| Challenge: | Existing approaches to text generation fail to capture higher-level structure in text, for example, rhyming patterns. |
| Approach: | They propose a method that uses a structured discriminator to learn rhyming constraints from poetry . the discriminator compares two English poetry datasets based on a learned similarity matrix . |
| Outcome: | The proposed method can learn rhyming patterns in English poetry without explicit phonetic information. |
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| Challenge: | Existing models for automatic poetry generation lack term novelty and thematic consistency. |
| Approach: | They propose a conditional variational autoencoder with adversarial training for classical Chinese poem generation. |
| Outcome: | The proposed model outperforms existing models on a large poetry corpus on 'classical Chinese' . it generates poems with novel terms and learns their thematic consistency with their titles. |
Constrained Language Models for Interactive Poem Generation (2022.lrec-1)
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Andrei Popescu-Belis, Àlex Atrio, Valentin Minder, Aris Xanthos, Gabriel Luthier, Simon Mattei, Antonio Rodriguez
| Challenge: | Neural language models cannot learn constraints from data, which is scarce for a well-resourced language such as French. |
| Approach: | They propose a system that combines neural language models with constraints that can be set by users on form, topic, emotion, and rhyming scheme. |
| Outcome: | The proposed system generates poems and stanzas using LMs and rule-based algorithms . it has been demonstrated at public events and log analysis shows that users found it engaging . |
The Mechanical Bard: An Interpretable Machine Learning Approach to Shakespearean Sonnet Generation (2023.acl-short)
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| Challenge: | Rather than train a model to obey these constraints implicitly, we opt to enforce them explicitly using a simple but novel approach to generation. |
| Approach: | They propose to automate the generation of sonnets within preset poetic constraints using a constrained decoding approach that uses a relatively modest neural backbone. |
| Outcome: | The proposed method produces sonnets that adhere to the genre’s defined constraints and contain lyrical language and literary devices. |
Automatic Poetry Generation with Mutual Reinforcement Learning (D18-1)
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| Challenge: | Existing models for automatic poetry generation are based on maximum likelihood estimation (MLE) MLE-based models tend to remember common patterns of the poetry corpus, which results in loss-evaluation mismatch. |
| Approach: | They propose to model the criteria and use them as explicit rewards to guide gradient update by reinforcement learning to motivate the model to pursue higher scores. |
| Outcome: | The proposed model outperforms the current state-of-the-art model and improves on Chinese poetry. |
It’s Morphin’ Time! Combating Linguistic Discrimination with Inflectional Perturbations (2020.acl-main)
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| Challenge: | Existing work on societal bias in NLP focuses on race and gender . linguistic background is a unique attribute that has been largely ignored in the field . |
| Approach: | They examine linguistic background to craft plausible adversarial examples that expose biases in popular NLP models. |
| Outcome: | The proposed model improves robustness without sacrificing performance on clean data. |
Automatic Poetry Generation from Prosaic Text (2020.acl-main)
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| Challenge: | In recent years, successful approaches have emerged to accurately model various aspects of natural language. |
| Approach: | They propose to combine neural networks with a poetry generation system that only uses standard text as input . they use standard text to model syntactic well-formedness and topical coherence . |
| Outcome: | The proposed framework is applied to the generation of poems in English and French . it uses standard, non-poetic text and its output is constrained to confer a poetic character . |
LexicalAT: Lexical-Based Adversarial Reinforcement Training for Robust Sentiment Classification (D19-1)
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| Challenge: | Existing text classification models are fragile and sensitive to simple perturbations. |
| Approach: | They propose a generator-classifier adversarial training approach to improve classification models . they use a large-scale lexical knowledge base to generate attacking examples . |
| Outcome: | The proposed approach outperforms strong baselines and reduces test errors on neural networks. |
Adversarial Grammatical Error Correction (2020.findings-emnlp)
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| Challenge: | Experimental results show that adversarial-GEC can achieve competitive GEC quality compared to NMT-based baselines. |
| Approach: | They propose an adversarial approach to Grammatical Error Correction using a transformer-based model and a sentence-pair classification model. |
| Outcome: | The proposed approach achieves competitive GEC quality compared to baselines. |
Adversarial Text Generation via Sequence Contrast Discrimination (2020.findings-emnlp)
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| Challenge: | Existing approaches to generate human-like texts are auto-regressive, but they suffer from exposure bias due to the dependence on the previous sampled output during the inferring phase. |
| Approach: | They propose a sequence contrast loss driven text generation framework which learns the difference between real texts and generated texts and uses that difference. |
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An End-to-End Generative Architecture for Paraphrase Generation (D19-1)
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| Challenge: | Existing methods for generating paraphrases with linguistic knowledge are often domain specific and hard to scale, or yield inferior results. |
| Approach: | They propose an end-to-end conditional generative architecture for generating paraphrases via adversarial training which does not depend on extra linguistic information. |
| Outcome: | The proposed method outperforms existing models on automatic metrics and human evaluations on four public datasets. |