Incremental sense weight training for contextualized word embedding interpretation Público

Abstract

In this work, we propose a new training procedure for learning the importance of dimensions of word embeddings in representing word meanings. Our algorithm advanced in the interpretation filed of word embeddings, which are extremely critical in the NLP filed due to the lack of understanding of word embeddings despite their superior ability in progressing NLP tasks. Although previous work has investigated in the interpretability of word embeddings through imparting interpretability to the embedding training models or through post-processing procedures of pre-trained embeddings, our algorithm proposes a new perspective to word embedding dimension interpretation where each dimension gets evaluated and can be visualized. Also, our algorithm adheres to a novel assumption that not all dimensions are necessary for representing a word sense (word meaning) and dimensions that are negligible get discarded, which have not been attempted in previous studies.

Table of Contents

1 Introduction 1

1.1 Problem Description . . . . . . . . . . . . . . . . . . . . . . . 2

1.2 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

1.3 Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

1.4 Related Works . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

2 Background 8

2.1 Word embedding extraction . . . . . . . . . . . . . . . . . . . 8

2.2 Embedding evaluations . . . . . . . . . . . . . . . . . . . . . . 9

2.2.1 Intrinsic evaluations . . . . . . . . . . . . . . . . . . . 9

2.2.2 Extrinsic evaluations . . . . . . . . . . . . . . . . . . . 11

2.2.3 Intrinsic and extrinsic evaluations . . . . . . . . . . . . 11

2.3 Sense Vector extraction . . . . . . . . . . . . . . . . . . . . . . 12

2.4 Neural Networks . . . . . . . . . . . . . . . . . . . . . . . . . 13

2.4.1 Convolutional Neural Network . . . . . . . . . . . . . . 13

2.4.2 Long Short-Term Memory Networks . . . . . . . . . . 14

2.4.3 Transformer . . . . . . . . . . . . . . . . . . . . . . . . 14

3 Qualitative Evaluation of Contextual Word Embeddings 15

3.1 Embedding Model Structures . . . . . . . . . . . . . . . . . . 15

3.2 Evaluations on Conversational Dataset . . . . . . . . . . . . . 16

3.2.1 Dataset . . . . . . . . . . . . . . . . . . . . . . . . . . 16

3.2.2 Embedding Models Used . . . . . . . . . . . . . . . . . 18

3.2.3 Approach . . . . . . . . . . . . . . . . . . . . . . . . . 19

3.2.4 Result . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

4 Representation of Sense Dimensions in Word Embeddings 23

4.1 Models used . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

4.2 Datasets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

4.3 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

4.4 Algorithm based on PCA . . . . . . . . . . . . . . . . . . . . . 26

4.4.1 Our Algorithm . . . . . . . . . . . . . . . . . . . . . . 29

5 Test and Evaluation 32

5.1 Test Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32

5.2 Evaluation of Algorithm . . . . . . . . . . . . . . . . . . . . . 37

6 Conclusion 47

6.1 Limitation and Future Work . . . . . . . . . . . . . . . . . . . 48

About this Honors Thesis

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School	Emory College
Department	Computer Science
Degree	B.S.
Submission	Honors Thesis
Language	English
Research Field	Computer Science
Palabra Clave	NLP Word Embedding
Committee Chair / Thesis Advisor	Jinho Choi, Emory University
Committee Members	Justin Burton, Emory University Davide Fossati, Emory University

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