Parallel Construction for Automated Question Generation
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Solution Overview
Problem
Current automated question generation systems for educational materials face challenges when dealing with languages other than English due to under-resourced Natural Language Processing (NLP) tools, leading to unreliable content knowledge selection and question creation, especially for languages like Brazilian Portuguese.
Innovation Solution
The implementation of a parallel construction method using machine translation and parallel corpus approaches, which leverages the sophistication of English NLP tools by aligning and translating questions from English to other languages, ensuring high linguistic quality and accuracy while bypassing noisy machine translation issues.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If automated question generation is implemented using native NLP tools for non-English languages, then the system can operate directly in the target language, but the quality and reliability of question generation deteriorates due to under-resourced NLP tools
Solution Approach 1:
The patent uses English as an intermediary language with robust NLP tools. The system translates non-English source text to English, generates questions using mature English NLP tools, then translates questions back to the target language. This intermediary approach bypasses the limitation of under-resourced NLP tools in non-English languages while maintaining question generation quality.
Solution Approach 2:
The system copies the successful English question generation pipeline and adapts it for non-English languages through translation. By replicating the English NLP processing steps (tokenization, parsing, question generation) in the translated English text, the system leverages well-established methodologies without requiring development of new NLP tools for each language.
2Adaptability or versatility
If machine translation is used to enable English NLP tools to process non-English text, then language versatility improves, but translation noise and inaccuracies worsen the reliability of content knowledge selection
Solution Approach 1:
The system performs preliminary translation of the source text to English before question generation. By translating the source material first and establishing alignment between original and translated texts, the system prepares the data in advance for processing by English NLP tools, enabling subsequent question generation to proceed smoothly despite the initial translation step.
Solution Approach 2:
The system uses alignment information between source language and translated English text as feedback to guide question generation. The alignment data allows the system to map generated English questions back to the original non-English text, ensuring that questions accurately reflect the source content despite translation variations.
3Measurement precision
If direct question generation in non-English languages is attempted, then linguistic authenticity is maintained, but the sophistication and accuracy of NLP analysis deteriorates due to lack of resources
Solution Approach 1:
The system creates a universal question generation framework that works across multiple languages by translating all inputs to English for processing. This multi-functional approach allows the same English NLP pipeline to serve multiple source languages, achieving language coverage without requiring separate specialized systems for each language.
Solution Approach 2:
English serves as a universal intermediary that bridges multiple source languages. By translating diverse language inputs into English and processing them through a single robust NLP pipeline, the system achieves both high measurement precision (through mature English NLP tools) and broad language coverage (by accepting multiple source languages).
Data Source
AI summary
Apparatuses, systems, and methods are provided for parallel construction for question generation (QG) corresponding to a content item. Text of at least a portion of the content item may be extracted as a source language text, at least a portion of which may be translated to generate a parallel text having a primary language different from the source language. The source and primary language texts may be aligned to create an aligned source language text and an aligned primary language text. A QG strategy may be determined and at least one step to be performed on the aligned primary language text may be generated based at least in part upon the determined QG strategy. At least one parallel operation corresponding to the at least one step to be performed on the aligned primary language text may be constructed, and a source language question may be generated.