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.
Approach: They propose to use a large-scale dataset to generate NLG from logical forms to obtain controllable and faithful generations from structured data.
Outcome: The proposed model can describe interesting facts from logical inferences across records, but it is difficult to produce such fidelity.

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Challenge: Existing studies on neural natural language generation focus on surface-level realizations with limited emphasis on logical inference.
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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.
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NL2Logic: AST-Guided Translation of Natural Language into First-Order Logic with Large Language Models (2026.findings-eacl)

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Challenge: Structured reasoning approaches that parse first-order logic rules from natural language lack syntax control and semantic faithfulness.
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Best Practices for Data-Efficient Modeling in NLG:How to Train Production-Ready Neural Models with Less Data (2020.coling-industry)

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Challenge: Natural language generation (NLG) is a critical component in conversational systems . Traditionally, NLG components have been deployed using template-based solutions . however, deployment of such model-based systems has been challenging due to high latency and data needs.
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The Amazing World of Neural Language Generation (2020.emnlp-tutorials)

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Challenge: Recent years have seen a paradigm shift in neural text generation due to advances in deep contextual language modeling and transfer learning.
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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.
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Controlled Language Generation for Language Learning Items (2022.emnlp-industry)

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Challenge: Recent advances in pre-trained language models have resulted in success in generating fluent English text.
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Automatic and Human-AI Interactive Text Generation (with a focus on Text Simplification and Revision) (2024.acl-tutorials)

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Challenge: In this tutorial, we focus on text-to-text generation, a class of natural language generation tasks, that takes a piece of text as input and then generates a revision that is improved according to some specific criteria.
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Curate and Generate: A Corpus and Method for Joint Control of Semantics and Style in Neural NLG (P19-1)

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Challenge: Neural natural language generation (NNLG) models generate syntactically correct utterances from structured inputs without needing hand-crafted rules or templates.
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Logic-Consistency Text Generation from Semantic Parses (2021.findings-acl)

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Challenge: Text generation from semantic parses is challenging due to the complexity of the inner logic and the lack of automatic evaluation metrics for logic consistency.
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