What are the baseline models?
PBMT-R, Hybrid, SBMT+PPDB+SARI, DRESS-LS, Pointer+Ent+Par, NTS+SARI, NSELSTM-S and DMASS+DCSS
Text simplification aims at making a text easier to read and understand by simplifying grammar and structure while keeping the underlying information identical. It is often considered an all-purpose generic task where the same simplification is suitable for all; however multiple audiences can benefit from simplified text in different ways. We adapt a discrete parametrization mechanism that provides explicit control on simplification systems based on Sequence-to-Sequence models. As a result, users can condition the simplifications returned by a model on parameters such as length, amount of paraphrasing, lexical complexity and syntactic complexity. We also show that carefully chosen values of these parameters allow out-of-the-box Sequence-to-Sequence models to outperform their standard counterparts on simplification benchmarks. Our model, which we call ACCESS (as shorthand for AudienCe-CEntric Sentence Simplification), increases the state of the art to 41.87 SARI on the WikiLarge test set, a +1.42 gain over previously reported scores.
In Natural Language Processing, the Text Simplification task aims at making a text easier to read and understand. Text simplification can be beneficial for people with cognitive disabilities such as aphasia BIBREF0, dyslexia BIBREF1 and autism BIBREF2 but also for second language learners BIBREF3 and people with low literacy BIBREF4. The type of simplification needed for each of these audiences is different. Some aphasic patients struggle to read sentences with a high cognitive load such as long sentences with intricate syntactic structures, whereas second language learners might not understand texts with rare or specific vocabulary. Yet, research in text simplification has been mostly focused on developing models that generate a single generic simplification for a given source text with no possibility to adapt outputs for the needs of various target populations. In this paper, we propose a controllable simplification model that provides explicit ways for users to manipulate and update simplified outputs as they see fit. This work only considers the task of Sentence Simplification (SS) where the input of the model is a single source sentence and the output can be composed of one sentence or splitted into multiple. Our work builds upon previous work on controllable text generation BIBREF5, BIBREF6, BIBREF7, BIBREF8 where a Sequence-to-Sequence (Seq2Seq) model is modified to control attributes of the output text. We tailor this mechanism to the task of SS by considering relevant attributes of the output sentence such as the output length, the amount of paraphrasing, lexical complexity, and syntactic complexity. To this end, we condition the model at train time, by feeding those parameters along with the source sentence as additional inputs. Our contributions are the following: (1) We adapt a parametrization mechanism to the specific task of Sentence Simplification by choosing relevant parameters; (2) We show through a detailed analysis that our model can indeed control the considered attributes, making the simplifications potentially able to fit the needs of various end audiences; (3) With careful calibration, our controllable parametrization improves the performance of out-of-the-box Seq2Seq models leading to a new state-of-the-art score of 41.87 SARI BIBREF9 on the WikiLarge benchmark BIBREF10, a +1.42 gain over previous scores, without requiring any external resource or modified training objective.