OneStop QAMaker: Extract Question-Answer Pairs

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

• 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.

• 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

• The goal of QA pair generation is to find related QA pairs given a document.

• 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)