Logic2Text: High-Fidelity Natural Language Generation from Logical Forms

By Zhiyu Chen, Wenhu Chen, Hanwen Zha, Xiyou Zhou, Yunkai Zhang, Sairam Sundaresan, William Yang Wang. 2020

75 citations recorded by the source corpus.

Previous studies on Natural Language Generation (NLG) from structured data have primarily focused on surface-level descriptions of record sequences. However, for complex structured data, e.g., multi-row tables, it is often desirable for an NLG system to describe interesting facts from logical inferences across records. If only provided with the table, it is hard for existing models to produce controllable and high-fidelity logical generations. In this work, we formulate high-fidelity NLG as generation from logical forms in order to obtain controllable and faithful generations. We present a new large-scale dataset, Logic2Text, with 10,753 descriptions involving common logic types paired with the underlying logical forms. The logical forms show diversified graph structure of free schema, which pose great challenges on the model’s ability to understand the semantics. We experiment on (1) Fully-supervised training with the full datasets, and (2) Few-shot setting, provided with hundreds of paired examples; We compare several popular generation models and analyze their performances. We hope our dataset can encourage research towards building an advanced NLG system capable of natural, faithful, and human-like generation. The dataset and code is available at https://github.com/czyssrs/Logic2Text.

Subfields: Artificial Intelligence, Natural Language Processing, Natural Language Generation

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Citations in the corpus

Survey of Hallucination in Natural Language Generation (2022, background): “Multiple terminologies, such as faithfulness [16, 18, 44, 104, 120, 130, 130, 146, 154, 177, 197], factual consistency [14, 15, 20, 139, 142, 176], fidelity [19], factualness3 [132], factuality [28], or on the other hand, hallucination [35, 63, 94, 139, 143], fact contradicting [116] are used in the”
DART: Open-Domain Structured Data Record to Text Generation (2020, background): “…et al., 2020b; Shen et al., 2020), Minimal Recursion Semantics (MRS) (Hajdik et al., 2019), Resource Description Framework (RDF triples) (Gardent et al., 2017b; Vougiouklis et al., 2018; Distiawan et al., 2018; Elsahar et al., 2018; Zhu et al., 2019b), logic forms (Chen et al., 2020b), and tables.”
UnifiedSKG: Unifying and Multi-Tasking Structured Knowledge Grounding with Text-to-Text Language Models (2022, methodology): “• Formal language to text translation describes formal language in natural language (Chen et al., 2020d).”
De-Confounded Variational Encoder-Decoder for Logical Table-to-Text Generation (2021, methodology): “We use a mix of BLEU-3 (Papineni et al., 2002) and NLI-Acc (Chen et al., 2020a) scores to supervise the selector.”
Pre-Trained Language Models for Text Generation: A Survey (2022, background): “[28] argued that it is important to leverage intermediate meaning representations to achieve faithful generation.”
Logic-Consistency Text Generation from Semantic Parses (2021, methodology): “We pick the SENT and LOGIC STR ﬁelds from the original Logic2Text to compose our own train data.”