The E2E Dataset: New Challenges For End-to-End Generation

Published in

(2017)

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TL;DR

The E2E dataset poses new challenges: (1) its human reference texts show more lexical richness and syntactic variation, including discourse phenomena; (2) generating from this set requires content selection, which promises more natural, varied and less template-like system utterances.

Abstract

This paper describes the E2E data, a new dataset for training end-to-end, data-driven natural language generation systems in the restaurant domain, which is ten times bigger than existing, frequently used datasets in this area. The E2E dataset poses new challenges: (1) its human reference texts show more lexical richness and syntactic variation, including discourse phenomena; (2) generating from this set requires content selection. As such, learning from this dataset promises more natural, varied and less template-like system utterances. We also establish a baseline on this dataset, which illustrates some of the difficulties associated with this data.

Authors

Jekaterina Novikova

5 papers

Verena Rieser

10 papers

Ondrej Dusek

9 papers

References28 items

Analysing Data-To-Text Generation Benchmarks

Imitation learning for language generation from unaligned data

A Context-aware Natural Language Generator for Dialogue Systems

Crowd-sourcing NLG Data: Pictures Elicit Better Data.

Natural Language Generation in Dialogue using Lexicalized and Delexicalized Data

The E2E Dataset: New Challenges For End-to-End Generation

Published in

(2017)

External Links:

Generate Graph DownloadPDF

TL;DR

Abstract

Authors

Jekaterina Novikova

5 papers

Verena Rieser

10 papers

Ondrej Dusek

9 papers

References28 items

Analysing Data-To-Text Generation Benchmarks

Imitation learning for language generation from unaligned data

A Context-aware Natural Language Generator for Dialogue Systems

Crowd-sourcing NLG Data: Pictures Elicit Better Data.

Natural Language Generation in Dialogue using Lexicalized and Delexicalized Data

Sequence-to-Sequence Generation for Spoken Dialogue via Deep Syntax Trees and Strings

Multi-domain Neural Network Language Generation for Spoken Dialogue Systems

What to talk about and how? Selective Generation using LSTMs with Coarse-to-Fine Alignment

Semantically Conditioned LSTM-based Natural Language Generation for Spoken Dialogue Systems

Training a Natural Language Generator From Unaligned Data

Microsoft COCO Captions: Data Collection and Evaluation Server

CIDEr: Consensus-based image description evaluation

Neural Machine Translation by Jointly Learning to Align and Translate

Natural Language Generation as Incremental Planning Under Uncertainty: Adaptive Information Presentation for Statistical Dialogue Systems

Computational Methods for Corpus Annotation and Analysis

The Relationship of Lexical Richness to the Quality of ESL Learners' Oral Narratives.

Phrase-Based Statistical Language Generation Using Graphical Models and Active Learning

Natural Language Generation as Planning Under Uncertainty for Spoken Dialogue Systems

Learning to sportscast: a test of grounded language acquisition

METEOR: An Automatic Metric for MT Evaluation with High Levels of Correlation with Human Judgments

Information Presentation in Spoken Dialogue Systems

Generation and evaluation of user tailored responses in multimodal dialogue

ROUGE: A Package for Automatic Evaluation of Summaries

Trainable Sentence Planning for Complex Information Presentations in Spoken Dialog Systems

Bleu: a Method for Automatic Evaluation of Machine Translation

Automatic evaluation of machine translation quality using n-gram co-occurrence statistics

Automatic measurement of syntactic complexity in child language acquisition

Proceedings of the 41st Annual Meeting of the Association for Computational Linguistics

Field of Study

Computer Science

Journal Information

Name

ArXiv

Volume

abs/2005.00687