Automated Language Generation (NLG) System

An Automated Language Generation (NLG) System is an automated writting system that implements an NLG algorithm to solve an NLG task.

• AKA: Natural Language Generator.
• Context:
  • It can (typically) apply an NLG Model.
  • It can range from being a General NLG System to being a Domain-Specific NLG System.
  • It can range from being a Written NLG System (Text-Output NLG System, Handwritten NLG System) to being a Voice-Output NLG System (which may use a text-to-speech system).
  • It can range from being a Data-Driven Text Generation System to being an to being a Heuristic Text Generation System.
  • It can be supported by an NLG Platform.
  • …
• Example(s):
  • a Language Model-based NLG System, such as a GPT2-based NLG System.
  • a Neural-based NLG System, such as a GPT2-based NLG System.
  • a Document Summarization System, such as SQuASH.
  • a Sentence Generation System, such as a definitional sentence generator.
  • Narrative Science's Quill.
  • Writesonic's writing assistant.
  • SCIgen system.
  • Texar.
  • https://github.com/IBM/MAX-Review-Text-Generator.
  • https://github.com/keras-team/keras/blob/master/examples/lstm_text_generation.py
  • https://github.com/Skuldur/LSTM-Text-Generation.
  • …
  • Texygen (Texygen Benchmark Task).
  • an Automated Writing System.
  • …
• Counter-Example(s):
  • a Automated Language Understanding System.
  • a Software Program Generation System.
  • an Information Extraction System.
  • a Language-Capable Human.
  • a Text Editing System.
  • a Automated Speaking System.
• See: Generation System, Text Editing System, Linguistic Item Completion System, Automated Insights, Inc.‎.

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References

2018a

• (Clark et al., 2018) ⇒ Elizabeth Clark, Yangfeng Ji, and Noah A. Smith. (2018). “Neural Text Generation in Stories Using Entity Representations As Context.” In: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT), Volume 1 (Long Papers). DOI:10.18653/v1/N18-1204.

2018b

• (Fedus et al., 2018) ⇒ William Fedus, Ian Goodfellow, and Andrew M Dai. (2018). "MaskGAN: Better Text Generation via Filling in the ________". In: Proceedings of the Sixth International Conference on Learning Representations (ICLR-2018).

2018c

• (Guo et al., 2018) ⇒ Jiaxian Guo, Sidi Lu, Han Cai, Weinan Zhang, Yong Yu, and Jun Wang. (2018). “Long Text Generation via Adversarial Training with Leaked Information.” In: Proceedings of the Thirty-Second (AAAI) Conference on Artificial Intelligence (AAAI-18), the 30th innovative Applications of Artificial Intelligence (IAAI-18), and the 8th (AAAI) Symposium on Educational Advances in Artificial Intelligence (EAAI-18).

2018d

• (Kudo & Richardson, 2018) ⇒ Taku Kudo, and John Richardson. (2018). “SentencePiece: A Simple and Language Independent Subword Tokenizer and Detokenizer for Neural Text Processing.” In: arXiv preprint arXiv:1808.06226.

2018e

• (Lee et al., 2018) ⇒ Chris van der Lee, Emiel Krahmer, and Sander Wubben. (2018). “Automated Learning of Templates for Data-to-text Generation: Comparing Rule-based, Statistical and Neural Methods.” In: Proceedings of the 11th International Conference on Natural Language Generation (INLG 2018). DOI:http://dx.doi.org/10.18653/v1/W18-6504

2018f

• (Song et al., 2018) ⇒ Linfeng Song, Yue Zhang, Zhiguo Wang, and Daniel Gildea. (2018). “A Graph-to-Sequence Model for AMR-to-Text Generation.” In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (ACL 2018) Volume 1: Long Papers. DOI:10.18653/v1/P18-1150

2018g

• (Zhu et al., 2018) ⇒ Yaoming Zhu, Sidi Lu, Lei Zheng, Jiaxian Guo, Weinan Zhang, Jun Wang, and Yong Yu. (2018). “Texygen: A Benchmarking Platform for Text Generation Models.” In: Proceedings of The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval (SIGIR 2018). DOI:10.1145/3209978.3210080.

2017a

• (Zhang et al., 2017) ⇒ Yizhe Zhang, Zhe Gan, Kai Fan, Zhi Chen, Ricardo Henao, Dinghan Shen, and Lawrence Carin. (2017). "Adversarial Feature Matching for Text Generation". In: Proceedings of the 34th International Conference on Machine Learning (ICML 2017).

2017b

• (Li et al., 2017) ⇒ Jiwei Li, Will Monroe, Tianlin Shi, Sebastien Jean, Alan Ritter, and Dan Jurafsky. (2017). “Adversarial Learning for Neural Dialogue Generation.” In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing (EMNLP 2017). DOI:10.18653/v1/D17-1230.

2017c

• (Lin, Li, et al., 2017) ⇒ Kevin Lin, Dianqi Li, Xiaodong He, Ming-ting Sun, and Zhengyou Zhang. (2017). “Adversarial Ranking for Language Generation.” In: Proceedings of Advances in Neural Information Processing Systems 30 (NIPS-2017).

2017d

• (Che et al., 2017) ⇒ Tong Che, Yanran Li, Ruixiang Zhang, R. Devon Hjelm, Wenjie Li, Yangqiu Song, and Yoshua Bengio. (2017). “Maximum-Likelihood Augmented Discrete Generative Adversarial Networks.” In: ArXiv Preprint: 1702.07983.

2017e

• (Semeniuta et al., 2017) ⇒ Stanislau Semeniuta, Aliaksei Severyn, and Erhardt Barth. (2017). “A Hybrid Convolutional Variational Autoencoder for Text Generation.” In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing (EMNLP 2017). DOI:10.18653/v1/D17-1066.

2017f

• (Yu et al., 2017a) ⇒ Lantao Yu, Weinan Zhang, Jun Wang, and Yong Yu. (2017). “SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient.” In: Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence (AAAI 2017).

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