BennettNLP at SemEval-2021 Task 5: Toxic Spans Detection using Stacked Embedding Powered Toxic Entity Recognizer

Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021), pages 130–137 Bangkok, Thailand (online), August 5–6, 2021. ©2021 Association for Computational Linguistics 130 TUDA-CCL at SemEval-2021 Task 1: Using Gradient-boosted Regression Tree Ensembles Trained on a Heterogeneous Feature Set for Predicting Lexical Complexity Sebastian Gombert and Sabine Bartsch Corpus and Computational Linguistics, English Philology Institute of Linguistics and Literary Studies Technische Universit¨at Darmstadt, Germany sebastiang@outlook.de, sabine.bartsch@tu-darmstadt.de Abstract In this paper, we present our systems submitted to SemEval-2021 Task 1 on lexical complexity prediction (Shardlow et al., 2021a). The aim of this shared task was to create systems able to predict the lexical complexity of word to- kens and bigram multiword expressions within a given sentence context, a continuous value in- dicating the difficulty in understanding a respec- tive utterance. Our approach relies on gradient boosted regression tree ensembles fitted using a heterogeneous feature set combining linguistic features, static and contextualized word embed- dings, psycholinguistic norm lexica, WordNet, word- and character bigram frequencies and inclusion in word lists to create a model able to assign a word or multiword expression a context-dependent complexity score. We can show that especially contextualised string em- beddings (Akbik et al., 2018) can help with predicting lexical complexity. 1 Introduction In this paper, we present our contribution to SemEval-2021 Shared Task 1 (Shardlow et al., 2021a), a shared task focused on the topic of lex- ical complexity prediction. The term lexical com- plexity prediction describes the task of assigning a word or multiword expression a continuous or discrete score signifying its likeliness of being un- derstood well within a given context, especially by a non-native speaker. Solving this task could benefit second-language learners and non-native speakers in various ways. One could imagine using such scores to extract vocabulary lists appropri- ate for a learner level from corpora and literature (Alfter and Volodina, 2018), to judge if a given piece of literature fits a learner’s skill or to assist authors of textbooks in finding a level of textual difficulty appropriate for a target audience. Predicting these scores can be formulated as a regression problem. Our approach to solve this problem relies on gradient-boosted regression tree ensembles which we fit on a heterogeneous feature set including different word embedding models, linguistic features, WordNet features, psycholin- guistic lexica, corpus-based word frequencies and word lists. We assumed that lexical complexity could be correlated with a wide range of features, neural ones as much as distributional or psycholin- guistic ones, which is why we chose to use an ensemble-based method in the form of gradient boosting (Mason et al., 1999) for our system as it usually performs best for tasks where such a fea- ture set is needed compared to solely neural models which need dense, homogeneous input data to per- form well. Out of all participants, our systems were ranked 15/54 in the single word- and 19/37 in the multi- word category during the official shared task eval- uations according to Pearson’s correlation coeffi- cient. Our key discovery is …

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