Human-AI Collaboration for Natural Language Generation With Interpretable Neural Networks
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CitationGehrmann, Sebastian. 2020. Human-AI Collaboration for Natural Language Generation With Interpretable Neural Networks. Doctoral dissertation, Harvard University, Graduate School of Arts & Sciences.
AbstractUsing computers to generate natural language from information (NLG) requires approaches that plan the content and structure of the text and actualize it in fluent and error-free language. The typical approaches to NLG are data-driven, which means that they aim to solve the problem by learning from annotated data.
Deep learning, a class of machine learning models based on neural networks, has become the standard data-driven NLG approach.
While deep learning approaches lead to increased performance, they replicate undesired biases from the training data and make inexplicable mistakes.
As a result, the outputs of deep learning NLG models cannot be trusted. We thus need to develop ways in which humans can provide oversight over model outputs and retain their agency over an otherwise automated writing task.
This dissertation argues that to retain agency over deep learning NLG models, we need to design them as team members instead of autonomous agents.
We can achieve these team member models by considering the interaction design as an integral part of the machine learning model development.
We identify two necessary conditions of team member-models -- interpretability and controllability. The models need to follow a reasoning process such that human team members can understand and comprehend it. Then, if humans do not agree with the model, they should be able to change the reasoning process, and the model should adjust its output accordingly.
In the first part of the dissertation, we present three case studies that demonstrate how interactive interfaces can positively affect how humans understand model predictions. In the second part, we introduce a neural network-based approach to document summarization that directly models the selection of relevant content. We show that, through this selection, a human user can control what part of a document the algorithm summarizes.
In the final part of this dissertation, we show that this design approach, coupled with an interface that exposes these interactions, can lead to a forth and back between human and autonomous agents where the two actors collaboratively generate text.
This dissertation thus demonstrates how to develop models with these properties and how to design neural networks as team members instead of autonomous agents.
Citable link to this pagehttps://nrs.harvard.edu/URN-3:HUL.INSTREPOS:37365160
- FAS Theses and Dissertations