Scalable Micro-planned Generation of Discourse from Structured Data

TL;DR

This paper introduces a scalable, modular pipeline for generating natural language descriptions from structured data, avoiding task-specific training and leveraging basic NLP tools for adaptability across domains.

Contribution

A novel pipeline-based approach that generates coherent paragraphs from structured data without requiring task-specific parallel data, enhancing scalability and domain adaptability.

Findings

Outperforms existing data-to-text systems on benchmark datasets.

Demonstrates robustness across diverse data types like Knowledge Graphs and Key-Value maps.

Operates effectively without task-specific labeled data.

Abstract

We present a framework for generating natural language description from structured data such as tables; the problem comes under the category of data-to-text natural language generation (NLG). Modern data-to-text NLG systems typically employ end-to-end statistical and neural architectures that learn from a limited amount of task-specific labeled data, and therefore, exhibit limited scalability, domain-adaptability, and interpretability. Unlike these systems, ours is a modular, pipeline-based approach, and does not require task-specific parallel data. It rather relies on monolingual corpora and basic off-the-shelf NLP tools. This makes our system more scalable and easily adaptable to newer domains. Our system employs a 3-staged pipeline that: (i) converts entries in the structured data to canonical form, (ii) generates simple sentences for each atomic entry in the canonicalized representation, and (iii) combines the sentences to produce a coherent, fluent and adequate paragraph description through sentence compounding and co-reference replacement modules. Experiments on a benchmark mixed-domain dataset curated for paragraph description from tables reveals the superiority of our system over existing data-to-text approaches. We also demonstrate the robustness of our system in accepting other popular datasets covering diverse data types such as Knowledge Graphs and Key-Value maps.