Data-to-Text Generation
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Benchmarks
WebNLG
E2E NLG Challenge
WebNLG Full
Cleaned E2E NLG Challenge
RotoWire
ToTTo
XAlign
DART
MULTIWOZ 2.1
RotoWire (Content Ordering)
Rotowire (Content Selection)
MLB Dataset
Czech Restaurant NLG
E2E
SR11Deep
ViGGO
WebNLG en
WebNLG ru
AMR3.0
GenWiki
WikiOFGraph
Most implemented
Language Models are Unsupervised Multitask Learners
MoverScore: Text Generation Evaluating with Contextualized Embeddings and Earth Mover Distance
Challenges in Data-to-Document Generation
Investigating Pretrained Language Models for Graph-to-Text Generation
Triples-to-isiXhosa (T2X): Addressing the Challenges of Low-Resource Agglutinative Data-to-Text Generation
TaTa: A Multilingual Table-to-Text Dataset for African Languages
Papers
Cross-Domain, Multi-Task Data-to-Text Generation without In-Domain Training Data
Structured data exists in many forms (tables, knowledge graphs, charts, and time series), and converting it into text may involve different generation tasks. However, most prior work on data-to-text (D2T) generation has …
Data-to-Text GenerationKnowledge DistillationKnowledge GraphsTailNLG: A Multilingual Benchmark Addressing Verbalization of Long-Tail Entities
The automatic verbalization of structured knowledge is a key task for making knowledge graphs accessible to non-expert users and supporting retrieval-augmented generation systems. Although recent advances in Data-to-Text…
Data-to-Text GenerationKnowledge GraphsMzansiText and MzansiLM: An Open Corpus and Decoder-Only Language Model for South African Languages
Decoder-only language models can be adapted to diverse tasks through instruction finetuning, but the extent to which this generalizes at small scale for low-resource languages remains unclear. We focus on the languages o…
Natural Language UnderstandingData-to-Text GenerationNews ClassificationTurk-LettuceDetect: A Hallucination Detection Models for Turkish RAG Applications
The widespread adoption of Large Language Models (LLMs) has been hindered by their tendency to hallucinate, generating plausible but factually incorrect information. While Retrieval-Augmented Generation (RAG) systems att…
Computational EfficiencyData-to-Text GenerationQuestion AnsweringLarge Language Models as Span Annotators
For high-quality texts, single-score metrics seldom provide actionable feedback. In contrast, span annotation - pointing out issues in the text by annotating their spans - can guide improvements and provide insights. Unt…
Data-to-Text GenerationMachine TranslationPropaganda detectionText Generation+1SCOPE: A Self-supervised Framework for Improving Faithfulness in Conditional Text Generation
Large Language Models (LLMs), when used for conditional text generation, often produce hallucinations, i.e., information that is unfaithful or not grounded in the input context. This issue arises in typical conditional t…
Conditional Text GenerationData-to-Text GenerationText GenerationText Summarization