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Questions and Answers
What is the purpose of extracting large-scale question-answer (QA) pairs?
What is the purpose of extracting large-scale question-answer (QA) pairs?
Advancing research areas like machine reading comprehension and question answering
What approach is proposed in the document for generating QA pairs?
What approach is proposed in the document for generating QA pairs?
The OneStop model is more efficient because it involves multiple models.
The OneStop model is more efficient because it involves multiple models.
False
The OneStop model can be easily built upon pre-trained models such as ____, ____, and ____.
The OneStop model can be easily built upon pre-trained models such as ____, ____, and ____.
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What is the basic unit of encoder and decoder in the OneStop model?
What is the basic unit of encoder and decoder in the OneStop model?
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Match the following keywords with their respective descriptions:
Match the following keywords with their respective descriptions:
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What does the self-attentive module in the OneStop model consist of?
What does the self-attentive module in the OneStop model consist of?
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In the computation process of the self-attentive module, what is used to determine the corresponding answer for a generated question?
In the computation process of the self-attentive module, what is used to determine the corresponding answer for a generated question?
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What is the key contribution of the OneStop model?
What is the key contribution of the OneStop model?
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What is OneStop known for being the first of its kind?
What is OneStop known for being the first of its kind?
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What distinguishes OneStop model from previous pipeline approaches in terms of efficiency?
What distinguishes OneStop model from previous pipeline approaches in terms of efficiency?
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In the OneStop model, human annotators take the whole QA pair into consideration during the QA pair generation process.
In the OneStop model, human annotators take the whole QA pair into consideration during the QA pair generation process.
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The OneStop model integrates the question generation and the answer extraction into a unified _________.
The OneStop model integrates the question generation and the answer extraction into a unified _________.
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Study Notes
OneStop QAMaker: Extract Question-Answer Pairs from Text
- OneStop QAMaker is a model that extracts question-answer (QA) pairs from documents in a one-stop approach, unlike traditional pipeline approaches.
- The traditional pipeline approach involves two separate steps: selecting a candidate answer span and then generating an answer-specific question.
Limitations of Traditional Pipeline Approach
- The pipeline approach ignores the connection between question generation and answer extraction, leading to incompatible QA pair generation.
- The question generation model may generate questions that are hard to find answers for, and the answer extraction model may extract answer spans that are not suitable for question generation.
- The pipeline approach is time-consuming and involves at least two models, leading to cumulative error.
OneStop Model
- The OneStop model takes documents as input and outputs questions and their corresponding answer spans.
- The model integrates question generation and answer extraction into a unified framework, enhancing the compatibility of generated questions and extracted answers.
- The model can be easily built upon existing pre-trained language models, making it efficient to train and deploy.
Advantages of OneStop Model
- The OneStop model tackles the objective of generating compatible QA pairs directly, unlike traditional pipeline approaches.
- The model is more efficient and requires less human effort, making it suitable for industrial scenarios.
- The model achieves state-of-the-art performance in generating QA pairs.
Related Work
- Question generation is a well-studied natural language processing task, with two main approaches: template-based and model-based.
- Template-based approaches rely on human efforts to design templates and are unscalable across datasets.
- Model-based approaches employ end-to-end neural networks to generate questions, but may require additional entity extraction models or sequence labeling models.
Existing Works on QA Pair Generation
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Most existing works on QA pair generation follow a pipeline approach: selecting points in the document to be asked, generating questions based on the selected points, and detecting answer spans.
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There have been studies on joint models for question generation and question answering, but these models are limited by their dual constraint.### Topic Subtitle: QA Pair Generation and OneStop Model
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The goal of QA pair generation is to find related QA pairs given a document.
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The objective is mathematically formulated as:
arg max 𝑃 (𝑞, 𝑎|𝑑) = arg max 𝑃 (𝑞|𝑑; 𝜃) · 𝑃 (𝑎|𝑑, 𝑞; 𝜃) -
Existing methods can be classified into two groups: pipeline approaches (D2A2Q and D2Q2A) and the OneStop model.
Differences Between Pipeline Approaches and OneStop Model
- Pipeline approaches focus on the duality of question generation and answer extraction, whereas the OneStop model optimizes these tasks simultaneously.
- The OneStop model is more precise and efficient, involving only one model.
OneStop Model Architecture
- The OneStop model consists of a self-attentive module, an encoder, and a decoder.
- The self-attentive module is the basic unit of the encoder and decoder, consisting of a self-attention layer and a position-wise fully connected feed-forward layer.
- Each layer is employed with residual connection, followed by layer normalization.
Question Generation Module
- The input of the encoder is the document, and the output of the decoder is expected to be the question.
- The cross-entropy loss for question generation is denoted as:
Φlm = − ∑︁|𝑞| log 𝑃 (𝑞𝑡 |𝑞)
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Description
This quiz is about a one-stop approach to extract question-answer pairs from text, specifically designed for a system called OneStop QAMaker. It involves understanding the mechanism and its application.
