Lexical Complexity Prediction (LCP) involves assigning a difficulty score to a particular word or expression, in a text intended for a target audience. In this paper, we introduce a deep learning system based on a pre-trained transformer encoder, for word and Multi-Word Expression (MWE) complexity prediction. The proposed model is trained to minimize both the Root Mean Square Error (RMSE) and the auxiliary loss associated to the negative Pearson Correlation. The two losses are combined using the uncertainty loss weighting. We investigate two pre-trained transformer networks, namely BERT and RoBERTa. Moreover, we evaluate both single-task and joint training of word and MWE complexity prediction sub-tasks.

A Deep Learning Model-based Pre-trained Transformer Encoder for Lexical Complexity

Humor detection has become a topic of interest for several research teams, especially those involved in socio-psychological studies, with the aim to detect the humor and the temper of a targeted population (e.g. a community, a city, a country, the employees of a given company). Most of the existing studies have formulated the humor detection problem as a binary classification task, whereas it revolves around learning the sense of humor by evaluating its different degrees. In this paper, we propose an end-to-end deep Multi-Task Learning (MTL) model to detect and rate humor and offense. It consists of a pre-trained transformer encoder and task-specific attention layers. The model is trained using MTL uncertainty loss weighting to adaptively combine all sub-tasks objective functions. Our MTL model tackles all sub-tasks of the SemEval-2021 Task-7 in one end-to-end deep learning system and shows very promising results.

CS-UM6P at SemEval-2021 Task 7: Deep Multi-Task Learning Model for Detecting and Rating Humor and Offense

SemEval-2021, 15th International Workshop on Semantic Evaluations

workshop paper

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A Deep Learning Model-based Pre-trained Transformer Encoder for Lexical Complexity

CS-UM6P at SemEval-2021 Task 7: Deep Multi-Task Learning Model for Detecting and Rating Humor and Offense

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