Meta-learning for Compressed Language Model: A Multiple Choice Question Answering Study

2021 ◽  
Author(s):  
Ming Yan ◽  
Yi Pan
Keyword(s):  
Question Answering ◽  
Language Model ◽  
Multiple Choice ◽  
Multiple Choice Question ◽  
Meta Learning

scholarly journals Multi-source Meta Transfer for Low Resource Multiple-Choice Question Answering

2020 ◽  
Author(s):  
Ming Yan ◽  
Hao Zhang ◽  
Di Jin ◽  
Joey Tianyi Zhou
Keyword(s):  
Question Answering ◽  
Multiple Choice ◽  
Multiple Choice Question ◽  
Low Resource

scholarly journals Multiple-Choice Question Answering Models for Automatic Depression Severity Estimation

2021 ◽  
Vol 7 (1) ◽  
pp. 23
Author(s):  
Jorge Gabín ◽  
Anxo Pérez ◽  
Javier Parapar
Keyword(s):  
Social Media ◽  
Question Answering ◽  
Multiple Choice ◽  
Multiple Choice Question ◽  
Self Report ◽  
Training Data ◽  
Language Models ◽  
Depression Severity ◽  
Suitable Alternative ◽  
Severity Estimation

Depression is one of the most prevalent mental health diseases. Although there are effective treatments, the main problem relies on providing early and effective risk detection. Medical experts use self-reporting questionnaires to elaborate their diagnosis, but these questionnaires have some limitations. Social stigmas and the lack of awareness often negatively affect the success of these self-report questionnaires. This article aims to describe techniques to automatically estimate the depression severity from users on social media. We explored the use of pre-trained language models over the subject’s writings. We addressed the task “Measuring the Severity of the Signs of Depression” of eRisk 2020, an initiative in the CLEF Conference. In this task, participants have to fill the Beck Depression Questionnaire (BDI-II). Our proposal explores the application of pre-trained Multiple-Choice Question Answering (MCQA) models to predict user’s answers to the BDI-II questionnaire using their posts on social media. These MCQA models are built over the BERT (Bidirectional Encoder Representations from Transformers) architecture. Our results showed that multiple-choice question answering models could be a suitable alternative for estimating the depression degree, even when small amounts of training data are available (20 users).

scholarly journals Learning to Classify the Wrong Answers for Multiple Choice Question Answering (Student Abstract)

2020 ◽  
Vol 34 (10) ◽  
pp. 13843-13844 ◽  
Author(s):  
Hyeondey Kim ◽  
Pascale Fung
Keyword(s):  
Problem Solving ◽  
Correct Answer ◽  
Question Answering ◽  
Multiple Choice ◽  
Multiple Choice Question ◽  
Improve Performance ◽  
Language Understanding ◽  
Wrong Answer ◽  
Novel Method ◽  
Machine Reading

Multiple-Choice Question Answering (MCQA) is the most challenging area of Machine Reading Comprehension (MRC) and Question Answering (QA), since it not only requires natural language understanding, but also problem-solving techniques. We propose a novel method, Wrong Answer Ensemble (WAE), which can be applied to various MCQA tasks easily. To improve performance of MCQA tasks, humans intuitively exclude unlikely options to solve the MCQA problem. Mimicking this strategy, we train our model with the wrong answer loss and correct answer loss to generalize the features of our model, and exclude likely but wrong options. An experiment on a dialogue-based examination dataset shows the effectiveness of our approach. Our method improves the results on a fine-tuned transformer by 2.7%.

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