P resi U niv at TSAR -2022 Shared Task: Generation and Ranking of Simplification Substitutes of Complex Words in Multiple Languages (2022)

Abstract: In this paper, we describe our approach to generate and rank candidate simplifications using pre-trained language models (Eg. BERT), publicly available word embeddings (Eg. FastText), and a part-of-speech tagger, to generate and rank candidate contextual simplifications for a given complex word. In this task, our system, PresiUniv, was placed first in the Spanish track, 5th in the Brazilian-Portuguese track, and 10th in the English track. We upload our codes and data for this project to aid in replication of our results. We also analyze some of the errors and describe design decisions which we took while writing the paper.

## introduction lexical simplification (ls) is a task of natural language generation that aims to substitute difficult words and phrases in a sentence for simpler ones that convey the same information @xcite . this is a challenging task because not only must the substitution retain the original meaning while still adhering to the grammatical requirements of the sentence that is being simplified, but different people may have different needs for simplification @xcite . figure 1 shows the pipeline for lexical simplification @xcite . in light of this, the tsar 2022 workshop organized a shared task on lexical simplification, where participating teams have to generate and rank simplifications for a given complex word @xcite . each team is allowed to submit three runs for their system. this paper describes the performance of our team, presiuniv 1 at this shared task. 1 code: http://www.github.com/lwsam/ tsar-2022/ ## problem statement our problem is defined as follows: hence, our task is divided into two sub-tasks. the first sub-task involves generating words that would replace a complex word in the target sentence, which would simplify it. the second subtask consists of ranking the top 10 most suitable words. ## related work lexical simplification must identify complex words and choose the optimal replacement @xcite . previous shared tasks have already been done as a part of semeval 2016 @xcite and bea 2018 @xcite . while the first shared task dealt with a single training and test set in english alone, the second shared task dealt with complex word identification in multiple languages (english, german, and spanish), as well as a multilingual scenario (where the system is tested in a fourth language, french). language pre-trained language model word vectors part-of-speech tagger english bert fasttext nltk pos tagger spanish beto fasttext stanford pos tagger brazilian portuguese bertimbeau nilc embeddings mac-morpho ## method figure 2 shows the different steps that we take to generate and select our candidates. it consists of the following steps: ## dataset there is no training dataset for the tsar-2022 shared task. a sample of 10 or 12 instances with gold standard annotations is provided here as the trial dataset. for the testing data, between 368 to 374 instances were given, with the annotations released upon the completion of the competition. ## results and analysis the results of our experiments on the testing dataset are given in ## conclusion and future work in this paper, we describe the participation of our team, presiuniv, in the tsar 2022 shared task on the generation and ranking of lexical simplification substitutes. overall, we achieved the best performance in the spanish track but finished 5th in the portuguese track and 10th in the english track. in the future, we plan to extend our work towards document-level simplification as well as personalized text simplification @xcite .