fact-to-text

**Fact-to-text** is the NLP task of **generating natural language sentences that express specific factual statements** — converting structured facts (entity-attribute-value triples, factual records, or knowledge assertions) into grammatically correct, fluent sentences that accurately convey the stated facts. **What Is Fact-to-Text?** - **Definition**: Generating text that expresses given factual statements. - **Input**: Structured facts (triples, key-value pairs, assertions). - **Output**: Natural language sentence(s) stating those facts. - **Goal**: Accurate, fluent verbalization of factual information. **How Fact-to-Text Differs from General Data-to-Text** - **Scope**: Facts are typically atomic statements (single assertions). - **Focus**: Emphasis on factual accuracy over narrative flow. - **Input**: Usually simpler structures (single or few triples). - **Output**: Often single sentences or short passages. - **Evaluation**: Factual correctness is the primary criterion. **Input Fact Types** **Entity-Attribute-Value**: - (Barack Obama, birthDate, 1961-08-04). - (Python, creator, Guido van Rossum). - (Silicon, atomicNumber, 14). **RDF Triples**: - (dbr:Paris, dbo:country, dbr:France). - (dbr:Einstein, dbo:field, dbr:Physics). **Simple Assertions**: - Company X was founded in 2020. - Product Y costs $99. **Composite Facts**: - Multiple related facts about one entity. - Example: Einstein was born in Ulm, Germany in 1879. He won the Nobel Prize in Physics in 1921. **Generation Approaches** **Template-Based**: - **Method**: Predefined sentence patterns for each relation type. - **Example**: "[Subject] was born on [Date] in [Place]." - **Benefit**: Perfect factual accuracy guaranteed. - **Limitation**: Repetitive, limited to known relation types. **Neural Generation**: - **Method**: Seq2Seq/Transformer maps facts to text. - **Training**: Parallel corpus of (facts, sentences). - **Benefit**: Natural, varied output. - **Challenge**: Risk of hallucination (adding unstated facts). **LLM Prompting**: - **Method**: Provide facts in prompt, instruct to verbalize. - **Technique**: "Express these facts in a natural sentence: [facts]." - **Benefit**: Strong quality without fine-tuning. - **Challenge**: May embellish beyond stated facts. **Constrained Generation**: - **Method**: Decode text while constraining to express given facts. - **Lexical Constraints**: Required words/phrases in output. - **Semantic Constraints**: Entailment checking during generation. - **Benefit**: Balances fluency with factual accuracy. **Key Challenges** - **Factual Faithfulness**: Express ALL given facts, add NO extra facts. - **Natural Language**: Output should read naturally, not robotically. - **Aggregation**: Combine multiple facts into coherent sentences. - **Referring Expressions**: Use appropriate pronouns and references. - **Numerical Precision**: Preserve exact numbers, dates, quantities. - **Negation**: Handle negative facts accurately. - **Rare Entities**: Generalize to unseen entity names. **Evaluation Metrics** **Factual Accuracy**: - Precision: % of stated facts actually in input. - Recall: % of input facts expressed in text. - F1: Harmonic mean of precision and recall. **Text Quality**: - BLEU, METEOR, BERTScore: Similarity to reference text. - Fluency: Human-rated naturalness. - Grammaticality: Error-free text. **Faithfulness**: - NLI-based: Does the generated text entail from the facts? - Fact extraction: Extract facts from generated text, compare with input. **Applications** - **Knowledge Base Completion**: Verbalize new KB entries. - **Fact-Checking**: Generate claims from facts for verification testing. - **Question Answering**: Verbalize factual answers from KBs. - **Education**: Generate factual quizzes and explanations. - **Data Journalism**: Auto-generate factual news from data. - **Chatbots**: Provide factual responses from structured backends. **Key Datasets** - **WebNLG**: RDF triples → text (standard benchmark). - **WikiBio**: Wikipedia infobox facts → biography sentences. - **E2E NLG**: Meaning representation facts → restaurant descriptions. - **KELM**: Knowledge-enhanced language model corpus. - **GenWiki**: Wikidata facts → Wikipedia sentences. **Tools & Models** - **Models**: T5, BART, GPT-4 for fact verbalization. - **Constrained Decoding**: NeuroLogic, FUDGE for constrained generation. - **Evaluation**: FactCC, DAE for faithfulness checking. - **KG Tools**: RDFLib, SPARQLWrapper for fact extraction. Fact-to-text is **the atomic unit of data-to-text generation** — getting individual facts right in natural language is the foundation for all more complex data narration tasks, from report generation to knowledge base verbalization to automated journalism.