Educational Testing Service

Debanjan Ghosh

Vikram Ramanarayanan

Beata Beigman Klebanov

Swapna Somasundaran

Avijit Vajpayee

Matthew Mulholland

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

Data is received that includes a passage of text generated in response to a prompt which comprises a plurality of sentences. Thereafter, the passage of text is tokenized into a plurality of tokens each corresponding to a different word in the passage of text. A first classification head of an adaptive fine-tuned transforms classifies each of the tokens into one of a plurality of classes. A second classification head of the adaptive fine-tuned transformer model classifies each of the sentences as either including or not including an argument. Data can then be provided which characterizes the first and second classifications. Related apparatus, systems, techniques and articles are also described.

Machine learning-based argument component segmentation

Systems and methods are provided for automatic evaluation of argument critique essays written by young students in response to prompts. A transformer pre-trained for natural language processing is employed as a machine learning model, which is fine-tune with a first training dataset comprising unannotated argument critique essays written by college students, and then fine-tuned with a second training dataset comprising annotated argument critique essays written by middle school students, where each sentence in the second training dataset is annotated for the presence of valid critiques to prompts. The fine-tuned machine learning model is used to classify each sentence in an essay to be evaluated as either containing a valid critique or not.

Automatic evaluation of argumentative writing by young students

Human-machine dialog is characterized by receiving data comprising a recording of an individual interacting with a dialog application simulating a conversation. Thereafter, the received data is parsed using automated speech recognition to result in text comprising a plurality of words. Features are extracted from the parsed data and then input an ensemble of different machine learning models each trained to generate a score characterizing a plurality of different dialog constructs. Thereafter, scores generated by the machine learning models for each of the dialog constructs are fused. A performance score is then generated based on the fused scores which characterizes a conversational proficiency of the individual interacting with the dialog application. Data can then be provided which includes or otherwise characterizes the generated score. Related apparatus, systems, techniques and articles are also described.

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Cambridge, MA, United States of America

Debanjan Ghosh