Common cutting-edge interactive services often use a one-size-fits-all model of language complexity to reach a base with varying levels of English proficiency. If we instead allowed chat-based programs to vary their behavior based on the proficiency of the user, it would better adapt to the needs of those most disadvantaged without hindering proficient speakers. This system could also be used in language education to improve with the user over time.
What it does
Challenges we ran into
We had to ensure that we could apply the readability score to any piece of input provided by the user and process enough user data to to provide a meaningful measure. Because readability can vary from sentence to sentence and from measure to measure, we had to give our project leeway in case some implementation wants to use a measurement besides Flesch-Kincaid. We also had to learn from scratch how to use intents and contexts in Google's DialogFlow to properly sequence conversations.
Accomplishments that we're proud of
We are able to have meaningful conversations with our bot that actually vary to accommodate the user depending on how proficient at English the user is.
What we learned
What's next for AdaptChat
We would like to train a natural language model, so that we can get more topics to chat about with the bot. We would like to add other readability scores, so that we can get more accurate information about the user in a variety of situations.