Simple and Effective Retrieve-Edit-Rerank Text Generation

By Nabil Hossain, Marjan Ghazvininejad, Luke S. Zettlemoyer. 2020

48 citations recorded by the source corpus.

Retrieve-and-edit seq2seq methods typically retrieve an output from the training set and learn a model to edit it to produce the final output. We propose to extend this framework with a simple and effective post-generation ranking approach. Our framework (i) retrieves several potentially relevant outputs for each input, (ii) edits each candidate independently, and (iii) re-ranks the edited candidates to select the final output. We use a standard editing model with simple task-specific re-ranking approaches, and we show empirically that this approach outperforms existing, significantly more complex methodologies. Experiments on two machine translation (MT) datasets show new state-of-art results. We also achieve near state-of-art performance on the Gigaword summarization dataset, where our analyses show that there is significant room for performance improvement with better candidate output selection in future work.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Natural Language Processing

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Citations in the corpus

Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks (2020, background): “These approaches have proved successful in a number of domains including Machine Translation [18, 22] and Semantic Parsing [21].”
Language Generation Models Can Cause Harm: So What Can We Do About It? An Actionable Survey (2022, background): “In contrast, Lewis et al. (2020); de Masson d'Autume et al. (2019); Izacard and Grave (2021); Hossain et al. (2020) use unstructured text as knowledge.”
Case-based Reasoning for Natural Language Queries over Knowledge Bases (2021, background): “Recently, Hossain et al. (2020) proposed a retrieve-edit-rerank approach for text generation in which each retrieved candidate from the training set is edited independently and then re-ranked.”
RefSum: Refactoring Neural Summarization (2021, background): “As a result, even on the same dataset, different selection biases of these choices will lead to diverse system outputs (Kedzie et al., 2018; Hossain et al., 2020).”
Template Guided Text Generation for Task Oriented Dialogue (2020, methodology): “Our template based approach bears similarities to sentence fusion (Barzilay and McKeown, 2005), and prototype based text editing (Hossain et al., 2020; Cao et al., 2018; Guu et al., 2018; Wu et al., 2019).”
Open-book Video Captioning with Retrieve-Copy-Generate Network (2021, methodology): “Meanwhile, SEG-NMT [7] and RER [12] leverage retrieval to assist the NMT system.”