Generate, Filter, and Rank: Grammaticality Classification for Production-Ready NLG Systems

TL;DR

This paper introduces a generate, filter, and rank framework for NLG systems, focusing on grammaticality classification to improve response quality, and presents new datasets and classification approaches tailored to production environments.

Contribution

It provides a new dataset for grammaticality and semantic correctness in NLG, and evaluates CNNs and GBDTs for grammaticality classification in a production context.

Findings

Grammaticality classification is highly sensitive to error distribution.

Existing datasets do not capture the error types in data-driven NLG.

High precision can be achieved with reasonable recall on the new dataset.

Abstract

Neural approaches to Natural Language Generation (NLG) have been promising for goal-oriented dialogue. One of the challenges of productionizing these approaches, however, is the ability to control response quality, and ensure that generated responses are acceptable. We propose the use of a generate, filter, and rank framework, in which candidate responses are first filtered to eliminate unacceptable responses, and then ranked to select the best response. While acceptability includes grammatical correctness and semantic correctness, we focus only on grammaticality classification in this paper, and show that existing datasets for grammatical error correction don't correctly capture the distribution of errors that data-driven generators are likely to make. We release a grammatical classification and semantic correctness classification dataset for the weather domain that consists of responses generated by 3 data-driven NLG systems. We then explore two supervised learning approaches (CNNs and GBDTs) for classifying grammaticality. Our experiments show that grammaticality classification is very sensitive to the distribution of errors in the data, and that these distributions vary significantly with both the source of the response as well as the domain. We show that it's possible to achieve high precision with reasonable recall on our dataset.