| Challenge: | Data2Text Studio is a platform for automated text generation from structured data. |
| Approach: | They conduct experiments on RotoWire datasets for template extraction and text generation . they find that the Semi-HMMs model improves interactivity and interpretability . |
| Outcome: | The proposed model improves on template extraction and text generation tasks on RotoWire datasets. |
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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. |
Make Templates Smarter: A Template Based Data2Text System Powered by Text Stitch Model (2020.findings-emnlp)
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| Challenge: | Neural network based data2text models drop or modify information in inputs and it is hard to control the generated contents. |
| Approach: | They propose a template-based data2text system powered by a text stitch model that automatically stitches adjacent template units. |
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Have Your Text and Use It Too! End-to-End Neural Data-to-Text Generation with Semantic Fidelity (2020.coling-main)
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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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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. |
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Logic2Text: High-Fidelity Natural Language Generation from Logical Forms (2020.findings-emnlp)
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| Challenge: | Recent studies on Natural Language Generation (NLG) from structured data focus on surface descriptions of simple record sequences, for example, attribute-value pairs of fixed or very limited schema. |
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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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Text Generation with Text-Editing Models (2022.naacl-tutorials)
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Eric Malmi, Yue Dong, Jonathan Mallinson, Aleksandr Chuklin, Jakub Adamek, Daniil Mirylenka, Felix Stahlberg, Sebastian Krause, Shankar Kumar, Aliaksei Severyn
| Challenge: | Text-editing models are a popular alternative to seq2seq for monolingual text generation tasks such as text summarization and style transfer. |
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Data-to-text Generation with Variational Sequential Planning (2022.tacl-1)
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| Challenge: | Recent advances in data-to-text generation have greatly facilitated the task of generating textual output from non-linguistic input. |
| Approach: | They propose a neural model enhanced with a planning component responsible for organizing high-level information in a coherent and meaningful way. |
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Investigating the Robustness of Natural Language Generation from Logical Forms via Counterfactual Samples (2022.emnlp-main)
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| Challenge: | State-of-the-art methods based on pre-trained models have achieved remarkable performance on the standard test dataset. |
| Approach: | They propose to incorporate hierarchical structure of logical forms into the model and exploit automatically generated counterfactual data for training. |
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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 . |
| Outcome: | The proposed model outperforms baselines on two data-to-text benchmarks . it uses the encoderdecoder architecture and is compared with existing models . |