Papers with neural text generation
The Amazing World of Neural Language Generation (2020.emnlp-tutorials)
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| 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. |
A Frustratingly Simple Decoding Method for Neural Text Generation (2024.lrec-main)
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| Challenge: | Neural text generation is notorious for repetitive loops and tedious outputs. |
| Approach: | They propose a method that penalizes future generation of repetitive content . they construct an anti-LM based on previously generated text . |
| Outcome: | The proposed method outperforms established baselines in terms of generation quality, decoding speed, and universality. |
NeuroLogic A*esque Decoding: Constrained Text Generation with Lookahead Heuristics (2022.naacl-main)
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Ximing Lu, Sean Welleck, Peter West, Liwei Jiang, Jungo Kasai, Daniel Khashabi, Ronan Le Bras, Lianhui Qin, Youngjae Yu, Rowan Zellers, Noah A. Smith, Yejin Choi
| Challenge: | Existing paradigms for text generation are left-to-right decoding from autoregressive language models. |
| Approach: | They propose a decoding algorithm that incorporates heuristic estimates of future cost that are efficient for large-scale language models. |
| Outcome: | The proposed method outperforms baselines on five generation tasks and achieves new state-of-the-art performance on table-to-text generation, constrained machine translation, and keyword-constrained generation. |
On-the-Fly Attention Modulation for Neural Generation (2021.findings-acl)
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Yue Dong, Chandra Bhagavatula, Ximing Lu, Jena D. Hwang, Antoine Bosselut, Jackie Chi Kit Cheung, Yejin Choi
| Challenge: | Degeneration of neural text is associated with insufficient learning of task-specific characteristics by the attention mechanism. |
| Approach: | They propose to use attention modulation to inject priors into inference to improve fluency, creativity, and commonsense reasoning in neural text generation models. |
| Outcome: | The proposed method improves fluency, creativity, and commonsense reasoning, and significantly reduces sentence-level repetition. |
Neural Text Generation in Stories Using Entity Representations as Context (N18-1)
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| Challenge: | Existing models of text generation that explicitly represent entities are based on the use of words and entities. |
| Approach: | They propose a neural model that explicitly represents entities mentioned in the text . they use vectors that are updated as the text proceeds to improve automatic evaluations . |
| Outcome: | The proposed model improves mention generation, sentence selection, and sentence generation. |
Towards Content Transfer through Grounded Text Generation (N19-1)
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| Challenge: | Recent work in neural natural language generation has attracted significant interest in controlling the form of text, such as style, persona, and wordiness. |
| Approach: | They propose a task where the task is to generate a next sentence in a document that fits its context and is grounded in . external textual source such as a news story. |
| Outcome: | The proposed task is based on 640k Wikipedia referenced sentences paired with the source articles to show significant improvements against baselines. |
Text Editing by Command (2021.naacl-main)
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| Challenge: | Recent work has focused on making such models more controllable and factually grounded. |
| Approach: | They propose a novel interactive text generation setting in which the user interacts with the system by issuing commands to edit existing text. |
| Outcome: | The proposed model outperforms baseline models and obtains positive results in automatic and human evaluations. |
Dependency-based Mixture Language Models (2022.acl-long)
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| Challenge: | Existing models to incorporate syntactic structures into neural language models have relied heavily on elaborate components for a specific language model, which makes them unwieldy in practice to fit into other models. |
| Approach: | They propose a dependency-based mixture language model that incorporates syntactic structures into neural language models by mixing previous dependency modeling probabilities with self-attention. |
| Outcome: | The proposed method can be easily and effectively applied to different neural language models while improving neural text generation on various tasks. |
Fˆ2-Softmax: Diversifying Neural Text Generation via Frequency Factorized Softmax (2020.emnlp-main)
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| Challenge: | Existing methods for text generation do not fully reflect the rich diversity of human language. |
| Approach: | They propose to use F2-Softmax and MefMax to train a balanced frequency distribution using a frequency class-based method. |
| Outcome: | The proposed methods improve the diversity and quality of generated texts. |
Smart To-Do: Automatic Generation of To-Do Items from Emails (2020.acl-main)
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| Challenge: | Using neural text generation, we generate To-Do items from emails where the sender has promised to perform an action. |
| Approach: | They propose a task and dataset for automatically generating To-Do items from emails where the sender has promised to perform an action. |
| Outcome: | The proposed task obtains BLEU and ROUGE scores of 0.23 and 0.63 for the task. |