Published on Thu Jun 01 2017

Semantic Refinement GRU-based Neural Language Generation for Spoken Dialogue Systems

Van-Khanh Tran, Le-Minh Nguyen

Natural language generation plays a critical role in spoken dialogue systems. This paper presents a new approach to NLG by using recurrent neural networks (RNN)

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Abstract

Natural language generation (NLG) plays a critical role in spoken dialogue systems. This paper presents a new approach to NLG by using recurrent neural networks (RNN), in which a gating mechanism is applied before RNN computation. This allows the proposed model to generate appropriate sentences. The RNN-based generator can be learned from unaligned data by jointly training sentence planning and surface realization to produce natural language responses. The model was extensively evaluated on four different NLG domains. The results show that the proposed generator achieved better performance on all the NLG domains compared to previous generators.

Wed Jun 21 2017
Machine Learning
Neural-based Natural Language Generation in Dialogue using RNN Encoder-Decoder with Semantic Aggregation
Encoder-Aggregator-Decoder is an extension of an Recurrent Neural Network. The proposed Semantic Aggregator consists of two components: an Aligner and a Refiner. The model was extensively assessed on four different NLG domains.
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Thu Jun 01 2017
NLP
Natural Language Generation for Spoken Dialogue System using RNN Encoder-Decoder Networks
Natural language generation (NLG) is a critical component in a spoken conversations. This paper presents a Recurrent Neural Network based on an LSTM-based decoder. The proposed model was extensively evaluated on four different NLG datasets.
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Wed Aug 08 2018
NLP
Natural Language Generation by Hierarchical Decoding with Linguistic Patterns
Natural language generation (NLG) is a critical component in spoken dialogue. Many simple NLG models are based on recurrent neural networks. This paper introduces a hierarchical decoding NLG model based on linguistic patterns. The proposed method outperforms the traditional one with a smaller model size.
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Wed Sep 19 2018
NLP
Investigating Linguistic Pattern Ordering in Hierarchical Natural Language Generation
Natural language generation (NLG) is a critical component in spoken dialogue. Most modern NLG models are based on a sequence-to-sequence model. This paper introduces an NLG model with a hierarchical attentional decoder. The hierarchy focuses on leveraging linguistic knowledge in a specific order.
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Mon Jun 17 2019
Artificial Intelligence
Constrained Decoding for Neural NLG from Compositional Representations in Task-Oriented Dialogue
Generating fluent natural language responses from structured semantic representations is a critical step in task-oriented conversational systems. The semantic representations used, however, are often underspecified. This places a higher burden on the generation model for sentence planning.
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Sun Jun 02 2019
NLP
A Survey of Natural Language Generation Techniques with a Focus on Dialogue Systems - Past, Present and Future Directions
Natural Language Generation (NLG) is a growing field of research in artificial intelligence. NLG aims to automatically generate language that is coherent and understandable to humans. We provide a comprehensive review of approaches to building dialogue systems.
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