team PN at TSAR -2022 Shared Task: Lexical Simplification using Multi-Level and Modular Approach (2022)

Abstract: Lexical Simplification is the process of reducing the lexical complexity of a text by replacing difficult words with easier-to-read (or understand) expressions while preserving the original information and meaning. This paper explains the work done by our team “teamPN” for the English track of TSAR 2022 Shared Task of Lexical Simplification. We created a modular pipeline which combines transformers based models with traditional NLP methods like paraphrasing and verb sense disambiguation. We created a multi-level and modular pipeline where the target text is treated according to its semantics (Part of Speech Tag). The pipeline is multi-level as we utilize multiple source models to find potential candidates for replacement. It is modular as we can switch the source models and their weighting in the final re-ranking.

## introduction as per tsar-2022 workshop shared task the problem definition is: "given a sentence containing a complex word, systems should return an ordered list of simpler valid substitutes for the complex word in its original context. the list of simpler words (up to a maximum of 10) returned by the system should be ordered by the confidence the system has in its prediction (best predictions first) and it must not contain ties." one example is shown in ## approach we parse the sentence using spacy @xcite and run different sets of modules for verb, noun and others respectively. our modules are explained in in detail as follows. see algorithm 1 for pseudo code of the pipeline. ## results as per tsar definition @xcite the evaluation metrics to be applied in the tsar-2022 shared task are the following: map@k (mean average precision @ k): @xmath0,3,5,10. the map@k metric is used to check whether the predicted word can be matched (relevant) or not matched (irrelevant) against the set of the gold-standard annotations for evaluation. potential@k: @xmath1,3,5,10. the percentage of instances for which at least one of the substitutions predicted is present in the set of gold annotations. accuracy@k@top1: @xmath2,2,3. the ratio of instances where at least one of the k top predicted candidates matches the most frequently suggested synonym/s in the gold list of annotated candidates. ## conclusion and future work we presented a novel approach where we combine power of transformer based models with traditional nlp. our work was restricted by computing resources. we would further like to improve on using more modules built out from complex transformers. also apart from ppdb we did not work with any other synonym dictionaries, adding more open source dictionary modules will bring on more variety. all of our code and documentation is available on github 5 .