Complexity-Weighted Loss and Diverse Reranking for Sentence Simplification (1904.02767v1)

Abstract: Sentence simplification is the task of rewriting texts so they are easier to understand. Recent research has applied sequence-to-sequence (Seq2Seq) models to this task, focusing largely on training-time improvements via reinforcement learning and memory augmentation. One of the main problems with applying generic Seq2Seq models for simplification is that these models tend to copy directly from the original sentence, resulting in outputs that are relatively long and complex. We aim to alleviate this issue through the use of two main techniques. First, we incorporate content word complexities, as predicted with a leveled word complexity model, into our loss function during training. Second, we generate a large set of diverse candidate simplifications at test time, and rerank these to promote fluency, adequacy, and simplicity. Here, we measure simplicity through a novel sentence complexity model. These extensions allow our models to perform competitively with state-of-the-art systems while generating simpler sentences. We report standard automatic and human evaluation metrics.

Complexity-Weighted Loss and Diverse Reranking for Sentence Simplification

The paper "Complexity-Weighted Loss and Diverse Reranking for Sentence Simplification" presents innovative approaches to address the limitations of existing sequence-to-sequence (Seq2Seq) models when applied to sentence simplification tasks. The authors identify two primary techniques to enhance simplification quality: complexity-weighted loss adjustments during training and diverse reranking strategies at test time. This combination empowers their model to produce shorter and simpler sentences while simultaneously competing with state-of-the-art systems on fluency and adequacy.

Seq2Seq models, widely applied in tasks like machine translation and summarization, often struggle with text simplification due to tendencies of content redundancy and complexity retention. This research confronts these issues by introducing a loss function that incorporates content word complexities based on a predictive model. By doing so, the model emphasizes simplifying content during training, aiming to reduce unnecessary complexities in generated sentences. Experimental results show that this complexity-weighted loss function improves simplification, adjusting probabilities to favor simpler word choices.

At test time, the authors leverage a reranking strategy based on fluency, adequacy, and their novel simplicity measure. This reranking process involves generating a plethora of candidate simplifications and applying metrics to select outputs that best balance the three aspects. A key component in their methodology is the use of diverse candidate generation, incorporating a similarity penalty during beam search and clustering techniques post-inference. Such methods ensure structural variation among candidate outputs, improving the potential for selecting high-quality simplifications.

Empirical evaluations compare the authors' models against prominent baselines in the field, including the DRESS (reinforcement learning) and DMASS (memory augmentation) systems. The proposed model consistently yields simplifications with lower Flesch-Kincaid Grade Level scores, indicating successful complexity reduction. It also demonstrates competitive SARI scores—a metric specifically tailored for measuring simplification quality—indicating that the model's outputs are recognized for correctly performing operations like word retention, insertion, and deletion relative to reference sentences.

The authors conduct a thorough human evaluation complementing the automatic metrics, revealing a nuanced understanding of sentence fluency and adequacy. Intriguingly, they report findings that implicate sentence length as a factor affecting perceived adequacy, with shorter sentences sometimes suggested to compromise meaning preservation.

This novel approach to sentence simplification posits significant practical implications, especially in domains requiring accessible language adaptations. By enhancing the simplicity model without sacrificing meaning, this research advances computational linguistics tools aimed at improving readability for diverse audiences, including individuals with reading disabilities and language learners. The insights provided by error analysis further suggest areas for improvement, emphasizing the need for better alignment methodologies and advanced anaphora resolution within future model iterations.

Overall, this research not only provides valuable contributions to the text simplification field but also suggests potential applications in broader AI tasks, such as personalized content delivery and adaptive learning systems. Future work should explore extended domain applications and investigate optimization of simplification quality, balancing structural and lexical adjustments to further improve accessibility and readability.