Neural data-to-text generation: A comparison between pipeline and end-to-end architectures (D19-1)
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| Challenge: | Traditionally, data-to-text applications have been designed using a modular pipeline architecture, in which the non-linguistic input data is converted into natural language through several intermediate transformations. |
| Approach: | They propose to use Gated-Recurrent Units and Transformer to implement neural pipelines for data-to-text generation. |
| Outcome: | The proposed models generalize better to unseen inputs and have better performance than the existing pipeline architectures. |
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| Challenge: | End-to-end neural data-totext generation has faced challenges generalizing to new domains and generating semantically consistent text. |
| Approach: | They propose a neural data-to-text generation system that makes minimal assumptions about the data representation and target domain. |
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Neural Pipeline for Zero-Shot Data-to-Text Generation (2022.acl-long)
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| Challenge: | In data-to-text generation, training on in-domain data leads to overfitting and repeating training data noise. |
| Approach: | They propose to train pretrained language models on general-domain text-based operations by transforming single-item descriptions with modules trained on ordering, aggregation, and paragraph compression. |
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Enhanced Transformer Model for Data-to-Text Generation (D19-56)
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| Challenge: | Neural models have shown significant progress on data-to-text generation tasks . data- to-text models generate descriptive texts from non-linguistic structured data . |
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Operation-guided Neural Networks for High Fidelity Data-To-Text Generation (D18-1)
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| Challenge: | Recent neural models for data-to-text generation generate descriptions that are not consistent with structured data. |
| Approach: | They propose a framework for data-to-text generation that uses symbolic operations to generate texts from structured data. |
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Neural Data-to-Text Generation with LM-based Text Augmentation (2021.eacl-main)
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| Challenge: | Neural data-to-text generation is a difficult task for many new applications because of a lack of training data. |
| Approach: | They propose a few-shot approach that augments the data available for training by generating new text samples based on replacing specific values by alternative ones from the same category and pairing the new text with data samples. |
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Data-to-text Generation with Entity Modeling (P19-1)
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| Challenge: | Recent approaches to data-to-text generation have shown great promise thanks to the use of large-scale datasets and the application of neural network architectures which are trained end-to end. |
| Approach: | They propose an entity-centric neural architecture for data-to-text generation which uses hierarchical attention to create entity-specific representations which are dynamically updated. |
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Step-by-Step: Separating Planning from Realization in Neural Data-to-Text Generation (N19-1)
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| Challenge: | Modern neural generation systems conflate these two steps into a single end-to-end differentiable system. |
| Approach: | They propose to split the generation process into a symbolic text-planning stage that is faithful to the input, followed by a neural generation stage that focuses only on realization. |
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Data-to-text Generation with Macro Planning (2021.tacl-1)
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| Challenge: | Recent approaches to data-to-text generation adopt the encoder-decoder architecture . however, these models perform poorly at selecting appropriate content and ordering it coherently . |
| Approach: | They propose a neural model with a macro planning stage followed by a generation stage . they use data from databases of records, simulations of physical systems, accounting spreadsheets . |
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Plan-then-Generate: Controlled Data-to-Text Generation via Planning (2021.findings-emnlp)
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| Challenge: | Existing studies focus on producing results that are close to the references, i.e. what to generate and in what order (the output structure) cannot be explicitly controlled by the users. |
| Approach: | They propose a Plan-then-Generate framework to improve the controllability of neural data-to-text models. |
| Outcome: | The proposed model can control both the intra-sentence and inter-sentent structure of the generated output. |
Faithful Low-Resource Data-to-Text Generation through Cycle Training (2023.acl-long)
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| Challenge: | Methods to generate text from structured data have advanced significantly in recent years, but can fail to produce output faithful to the input data, especially on out-of-domain data. |
| Approach: | They evaluate the effectiveness of cycle training by using two models which are inverses of each other to generate text from structured data and one which generates the structured data from natural language text. |
| Outcome: | The proposed approach achieves nearly the same performance as fully supervised approaches on the WebNLG, E2E, WTQ, and WSQL datasets. |