Educational Testing Service

Hillary Molloy

Swapna Somasundaran

Martin S Chodorow

Keelan Evanini

Michael Flor

Vikram Ramanarayanan

David Suendermann-Oeft

Yao Qian

Patrick Lange

Avijit Vajpayee

Debanjan Ghosh

Alexei V Ivanov

Eugene Tsuprun

Binod Gyawali

Laura McCulla

Computer-based argument mining can be implemented by accessing data comprising text encapsulating a plurality of debates relating to varying topics. Thereafter, the text is parsed into snippets of text containing arguments. Features are then extracted or otherwise derived from pairs of snippets. The features are then used by a transformer network to classify each of the snippets according to one of three categories, the pairs of snippets either being on a same side of a debate or on a different side of the debate. Data is then provided which characterizes the classification. Related apparatus, systems, techniques and articles are also described.

Machine learning-based argument mining and classification

Systems and methods are provided for processing a response to essay prompts that request a narrative response. A data structure associated with a narrative essay is accessed. The essay is analyzed to generate an organization subscore, where the organization subscore is generated using a graph metric by identifying content words in each sentence of the essay and populating a data structure with links between related content words in neighboring sentences, wherein the organization subscore is determined based on the links. The essay is analyzed to generate a development subscore, where the development subscore is generated using a transition metric by accessing a transition cue data store and identifying transition words in the essay, wherein the development subscore is based on a number of words in the essay that match words in the transition cue data store. A narrative quality metric is determined based on the organization subscore and the development subscore.

Platform for administering and evaluating narrative essay examinations

Systems and methods are provided providing a spoken dialog system. Output is provided from a spoken dialog system that determines audio responses to a person based on recognized speech content from the person during a conversation between the person and the spoken dialog system. Video data associated with the person interacting with the spoken dialog system is received. A video engagement metric is derived from the video data, where the video engagement metric indicates a level of the person's engagement with the spoken dialog system.

Computer-implemented systems and methods for evaluating speech dialog system engagement via video

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Hillary Molloy