Bootstrapping Generators from Noisy Data

By Laura Perez-Beltrachini, Mirella Lapata. 2018

42 citations recorded by the source corpus.

A core step in statistical data-to-text generation concerns learning correspondences between structured data representations (e.g., facts in a database) and associated texts. In this paper we aim to bootstrap generators from large scale datasets where the data (e.g., DBPedia facts) and related texts (e.g., Wikipedia abstracts) are loosely aligned. We tackle this challenging task by introducing a special-purpose content selection mechanism. We use multi-instance learning to automatically discover correspondences between data and text pairs and show how these can be used to enhance the content signal while training an encoder-decoder architecture. Experimental results demonstrate that models trained with content-specific objectives improve upon a vanilla encoder-decoder which solely relies on soft attention.

Subfields: Artificial Intelligence, Generative Models, Signal Processing

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Citations in the corpus

DART: Open-Domain Structured Data Record to Text Generation (2020, background): “, 2016), biographical texts (Lebret et al., 2016; Sha et al., 2018; Liu et al., 2018; Vougiouklis et al., 2018; Perez-Beltrachini and Lapata, 2018), knowledge-base descriptions (OâĂŹDonnell et al.”
Data-to-Text Generation with Content Selection and Planning (2018, methodology): “Perez-Beltrachini and Lapata (2018) introduce a content selection component (based on multi-instance learning) without content planning, while Liu et al. (2017) propose a sentence planning mechanism which orders the contents of a Wikipedia infobox in order to generate a single sentence.”
Data-to-text Generation with Entity Modeling (2019, background): “Modern approaches to data-to-text generation have shown great promise (Lebret et al., 2016; Mei et al., 2016; Perez-Beltrachini and Lapata, 2018; Puduppully et al., 2019; Wiseman et al., 1 Our code and dataset can be found at https:// github.com/ratishsp/data2text-entity-py .”
A Simple Recipe towards Reducing Hallucination in Neural Surface Realisation (2019, background): “We are still on the way to ﬁnd out solutions for cases with huge noise (Perez-Beltrachini and Lapata, 2018; Wiseman et al., 2017), where heavy manual intervention or exter-nal knowledge should be desperately needed.”
Controlling hallucinations at word level in data-to-text generation (2021, unknown): “Underlined, red words are hallucinated according either to our scoring procedure or to the method proposed by Perez-Beltrachini and Lapata [47].”
Towards Faithful Neural Table-to-Text Generation with Content-Matching Constraints (2020, methodology): “Moreover, the REINFORCE algorithm (Williams, 1992) and policy gradient method used in (Perez-Beltrachini and Lapata, 2018; Liu et al., 2019b) exhibits high variance when training the model.”
Latent Relation Language Models (2019, unknown): “, 2018) or tables (Liu et al., 2018; Perez-Beltrachini and Lapata, 2018).”
Towards Faithfulness in Open Domain Table-to-text Generation from an Entity-centric View (2021, background): “The difficulty of faithful table-to-text generation originates from the divergence of source tables and reference text (Perez-Beltrachini and Lapata 2018) in the training stage, especially for open domain datasets without human curation, e.”
Deep Graph Convolutional Encoders for Structured Data to Text Generation (2018, background): “Recent neural generation approaches (Mei et al., 2016; Lebret et al., 2016; Wiseman et al., 2017; Gardent et al., 2017b; Ferreira et al., 2017; Konstas et al., 2017; Perez-Beltrachini and Lapata, 2018) build on encoder-decoder architectures proposed for machine translation (Sutskever et al.”
ESPRIT: Explaining Solutions to Physical Reasoning Tasks (2020, background): “, 2016), biographical texts from Wikipedia infoboxes (Lebret et al., 2016; Sha et al., 2018; Liu et al., 2018; Perez-Beltrachini and Lapata, 2018), descriptions of knowledge bases (ODonnell et al.”