Toward Multi-domain Language Generation using Recurrent Neural Networks

By Tsung-Hsien Wen, Milica Gasic, Nikola Mrksic, Lina Maria Rojas-Barahona, Pei-hao Su, David Vandyke, Susannah Young. 2015

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Citations in the corpus

Continual Learning for Natural Language Generation in Task-oriented Dialog Systems (2020, background): “As an essential part of task-oriented dialog systems (Wen et al., 2015b; Bordes et al., 2016), the task of Natural Language Generation (NLG) is to produce a natural language utterance containing the desired information given a semantic representation (so-called dialog act).”
Analysing Data-To-Text Generation Benchmarks (2017, methodology): “For instance, BLEU results reported by (Wen et al., 2015a) on three different datasets indicate that the same systems are facing different difﬁculties on each of these.”
Adversarial Domain Adaptation for Variational Neural Language Generation in Dialogue Systems (2018, methodology): “Moreover, a phrase-based statistical generator (Mairesse et al., 2010) using graphical models and active learning, and a multi-domain procedure (Wen et al., 2016a) via data counterfeiting and discriminative training.”
Natural Language Generation for Spoken Dialogue System using RNN Encoder-Decoder Networks (2017, methodology): “The recurrent language generator proposed in this paper is based on a neural language generator (Wen et al., 2016b), which consists of three main components: (i) an Encoder that incorporates the target meaning representation (MR) as the model inputs, (ii) an Aligner that aligns and controls the…”
Meta-Learning for Low-resource Natural Language Generation in Task-oriented Dialogue Systems (2019, methodology): “A series of neural methods have been proposed, including HLSTM [ Wen et al. , 2015a ] , SCLSTM [ Wen et al. , 2015c ] , Enc-Dec [ Wen et al. , 2015b ] and RALSTM [ Tran and Nguyen, 2017 ] .”