Text simplification has emerged as an increasingly useful application of AI
for bridging the communication gap in specialized fields such as medicine,
where the lexicon is often dominated by technical jargon and complex
constructs. Despite notable progress, methods in medical simplification
sometimes result in the generated text having lower quality and diversity. In
this work, we explore ways to further improve the readability of text
simplification in the medical domain. We propose (1) a new unlikelihood loss
that encourages generation of simpler terms and (2) a reranked beam search
decoding method that optimizes for simplicity, which achieve better performance
on readability metrics on three datasets. This study's findings offer promising
avenues for improving text simplification in the medical field.

本文研究了在医学领域中进一步提高文本简化可读性的方法，通过提出新的非概然性损失函数和重新排序的束搜索解码方法，在三个数据集上取得了更好的可读性指标表现，这些研究结果为改善医学领域的文本简化提供了有希望的途径。

医疗文本简化：通过不太可能的训练和重新排序的束搜索解码来优化可读性

Medical Text Simplification: Optimizing for Readability with  Unlikelihood Training and Reranked Beam Search Decoding

Imbalanced token distributions naturally exist in text documents, leading
neural language models to overfit on frequent tokens. The token imbalance may
dampen the robustness of radiology report generators, as complex medical terms
appear less frequently but reflect more medical information. In this study, we
demonstrate how current state-of-the-art models fail to generate infrequent
tokens on two standard benchmark datasets (IU X-RAY and MIMIC-CXR) of radiology
report generation. % However, no prior study has proposed methods to adapt
infrequent tokens for text generators feeding with medical images. To solve the
challenge, we propose the \textbf{T}oken \textbf{Im}balance Adapt\textbf{er}
(\textit{TIMER}), aiming to improve generation robustness on infrequent tokens.
The model automatically leverages token imbalance by an unlikelihood loss and
dynamically optimizes generation processes to augment infrequent tokens. We
compare our approach with multiple state-of-the-art methods on the two
benchmarks. Experiments demonstrate the effectiveness of our approach in
enhancing model robustness overall and infrequent tokens. Our ablation analysis
shows that our reinforcement learning method has a major effect in adapting
token imbalance for radiology report generation.

本研究提出了一种名为 TIMER 的方法来解决放射学报告生成中，由于词汇分布不均衡导致的常见词汇过拟合问题。该方法通过无似然损失自适应平衡并优化词汇生成过程，有效提高了模型的生成鲁棒性和生成不常见词的能力，同时与先进方法比较，使用强化学习得出的结果更为优越。

放射医学报告生成的令牌 imbalance 适应

Token Imbalance Adaptation for Radiology Report Generation

Generative dialogue models currently suffer from a number of problems which
standard maximum likelihood training does not address. They tend to produce
generations that (i) rely too much on copying from the context, (ii) contain
repetitions within utterances, (iii) overuse frequent words, and (iv) at a
deeper level, contain logical flaws. In this work we show how all of these
problems can be addressed by extending the recently introduced unlikelihood
loss (Welleck et al., 2019) to these cases. We show that appropriate loss
functions which regularize generated outputs to match human distributions are
effective for the first three issues. For the last important general issue, we
show applying unlikelihood to collected data of what a model should not do is
effective for improving logical consistency, potentially paving the way to
generative models with greater reasoning ability. We demonstrate the efficacy
of our approach across several dialogue tasks.

本文使用扩展的反似然损失函数及正则化方法解决了生成式对话模型中含复制文本、重复、高频词汇和逻辑缺陷等问题，并通过采集数据并应用反似然来提高逻辑一致性，实现了具有更强推理能力的生成式对话模型，我们还在多项对话任务中验证了所提出的方法的有效性。