4 research outputs found
Generating Well-Formed Answers by Machine Reading with Stochastic Selector Networks
Question answering (QA) based on machine reading comprehension has been a recent surge in popularity, yet most work has focused on extractive methods. We instead address a more challenging QA problem of generating a well-formed answer by reading and summarizing the paragraph for a given question.For the generative QA task, we introduce a new neural architecture, LatentQA, in which a novel stochastic selector network composes a well-formed answer with words selected from the question, the paragraph and the global vocabulary, based on a sequence of discrete latent variables. Bayesian inference for the latent variables is performed to train the LatentQA model. The experiments on public datasets of natural answer generation confirm the effectiveness of LatentQA in generating high-quality well-formed answers
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μ μ μνλ€.Despite the recent advances of conditional text generation systems leveraged from pre-trained language models, factual consistency of the systems are still not sufficient. However, widely used n-gram similarity metrics are vulnerable to evaluate the factual consistency. Hence, in order to develop a factual consistent system, an automatic factuality metric is first necessary. In this dissertation, we propose four metrics that show very higher correlation with human judgments than previous metrics in evaluating factual consistency, for diverse conditional text generation systems. To build such metrics, we utilize (1) auxiliary tasks and (2) data augmentation methods.
First, we focus on the keywords or keyphrases that are critical for evaluating factual consistency and propose two factual consistency metrics using two different auxiliary tasks. We first integrate the keyphrase weights prediction task to the previous metrics to propose a KPQA (Keyphrase Prediction for Question Answering)-metric for generative QA. Also, we apply question generation and answering to develop a captioning metric QACE (Question Answering for Captioning Evaluation). QACE generates questions on the keywords of the candidate. QACE checks the factual consistency by comparing the answers of these questions for the source image and the caption.
Secondly, different from using auxiliary tasks, we directly train a metric with a data-driven approach to propose two metrics. Specifically, we train a metric to distinguish augmented inconsistent texts with the consistent text. We first modify the original reference captions to generate inconsistent captions using several rule-based methods such as substituting keywords to propose UMIC (Unreferenced Metric for Image Captioning). As a next step, we introduce a MFMA (Mask-and-Fill with Masked-Article)-metric by generating inconsistent summary using the masked source and the masked summary. Finally, as an extension of developing data-driven factual consistency metrics, we also propose a faster post-editing system that can fix the factual errors in the system.1 Introduction 1
2 Background 10
2.1 Text Evaluation Metrics 10
2.1.1 N-gram Similarity Metrics 10
2.1.2 Embedding Similarity Metrics 12
2.1.3 Auxiliary Task Based Metrics 12
2.1.4 Entailment Based Metrics 13
2.2 Evaluating Automated Metrics 14
3 Integrating Keyphrase Weights for Factual Consistency Evaluation 15
3.1 Related Work 17
3.2 Proposed Approach: KPQA-Metric 18
3.2.1 KPQA 18
3.2.2 KPQA Metric 19
3.3 Experimental Setup and Dataset 23
3.3.1 Dataset 23
3.3.2 Implementation Details 26
3.4 Empirical Results 27
3.4.1 Comparison with Other Methods 27
3.4.2 Analysis 29
3.5 Conclusion 35
4 Question Generation and Question Answering for Factual Consistency Evaluation 36
4.1 Related Work 37
4.2 Proposed Approach: QACE 38
4.2.1 Question Generation 38
4.2.2 Question Answering 39
4.2.3 Abstractive Visual Question Answering 40
4.2.4 QACE Metric 42
4.3 Experimental Setup and Dataset 43
4.3.1 Dataset 43
4.3.2 Implementation Details 44
4.4 Empirical Results 45
4.4.1 Comparison with Other Methods 45
4.4.2 Analysis 46
4.5 Conclusion 48
5 Rule-Based Inconsistent Data Augmentation for Factual Consistency Evaluation 49
5.1 Related Work 51
5.2 Proposed Approach: UMIC 52
5.2.1 Modeling 52
5.2.2 Negative Samples 53
5.2.3 Contrastive Learning 55
5.3 Experimental Setup and Dataset 56
5.3.1 Dataset 56
5.3.2 Implementation Details 60
5.4 Empirical Results 61
5.4.1 Comparison with Other Methods 61
5.4.2 Analysis 62
5.5 Conclusion 65
6 Inconsistent Data Augmentation with Masked Generation for Factual Consistency Evaluation 66
6.1 Related Work 68
6.2 Proposed Approach: MFMA and MSM 70
6.2.1 Mask-and-Fill with Masked Article 71
6.2.2 Masked Summarization 72
6.2.3 Training Factual Consistency Checking Model 72
6.3 Experimental Setup and Dataset 73
6.3.1 Dataset 73
6.3.2 Implementation Details 74
6.4 Empirical Results 75
6.4.1 Comparison with Other Methods 75
6.4.2 Analysis 78
6.5 Conclusion 84
7 Factual Error Correction for Improving Factual Consistency 85
7.1 Related Work 87
7.2 Proposed Approach: RFEC 88
7.2.1 Problem Formulation 88
7.2.2 Training Dataset Construction 89
7.2.3 Evidence Sentence Retrieval 90
7.2.4 Entity Retrieval Based Factual Error Correction 90
7.3 Experimental Setup and Dataset 92
7.3.1 Dataset 92
7.3.2 Implementation Details 93
7.4 Empirical Results 93
7.4.1 Comparison with Other Methods 93
7.4.2 Analysis 95
7.5 Conclusion 95
8 Conclusion 97
Abstract (In Korean) 118λ°