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Register a new userMagicPai at SemEval-2021 Task 7: Method for Detecting and Rating Humor Based on Multi-Task Adversarial Training
MagicPai في Semeval-2021 Task 7: طريقة للكشف عن الفكاهة وتصنيفها بناء على التدريب المشفر متعدد المهام
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تصف هذه الورقة نظام MagicPai لمهمة Semeval 2021 7، Hahackathon: الكشف عن الفكاهة والعموم.تهدف هذه المهمة إلى اكتشاف ما إذا كان النص روح الدعابة وكيف من روح الدعابة.هناك أربعة مجموعات فرعية في المسابقة.في هذه الورقة، نقدم ذلك بشكل أساسي حلنا، وهو نموذج تعليمي متعدد المهام يستند إلى أمثلة الخصومة، المهمة 1A و 1B.وبشكل أكثر تحديدا، نقوم أولا بتخفيف مجموعة البيانات التي تنظيفها وإضافة الاضطرابات للحصول على مزيد من تمثيلات تضمين أكثر قوة.ثم تصحح الخسارة عبر مستوى الثقة.أخيرا، نقوم بإجراء التعلم المشترك التفاعلي على مهام متعددة لالتقاط العلاقة بين ما إذا كان النص مضحك وما مدى دهبها.النتائج النهائية تظهر فعالية نظامنا.
This paper describes MagicPai's system for SemEval 2021 Task 7, HaHackathon: Detecting and Rating Humor and Offense. This task aims to detect whether the text is humorous and how humorous it is. There are four subtasks in the competition. In this paper, we mainly present our solution, a multi-task learning model based on adversarial examples, for task 1a and 1b. More specifically, we first vectorize the cleaned dataset and add the perturbation to obtain more robust embedding representations. We then correct the loss via the confidence level. Finally, we perform interactive joint learning on multiple tasks to capture the relationship between whether the text is humorous and how humorous it is. The final result shows the effectiveness of our system.
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This paper describes our system participated in Task 7 of SemEval-2021: Detecting and Rating Humor and Offense. The task is designed to detect and score humor and offense which are influenced by subjective factors. In order to obtain semantic informa
tion from a large amount of unlabeled data, we applied unsupervised pre-trained language models. By conducting research and experiments, we found that the ERNIE 2.0 and DeBERTa pre-trained models achieved impressive performance in various subtasks. Therefore, we applied the above pre-trained models to fine-tune the downstream neural network. In the process of fine-tuning the model, we adopted multi-task training strategy and ensemble learning method. Based on the above strategy and method, we achieved RMSE of 0.4959 for subtask 1b, and finally won the first place.
Humor recognition is a challenging task in natural language processing. This document presents my approaches to detect and rate humor and offense from the given text. This task includes 2 tasks: task 1 which contains 3 subtasks (1a, 1b, and 1c), and
task 2. Subtask 1a and 1c can be regarded as classification problems and take ALBERT as the basic model. Subtask 1b and 2 can be viewed as regression issues and take RoBERTa as the basic model.
This paper describes the winning system for SemEval-2021 Task 7: Detecting and Rating Humor and Offense. Our strategy is stacking diverse pre-trained language models (PLMs) such as RoBERTa and ALBERT. We first perform fine-tuning on these two PLMs wi
th various hyperparameters and different training strategies. Then a valid stacking mechanism is applied on top of the fine-tuned PLMs to get the final prediction. Experimental results on the dataset released by the organizer of the task show the validity of our method and we win first place and third place for subtask 2 and 1a.
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 em
ployees 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.
The HaHackathon: Detecting and Rating Humor and Offense'' task at the SemEval 2021 competition focuses on detecting and rating the humor level in sentences, as well as the level of offensiveness contained in these texts with humoristic tones. In this
paper, we present an approach based on recent Deep Learning techniques by both trying to train the models based on the dataset solely and by trying to fine-tune pre-trained models on the gigantic corpus.
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