Alan Cowap

Both humans and machines have difficulty detecting fake news, bias, and identifying emotions. These problems take on a new dimension with the advent of ever more sophisticated machine generated text, such as GPT-2 and Grover which were both released this year. We now face the additional difficulty of detecting machine generated synthetic text. Such is the sophistication of machine generated text that there is ongoing work on release strategies for text generators in order to avoid misuse. GPT-2 is undergoing a staged release approach and was fully released publicly only last week, whereas Grover’s authors plan to release it, because they found “the best defense against Grover turns out to be Grover itself”. There is ongoing work to detect synthetic text, and bias. These approaches can be categorised into Human detection, Automated ML-based detection, and Human-machine teaming. Metadata-based (e.g. time taken to write text, social graph of participants, etc.) prevention provides another tool for detecting synthetic text. My initial scan of the work to date shows no inclusion of emotion classification in these approaches. In one example using controlled generation Grover was prompted with a headline “Timing of May’s ‘festival of Britain’ risks Irish anger”, note the emotion “anger” in the prompt, and tasked to write an article. The human authored article includes emotional words such as “fear”, “attacks”, “hostility”, “mocked”; whereas Grover’s generated article is relatively light on emotion. Other useful applications for emotion classification include assisting people who have difficulty detecting emotions e.g. Asperger’s syndrome; and to allow people to filter content based on emotions. Can emotion help detect synthetic text?

There are several schemes for classifying emotions. IBM Watson Tone Analyzer detects seven tones in written text i.e. “anger, fear, joy, sadness, confident, analytical, and tentative”. Other approaches use six emotions i.e. “Happiness, Sadness, Surprise, Disgust, Anger, Fear” per Ekman’s model. Several other models exist for classifying emotions.

This proposal is to investigate the role emotion classification can play in synthetic text detection. A possible roadmap is to establish the state of the art, partner with OpenAI (who are reaching out for partners). Choose the most appropriate emotion set(s), and benchmark(s), and then to compare human vs synthetic text using e.g. a classifier trained on human text, and synthetic text. The result of this proposal should prove useful and could stand alone, or form part of the wider analysis of neural network interpretability and explainable AI