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

by   Alessandra Cervone, et al.

Current approaches to Natural Language Generation (NLG) focus on domain-specific, task-oriented dialogs (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 statistical NLG for conversational applications with larger ontologies, which may be required by multi-domain dialog systems as well as open-domain knowledge graph based question answering (QA). We focus on modeling 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 explore multi-task learning for NLG and benchmark our model on a popular NLG dataset and perform experiments with open-domain QA and task-oriented dialog. Finally, we integrate conversation context by using context embeddings as an additional input for generation to improve response quality. Our experiments show the feasibility of learning statistical NLG models for open-domain contextual QA with larger ontologies.


page 1

page 2

page 3

page 4


Improved and Efficient Conversational Slot Labeling through Question Answering

Transformer-based pretrained language models (PLMs) offer unmatched perf...

Zero-shot Generalization in Dialog State Tracking through Generative Question Answering

Dialog State Tracking (DST), an integral part of modern dialog systems, ...

SF-QA: Simple and Fair Evaluation Library for Open-domain Question Answering

Although open-domain question answering (QA) draws great attention in re...

BERT-CoQAC: BERT-based Conversational Question Answering in Context

As one promising way to inquire about any particular information through...

Actively Discovering New Slots for Task-oriented Conversation

Existing task-oriented conversational search systems heavily rely on dom...

The Dangers of trusting Stochastic Parrots: Faithfulness and Trust in Open-domain Conversational Question Answering

Large language models are known to produce output which sounds fluent an...

Introducing "Forecast Utterance" for Conversational Data Science

Envision an intelligent agent capable of assisting users in conducting f...

Please sign up or login with your details

Forgot password? Click here to reset