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.
@cite_29: Abstract : Three readability formulas were recalculated to be more suitable for Navy use. The three formulas are the Automated Readability Index (ARI), Fog Count, and Flesch Reading Ease Formula. They were derived from test results of 531 Navy enlisted personnel enrolled in four technical training schools. Personnel were tested for their reading comprehension level according to the comprehension section of the Gates-McGinitie reading test. At the same time, they were tested for their comprehension of 18 passages taken from Rate Training Manuals. Scores on the reading test and training material passages allowed the calculation of the grade level of the passages. This scaled reading grade level is based on Navy personnel reading Navy training material and comprehending it.
There are two main Seq2Seq models we will compare to in this work, along with the statistical model from . proposed DRESS (Deep REinforcement Sentence Simplification), a Seq2Seq model that uses a reinforcement learning framework at training time to reward the model for producing sentences that score high on fluency, adequacy, and simplicity. This work showed state-of-the-art results on human evaluation. However, the sentences generated by this model are in general longer than the reference simplifications. proposed DMASS (Deep Memory Augmented Sentence Simplification), a multi-layer, multi-head attention transformer architecture which also integrates simplification rules. This work has been shown to get state-of-the-art results in an automatic evaluation, training on the WikiLarge dataset introduced by . , however, does not perform a human evaluation, and restricting evaluation to automatic metrics is generally insufficient for comparing simplification models. Our model, in comparison, is able to generate shorter and simpler sentences according to Flesch-Kincaid grade level @cite_29 and human judgments, and provide a comprehensive analysis using human evaluation and a qualitative error analysis.