| Challenge: | Existing approaches to train text generation models using cross-entropy loss do not always correlate well with achieving high scores on commonly used evaluation measures. |
| Approach: | They propose to use discourse-aware rewards to model cross-sentence ordering to approximate desired discourse structure to train a model of long, coherent text. |
| Outcome: | The proposed model produces more coherent and less repetitive text than models trained with cross-entropy or with commonly used scores as rewards. |
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Long Text Generation by Modeling Sentence-Level and Discourse-Level Coherence (2021.acl-long)
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| Challenge: | Existing generation models struggle to maintain a coherent event sequence throughout the generated text. |
| Approach: | They propose a long text generation model which can represent prefix sentences at sentence level and discourse level in the decoding process. |
| Outcome: | The proposed model can generate more coherent texts than state-of-the-art models. |
MOCHA: A Multi-Task Training Approach for Coherent Text Generation from Cognitive Perspective (2022.emnlp-main)
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| Challenge: | Recent pre-trained language models have produced impressive results, but there is still a gap between human written texts and machine-generated outputs. |
| Approach: | They propose a multi-task training strategy for long text generation grounded on the cognitive theory of writing. |
| Outcome: | The proposed model achieves better results on three open-ended generation tasks than baselines. |
Improving Multi-party Dialogue Generation via Topic and Rhetorical Coherence (2024.emnlp-main)
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| Challenge: | Existing studies on multi-party dialogue generation focus on the reply-to structure of dialogue histories, but they neglect the coherence between generated responses and target utterances. |
| Approach: | They propose a Reinforcement Learning approach emphasizing Topic and Rhetorical Coherence to enhance the model's perception of coherence with the target utterance. |
| Outcome: | The proposed approach significantly outperforms the state-of-the-art baselines on two popular datasets. |
Discourse-Aware Soft Prompting for Text Generation (2022.emnlp-main)
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| Challenge: | Recent advances in pre-trained langauge models (PLMs) have made great impact on text generation research. |
| Approach: | They propose to use hierarchical blocking to simulate a higher-level discourse structure of human written text and attention sparsity to learn sparse transformations on the softmax-function. |
| Outcome: | The proposed methods perform better on some generation tasks but don't generalize across all generation tasks. |
Leveraging Discourse Rewards for Document-Level Neural Machine Translation (2020.coling-main)
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| Challenge: | Document-level machine translation models are often not trained to explicitly ensure discourse quality. |
| Approach: | They propose a method that explicitly optimizes lexical cohesion and coherence metrics by using a reinforcement learning objective. |
| Outcome: | The proposed approach improves document translations over four different languages and three translation domains while maintaining faithfulness to the reference translation. |
Factually Consistent Summarization via Reinforcement Learning with Textual Entailment Feedback (2023.acl-long)
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Paul Roit, Johan Ferret, Lior Shani, Roee Aharoni, Geoffrey Cideron, Robert Dadashi, Matthieu Geist, Sertan Girgin, Leonard Hussenot, Orgad Keller, Nikola Momchev, Sabela Ramos Garea, Piotr Stanczyk, Nino Vieillard, Olivier Bachem, Gal Elidan, Avinatan Hassidim, Olivier Pietquin, Idan Szpektor
| Challenge: | Recent advances in abstractive summarization systems produce factually inconsistent text . this is emphasized in tasks like summarizing, which often produce inconsistent text with no input article . |
| Approach: | They use reinforcement learning to optimize for factual consistency and explore trade-offs . they use textual-entailment rewards to optimize the accuracy of the generated summaries . |
| Outcome: | The proposed method improves faithfulness, salience and conciseness of the generated summaries. |
Using Semantic Similarity as Reward for Reinforcement Learning in Sentence Generation (P19-2)
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| Challenge: | Existing models for sentence generation use cross-entropy loss as the loss function . however, cross-etropy is unable to evaluate sentences as a whole and lacks flexibility . et al., 2018: a novel approach to improve sentence generation models . |
| Approach: | They propose a method to train a model using estimated semantic similarity between output and reference sentences to alleviate cross-entropy loss problems. |
| Outcome: | The proposed model improves the BLEU scores from the baseline LSTM NMT model. |
Reward Gaming in Conditional Text Generation (2023.acl-long)
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| Challenge: | Recent work has used reward functions learned from human annotations to align conditional text generation models with desired behaviors. |
| Approach: | They propose to use reinforcement learning to train conditional text generation models with reward functions learned from human annotations to align outputs with desired behaviors. |
| Outcome: | The proposed framework improves the quality of generated summaries by using saliency and faithfulness metrics. |
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. |
Text Simplification with Reinforcement Learning Using Supervised Rewards on Grammaticality, Meaning Preservation, and Simplicity (2020.aacl-srw)
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| Challenge: | Existing studies in text-to-text generation do not align with human-perspectives for these perspectives. |
| Approach: | They propose to use BERT regressors fine-tuned for grammaticality, meaning preservation, and simplicity as reward estimators to optimize rewards for text simplification. |
| Outcome: | The proposed method achieves text simplification conforming to human-perspectives. |