Automatic Question Generation ::
language learning context
Chinese retailers have cut staff.
No, this is not an introduction to a post about economics or politics.
This is an example sentence from an English textbook (and originally, from a news article).
Using this sentence alone, an English teacher could come up with a dozen of ways to facilitate or test their students' knowledge of English.
When it comes to teaching, practicing and revising grammar, this sentence can be used to produce the following questions:
- Is it important when exactly Chinese retailers cut staff or the fact that cutting staff took place at all?
- Chinese retailers _________ staff. (cut)
- What is the grammatical tense of the verb 'cut' in the sentence?
Each of the questions above serves a specific goal:
- checking understanding of certain grammatical categories in the text
- drawing attention to certain grammatical constructions in the text
- testing explicit knowledge of grammar
A good teacher sure wants to make the best out of every text their students read.
As well as check their homework.
And prepare fun communicative activities for the next class.
And come up with the topics for the next classroom debate... and much more.
Would it not be nice to delegate at least some of this work to a knowledgeable, reliable assistant?
Fortunately, Natural Language Processing techniques and tools make it possible!
In our paper titled Question Generation for Language Learning,
Detmar Meurers and I discuss the different functions that questions play in language teaching and learning
and exemplify how automatic question generation can support those uses.
By the way, a crowdsourcing study we conducted (also reported in the paper) showed that it is not that easy for proficient English speakers to tell a question written by an English teacher from a computer-generated one: 67% of automatically generated questions were thought to have been written by a teacher.
Of course, there is always room for improvement. We are currently working on the multiple-choice answer format, which requires generation of distractors (multiple-choice options),
and exploring the NLP task of Tense Sense Disambiguation in order to improve our algorithm for generating grammar concept questions about grammatical tenses.
I would like to thank my colleagues:
Simón Ruiz for putting together and providing a dataset of manually written questions;
and Michael Grosz and Johann Jacoby for taking their time to guide me through the dangerous but exciting world of statistical analysis!
FLAIR stands for Form-focused Language-Aware Information Retrieval. It is an online web application for language teachers and learners that helps them find authentic web texts containing a sufficient number of different linguistic constructions studied as part of the English language curriculum, such as the passive voice, grammar tenses, wh- questions, etc.
Try out the FLAIR tool and see for yourself (no registration needed). There you will also find the list of the implemented linguistic constructions as well as the utilized third-party tools and libraries.
By the way, we are currently preparing an online experiment to find out whether English teachers prefer FLAIR over the Microsoft Bing search engine when choosing the reading material for their students.
I would like to thank Professor Detmar Meurers for his supervision and promoting FLAIR in all corners of the world;
Madeesh Kannan for optimizing FLAIR;
and Ankita Oswal for her work on the online experiment!
Slash/A is a new visualization by me (Maria Chinkina) and Velislava Todorova that allows you to search for and visualize sequences of words, lemmas, parts of speech, or any combination of the three.
Download Slash/A (10,6MB) including the Barrett-Browning corpus
Watch the demos: Part 1 and Part 2
I would like to thank Velislava Todorova for her endless enthusiasm and Dr. Chris Culy for his support and for hosting Slashenka!