Semi-Supervised Neural Text Generation by Joint Learning of Natural Language Generation and Natural Language Understanding Models - Archive ouverte HAL Accéder directement au contenu
Communication Dans Un Congrès Année : 2019

Semi-Supervised Neural Text Generation by Joint Learning of Natural Language Generation and Natural Language Understanding Models

Résumé

In Natural Language Generation (NLG), End-to-End (E2E) systems trained through deep learning have recently gained a strong interest. Such deep models need a large amount of carefully annotated data to reach satisfactory performance. However, acquiring such datasets for every new NLG application is a tedious and time-consuming task. In this paper, we propose a semi-supervised deep learning scheme that can learn from non-annotated data and annotated data when available. It uses an NLG and a Natural Language Understanding (NLU) sequence-to-sequence models which are learned jointly to compensate for the lack of annotation. Experiments on two benchmark datasets show that, with limited amount of annotated data, the method can achieve very competitive results while not using any pre-processing or re-scoring tricks. These findings open the way to the exploitation of non-annotated datasets which is the current bottleneck for the E2E NLG system development to new applications.

Dates et versions

hal-02371384 , version 1 (19-11-2019)

Identifiants

Citer

Raheel Qader, François Portet, Cyril Labbé. Semi-Supervised Neural Text Generation by Joint Learning of Natural Language Generation and Natural Language Understanding Models. 12th International Conference on Natural Language Generation (INLG 2019), Oct 2019, Tokyo, Japan. pp.552--562. ⟨hal-02371384⟩
63 Consultations
0 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More