Automated Language Generation (NLG) System
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An Automated Language Generation (NLG) System is an automated writing system that implements an NLG algorithm to solve an NLG task.
- AKA: Natural Language Generator, Text Generation System, NLG Engine, Automated Text Producer, Language Production System.
- Context:
- It can typically apply an NLG Model with computational linguistics techniques.
- It can typically transform structured data into natural language text through generation pipeline processes.
- It can typically maintain linguistic quality through natural language processing controls.
- It can typically handle specialized vocabulary through domain lexicon management.
- It can typically produce human-readable output with linguistic coherence mechanisms.
- It can typically follow discourse patterns through rhetorical structure analysis.
- It can typically preserve contextual relations through anaphora resolution systems.
- ...
- It can often facilitate content automation through template adaptation capabilities.
- It can often provide multimodal integration through cross-media representation frameworks.
- It can often implement personalization features through user model incorporation.
- It can often support multilingual generation through language-specific rule application.
- It can often optimize output quality through evaluation feedback loops.
- It can often manage content consistency through document-level planning approaches.
- It can often adapt stylistic variation through register-specific parameter tuning.
- It can often ensure knowledge accuracy through fact verification mechanisms.
- ...
- It can range from being a General NLG System to being a Automated Domain-Specific NLG System, depending on its application scope.
- It can range from being a Automated Written Language Generation System (Text-Output NLG System, Handwritten NLG System) to being a Voice-Output NLG System (which may use a text-to-speech system), depending on its output modality.
- It can range from being a Data-Driven Text Generation System to being a Heuristic Text Generation System, depending on its underlying approach.
- It can range from being a Rule-Based NLG System to being a Neural NLG System, depending on its technological foundation.
- It can range from being a Simple Template System to being a Complex Generative Model, depending on its architectural sophistication.
- It can range from being a Deterministic Generator to being a Probabilistic Generator, depending on its output variability.
- It can range from being a Single-Stage Generator to being a Multi-Stage Pipeline, depending on its processing architecture.
- ...
- It can have Content Planning Modules for discourse structure organization.
- It can have Sentence Realization Components for grammatical text formation.
- It can have Lexicalization Processes for word choice optimization.
- It can have Reference Expression Generation mechanisms for entity coherence maintenance.
- It can have Surface Realization Modules for final text production.
- It can have Document Structuring Components for rhetorical relation management.
- It can have Aggregation Mechanisms for sentence combination optimization.
- It can have Discourse Marker Selection tools for textual coherence enhancement.
- It can have Linguistic Quality Assurance systems for output verification.
- ...
- It can be Context Aware during situational text generation.
- It can be Style Constrained during brand communication production.
- It can be Fact Grounded during information-based content creation.
- It can be Audience Adapted during targeted communication delivery.
- It can be Data Dependent during report generation process.
- It can be Format Restricted during structured document creation.
- It can be supported by an NLG Platform during enterprise deployment.
- It can incorporate machine learning techniques such as deep learning and reinforcement learning.
- It can be designed for various purposes such as content creation, summarization, translation, or dialogue generation.
- It can face challenges related to coherence, factual accuracy, and ethical considerations.
- It can be evaluated using both automatic metrics and human evaluation.
- ...
- Examples:
- Model-Based NLG Systems, such as:
- Language Model-based NLG Systems, such as:
- GPT-based NLG System for open-domain text generation.
- BERT-based NLG System for context-aware text production.
- T5-based Generation System for diverse text format conversion.
- LLaMA-based NLG System for large-scale text generation.
- Neural-based NLG Systems, such as:
- LSTM-based Text Generator for sequential content creation.
- Transformer-based Generator for parallel text processing.
- GAN-based Text Generator for adversarial text production.
- VAE-based Text Generator for diverse output generation.
- Language Model-based NLG Systems, such as:
- Task-Specific NLG Systems, such as:
- Document Summarization Systems, such as:
- SQuASH for scientific document summarization.
- News Summarization Engine for news article condensation.
- Extractive-Abstractive Hybrid Summarizer for comprehensive summary creation.
- Multi-Document Summarization System for information synthesis across sources.
- Sentence Generation Systems, such as:
- Definitional Sentence Generator for term explanation creation.
- Descriptive Sentence System for entity characterization.
- Question Generation System for educational material creation.
- Simplification System for complex sentence transformation.
- Machine Translation Systems, such as:
- Neural Machine Translator for cross-language content conversion.
- Statistical Machine Translation System for language transformation.
- Rule-Based Translation System for controlled domain translation.
- Hybrid Translation Architecture for optimized translation quality.
- Dialogue Generation Systems, such as:
- Document Summarization Systems, such as:
- Commercial NLG Systems, such as:
- Narrative Science's Quill for business intelligence reporting.
- Writesonic's writing assistant for marketing content creation.
- Arria NLG Platform for financial report generation.
- Automated Insights' Wordsmith for data-driven narrative production.
- AX Semantics for e-commerce description creation.
- Yseop for financial document automation.
- Research-Oriented NLG Systems, such as:
- SCIgen system for academic paper generation.
- Texar for text generation research.
- OpenAI GPT Models for natural language generation research.
- CTRL for controllable text generation experimentation.
- BART for sequence-to-sequence generation research.
- MEGA for efficient text generation investigation.
- Open-Source NLG Implementations, such as:
https://github.com/IBM/MAX-Review-Text-Generatorfor review text generation.https://github.com/keras-team/keras/blob/master/examples/lstm_text_generation.pyfor character-level generation implementation.https://github.com/Skuldur/LSTM-Text-Generationfor sequence generation experimentation.- HuggingFace Transformers Library for pre-trained model utilization.
- SimpleNLG for rule-based realization development.
- PyText for production NLG system implementation.
- NLG Benchmarking Systems, such as:
- Texygen (Texygen Benchmark Task) for generation quality evaluation.
- GEM Benchmark for natural language generation assessment.
- BLEU Evaluation System for translation quality measurement.
- ROUGE Framework for summarization effectiveness quantification.
- BERTScore for semantic similarity measurement.
- BLEURT for learned evaluation metric application.
- General Purpose NLG Systems, such as:
- Automated Writing Systems for general content creation.
- Multi-domain Text Generators for versatile text production.
- Universal Language Generators for cross-domain text generation.
- Large Language Model Systems for diverse text application support.
- Specialized NLG Systems, such as:
- Data-to-Text Systems, such as:
- Weather Report Generator for meteorological data communication.
- Sports Summary Generator for game statistics narration.
- Code Generation Systems, such as:
- Creative Text Generators, such as:
- Story Generation System for narrative content creation.
- Poetry Generation System for verse composition.
- Data-to-Text Systems, such as:
- ...
- Model-Based NLG Systems, such as:
- Counter-Examples:
- An Automated Language Understanding System, which analyzes rather than generates natural language.
- A Software Program Generation System, which produces executable code rather than natural language.
- An Information Extraction System, which identifies structured data from unstructured text rather than generating text.
- A Language-Capable Human, which uses cognitive processes rather than computational algorithms for language generation.
- A Text Editing System, which modifies existing text rather than generating new content.
- An Automated Speaking System, which focuses on voice synthesis rather than content generation.
- A Data Visualization System, which transforms data into graphical representations rather than textual descriptions.
- A Language Training System, which teaches language skills rather than producing language output.
- A Text Classification System, which categorizes existing content rather than creating new text.
- See: Generation System, Text Editing System, Linguistic Item Completion System, Automated Content Creation Tool, Natural Language Processing System, Human-Machine Communication System, Machine Learning for Text.
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).