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| 001 | 000046048349 | |
| 005 | 20230530104414 | |
| 007 | ta | |
| 008 | 200629s2020 ulkd bmAC 000c eng | |
| 040 | ▼a 211009 ▼c 211009 ▼d 211009 | |
| 085 | 0 | ▼a 0510 ▼2 KDCP |
| 090 | ▼a 0510 ▼b 6D36 ▼c 1117 | |
| 100 | 1 | ▼a 고미영, ▼g 高美煐 |
| 245 | 1 0 | ▼a Analyzing position bias in question answering system / ▼d Miyoung Ko |
| 260 | ▼a Seoul : ▼b Graduate School, Korea University, ▼c 2020 | |
| 300 | ▼a iv, 22장 : ▼b 도표 ; ▼c 26 cm | |
| 500 | ▼a 지도교수: 강재우 | |
| 502 | 0 | ▼a 학위논문(석사)-- ▼b 고려대학교 대학원, ▼c 컴퓨터·전파통신공학과, ▼d 2020. 8 |
| 504 | ▼a 참고문헌: 장 18-22 | |
| 530 | ▼a PDF 파일로도 이용가능; ▼c Requires PDF file reader(application/pdf) | |
| 653 | ▼a Question Answering | |
| 776 | 0 | ▼t Analyzing Position Bias in Question Answering System ▼w (DCOLL211009)000000232146 |
| 900 | 1 0 | ▼a Ko, Mi-young, ▼e 저 |
| 900 | 1 0 | ▼a 강재우, ▼g 姜在雨, ▼d 1969-, ▼e 지도교수 ▼0 AUTH(211009)151698 |
| 945 | ▼a KLPA |
Electronic Information
| No. | Title | Service |
|---|---|---|
| 1 | Analyzing position bias in question answering system (26회 열람) |
View PDF Abstract Table of Contents |
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| No. 1 | Location Science & Engineering Library/Stacks(Thesis)/ | Call Number 0510 6D36 1117 | Accession No. 123064864 | Availability Available | Due Date | Make a Reservation | Service |
| No. 2 | Location Science & Engineering Library/Stacks(Thesis)/ | Call Number 0510 6D36 1117 | Accession No. 123064865 | Availability Available | Due Date | Make a Reservation | Service |
Contents information
Abstract
Many extractive question answering models are trained to predict start and end positions of answers.
The choice of predicting answers as positions is mainly due to its simplicity and effectiveness.
In this study, we hypothesize that when the distribution of the answer positions is highly skewed in the training set (e.g., answers lie only in the $k$-th sentence of each passage), QA models predicting answers as positions learn spurious positional cues and fail to give answers in different positions.
We first illustrate this \textit{position bias} in popular extractive QA models such as BiDAF and BERT and thoroughly examine how position bias propagates through each layer of BERT.
To safely deliver position information without position bias, we train models with various de-biasing methods including entropy regularization and randomized position.
We found that reducing correlation between word positions and answers helps us to resolve position bias.
Table of Contents
Abstract Contents i List of Figures iii List of Tables iv 1 Introduction 1 2 Related Work 4 3 Analyzing Position Bias 6 3.1 Position Bias on Synthetic Datasets . . . . . . . . . . . . . . . . . . . . .6 3.2 Visualization of Position Bias . . . . . . . . . . . . . . . . . . . . . . . . .7 3.3 Generalizing to Different Positions . . . . . . . . . . . . . . . . . . . . . .11 4 De-biasing Position Bias12 4.1 Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .12 4.1.1Entropy Regularization . . . . . . . . . . . . . . . . . . . . . . . .12 4.1.2Randomized Position . . . . . . . . . . . . . . . . . . . . . . . . . .13 4.2 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .13 4.2.1Implementation Details . . . . . . . . . . . . . . . . . . . . . . . .13 4.2.2Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .13 5 Discussion 15 6 Conclusion 17 Bibliography 18
