# Natural Language Generation at Scale: A Case Study for Open Domain Question Answering

## TL;DR

This paper investigates the application of statistical Natural Language Generation models to large-scale, open-domain question answering scenarios, demonstrating their feasibility with larger ontologies and multi-task learning.

## Contribution

It introduces an Encoder-Decoder NLG framework for open-domain QA, exploring large ontologies, multi-task learning, and context-aware generation, which extends beyond traditional small-ontology, task-specific approaches.

## Key findings

- Feasibility of NLG models with up to 369 slot types
- Multi-task learning improves generation quality
- Using conversational context enhances responses

## Abstract

Current approaches to Natural Language Generation (NLG) for dialog mainly focus on domain-specific, task-oriented applications (e.g. restaurant booking) using limited ontologies (up to 20 slot types), usually without considering the previous conversation context. Furthermore, these approaches require large amounts of data for each domain, and do not benefit from examples that may be available for other domains. This work explores the feasibility of applying statistical NLG to scenarios requiring larger ontologies, such as multi-domain dialog applications or open-domain question answering (QA) based on knowledge graphs. We model NLG through an Encoder-Decoder framework using a large dataset of interactions between real-world users and a conversational agent for open-domain QA. First, we investigate the impact of increasing the number of slot types on the generation quality and experiment with different partitions of the QA data with progressively larger ontologies (up to 369 slot types). Second, we perform multi-task learning experiments between open-domain QA and task-oriented dialog, and benchmark our model on a popular NLG dataset. Moreover, we experiment with using the conversational context as an additional input to improve response generation quality. Our experiments show the feasibility of learning statistical NLG models for open-domain QA with larger ontologies.