| Challenge: | Text generation has played an important role in various applications of natural language processing. |
| Approach: | They present different settings of stylized text generation and introduce machine learning methods to represent style. |
| Outcome: | This paper presents a comprehensive literature review on stylized text generation . it focuses on the challenges and future directions of stylized generation based on machine learning . |
Similar Papers
Automatic and Human-AI Interactive Text Generation (with a focus on Text Simplification and Revision) (2024.acl-tutorials)
Copied to clipboard
| Challenge: | In this tutorial, we focus on text-to-text generation, a class of natural language generation tasks, that takes a piece of text as input and then generates a revision that is improved according to some specific criteria. |
| Approach: | This tutorial focuses on text-to-text generation, a class of natural language generation tasks that takes a piece of text as input and generates a revision that is improved according to some specific criteria. |
| Outcome: | This tutorial focuses on text-to-text generation, a class of natural language generation tasks, that takes a piece of text as input and generates a revision that is improved according to some specificcriteria. |
Synthetic Data in the Era of Large Language Models (2025.acl-tutorials)
Copied to clipboard
| Challenge: | 'synthetic data' is a data generated with the assistance of large language models to make dataset construction faster and cheaper. |
| Approach: | This tutorial seeks to build a shared understanding of recent progress in synthetic data generation from NLP and related fields by grouping and describing major methods, applications, and open problems. |
| Outcome: | This tutorial will describe methods, applications, and open problems that have been developed and are being used to improve the quality and efficiency of synthetic data generation. |
Sentence-Level Content Planning and Style Specification for Neural Text Generation (D19-1)
Copied to clipboard
| Challenge: | Recent advances in text generation systems often produce incoherent and unfaithful outputs . a novel automated text generation system takes into account content selection, text planning, and surface realization. |
| Approach: | They propose an end-to-end trained two-step text generation model that considers sentence-level content planners and language styles. |
| Outcome: | The proposed model outperforms competing models in three domains with diverse topics and varying language styles. |
StyleDGPT: Stylized Response Generation with Pre-trained Language Models (2020.findings-emnlp)
Copied to clipboard
| Challenge: | Existing methods for generating responses following a desired style are lacking of parallel data for training. |
| Approach: | They propose a KL loss and a style classifier to fine-tune response generation . they show that their model can significantly outperform state-of-the-art methods . |
| Outcome: | The proposed model outperforms state-of-the-art models in style consistency and contextual coherence with two public datasets. |
Creative Natural Language Generation (2023.emnlp-tutorial)
Copied to clipboard
| Challenge: | This tutorial aims to bring awareness of the important and emerging research area of open-domain creative generation. |
| Approach: | They will review recent studies on creative language generation at sentence level as well as longer forms of text. |
| Outcome: | This paper reviews recent studies on creative language generation at sentence level as well as longer forms of text. |
Exploring Controllable Text Generation Techniques (2020.coling-main)
Copied to clipboard
| Challenge: | Neural controllable text generation has a plethora of applications but there is no unifying theme. |
| Approach: | They propose a new schema for the control of attributes in the generation process by classifying it into five modules and providing an analysis on the advantages and disadvantages of these techniques. |
| Outcome: | The proposed frameworks can be used to control the attributes of natural sentences and to modulate the formality and politeness of emails. |
The Amazing World of Neural Language Generation (2020.emnlp-tutorials)
Copied to clipboard
| Challenge: | Recent years have seen a paradigm shift in neural text generation due to advances in deep contextual language modeling and transfer learning. |
| Approach: | They will discuss how and why NLG models succeed/fail at generating coherent text. |
| Outcome: | This paper will discuss how and why these models succeed/fail at generating coherent text, and provide insights on several applications. |
Generating Text from Language Models (2023.acl-tutorials)
Copied to clipboard
| Challenge: | a growing percentage of natural language processing tasks focus on the generation of text from probabilistic language models. |
| Approach: | They will provide a centralized discussion of critical considerations when choosing how to generate from a language model. |
| Outcome: | This tutorial will provide a centralized discussion of critical considerations when choosing how to generate from a language model. |
Detecting Machine-Generated Text: Techniques and Challenges (2024.acl-tutorials)
Copied to clipboard
| Challenge: | This tutorial focuses on machine-generated text and deepfakes. |
| Approach: | This tutorial aims to provide a comprehensive overview of text detection techniques . it will focus on machine-generated text and deepfakes . |
| Outcome: | This tutorial focuses on machine-generated text and deepfakes. |
Natural Language Generation: Recently Learned Lessons, Directions for Semantic Representation-based Approaches, and the Case of Brazilian Portuguese Language (P19-2)
Copied to clipboard
| Challenge: | Natural Language Generation (NLG) is a promising area in Natural Language Processing (NLP) . |
| Approach: | They present a review of the literature on Natural Language Generation in Brazilian Portuguese. |
| Outcome: | The proposed approaches are based on the Abstract Meaning Representation formalism and have potential future directions. |