval. Prior work typically encodes all tokens in articles uniformly using pretrained language models. However, in many applications, such as understanding news stories, these articles are based on real-world events and may reference many named entities that are difficult to accurately recognize and predict by language models. To address this challenge, we propose an ENtity-aware article GeneratIoN and rEtrieval (ENGINE) framework, to explicitly incorporate named entities into language models. ENGINE has two main components: a named-entity extraction module to extract named entities from both metadata and embedded images associated with articles, and an entity-aware mechanism that enhances the model's ability to recognize and predict entity names. We conducted experiments on three public datasets: GoodNews, VisualNews, and WikiText, where our results demonstrate that our model can boost both article generation and article retrieval performance, with a 4-5 perplexity improvement in article generation and a 3-4% boost in recall@1 in article retrieval. We release our implementation at https://github.com/Zhongping-Zhang/ENGINE .

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