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. |
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| Challenge: | a new paper proposes and evaluates a set of loss functions that can be used to train models for representation learning . cross-entropy loss penalizes models when they fail to generate the exact word from ground truth data . |
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Discourse-Aware Neural Rewards for Coherent Text Generation (N18-1)
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| 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. |
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Advancing Semantic Textual Similarity Modeling: A Regression Framework with Translated ReLU and Smooth K2 Loss (2024.emnlp-main)
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| Challenge: | despite its efficiency, Sentence-BERT ignores the progressive nature of semantic relationships, despite a promising approach . contrastive learning methods have improved performance on renowned STS benchmarks, but they fail to leverage fine-grained information. |
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Beyond BLEU:Training Neural Machine Translation with Semantic Similarity (P19-1)
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| Challenge: | Recent work has shown that optimizing neural machine translation systems to directly improve evaluation metrics such as BLEU can improve final translation accuracy. |
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Rewarding Semantic Similarity under Optimized Alignments for AMR-to-Text Generation (2022.acl-short)
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| Challenge: | Automatic evaluation metrics score natural language generation systems based on how well they lexically align to humanannotated references. |
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Reweighting Strategy Based on Synthetic Data Identification for Sentence Similarity (2022.coling-1)
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| Challenge: | obtaining large amounts of human-annotated datasets to train a sentence embedding model is difficult and expensive. |
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3R: Enhancing Sentence Representation Learning via Redundant Representation Reduction (2025.emnlp-main)
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| Challenge: | Existing approaches to improve sentence representations lack fine-grained guidance on reducing redundant information. |
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Paraphrase Generation with Deep Reinforcement Learning (D18-1)
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| Challenge: | Paraphrase generation is an important but challenging task in natural language processing . traditional symbolic approaches to paraphrase generation include rule-based methods, thesaurus-based approaches and statistical machine translation (SMT) |
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Accelerated Reinforcement Learning for Sentence Generation by Vocabulary Prediction (N19-1)
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| Challenge: | In reinforcement learning-based sentence generation, the large action space is often too computa-tionally demanding to be used with large training data. |
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Retrieval Enhanced Model for Commonsense Generation (2021.findings-acl)
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| Challenge: | Existing frameworks for commonsense generation are lacking for pre-trained models. |
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