Data-to-Text Generation
105 papers with code • 24 benchmarks • 22 datasets
A classic problem in natural-language generation (NLG) involves taking structured data, such as a table, as input, and producing text that adequately and fluently describes this data as output. Unlike machine translation, which aims for complete transduction of the sentence to be translated, this form of NLG is usually taken to require addressing (at least) two separate challenges: what to say, the selection of an appropriate subset of the input data to discuss, and how to say it, the surface realization of a generation.
( Image credit: Data-to-Text Generation with Content Selection and Planning )
Benchmarks
These leaderboards are used to track progress in Data-to-Text Generation
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Libraries
Use these libraries to find Data-to-Text Generation models and implementations
MultiWOZ
WebNLG
E2E
RotoWire
ToTTo
RoboCup
ViGGO
Chart2Text
Latest papers with no code
A Systematic Review of Data-to-Text NLG
Relevant literature in this field on datasets, evaluation metrics, application areas, multilingualism, language models, and hallucination mitigation methods is reviewed.
TrICy: Trigger-guided Data-to-text Generation with Intent aware Attention-Copy
Data-to-text (D2T) generation is a crucial task in many natural language understanding (NLU) applications and forms the foundation of task-oriented dialog systems.
Beyond Reference-Based Metrics: Analyzing Behaviors of Open LLMs on Data-to-Text Generation
We analyze the behaviors of open large language models (LLMs) on the task of data-to-text (D2T) generation, i. e., generating coherent and relevant text from structured data.
Compositional Generalization for Data-to-Text Generation
Data-to-text generation involves transforming structured data, often represented as predicate-argument tuples, into coherent textual descriptions.
Prompt Optimization via Adversarial In-Context Learning
We propose a new method, Adversarial In-Context Learning (adv-ICL), to optimize prompt for in-context learning (ICL) by employing one LLM as a generator, another as a discriminator, and a third as a prompt modifier.
PixT3: Pixel-based Table To Text generation
Table-to-text generation involves generating appropriate textual descriptions given structured tabular data.
Few-Shot Data-to-Text Generation via Unified Representation and Multi-Source Learning
We present a novel approach for structured data-to-text generation that addresses the limitations of existing methods that primarily focus on specific types of structured data.
You Can Generate It Again: Data-to-text Generation with Verification and Correction Prompting
In this paper, we propose a novel approach that goes beyond traditional one-shot generation methods by introducing a multi-step process consisting of generation, verification, and correction stages.
ReTAG: Reasoning Aware Table to Analytic Text Generation
The task of table summarization involves generating text that both succinctly and accurately represents the table or a specific set of highlighted cells within a table.
Stylized Data-to-Text Generation: A Case Study in the E-Commerce Domain
This task is non-trivial, due to three challenges: the logic of the generated text, unstructured style reference, and biased training samples.
