Educational Testing Service

Robert A Pugh

Yao Qian

Keelan Evanini

Michael Flor

Vikram Ramanarayanan

David Suendermann-Oeft

Brian W Riordan

Patrick Lange

Rutuja Ubale

Kenneth Steimel

Systems and methods are disclosed for automatically scoring a constructed response using a neural network. In embodiments, a constructed response received by a processing system may be processed to divide the constructed response into multiple series of word tokens, wherein each word token includes a sequence of characters. The constructed response may be further processed to correct one or more spelling errors. The word tokens may be encoded to generate representation vectors for the constructed response. A set of nonlinear operations may be applied to the plurality of representation vectors in a neural network to generate a single vector output. A set of predetermined network weights may be applied to the vector output of the neural network to generate a scalar output for scoring the constructed response.

Systems and methods for neural content scoring

Systems and methods for identifying a person's native language and/or non-native language based on code-switched text and/or speech, are presented. The systems may be trained using various methods. For example, a language identification system may be trained using one or more code-switched corpora. Text and/or speech features may be extracted from the corpora and used, in combination with a per-word language identify of the text and/or speech, to train at least one machine learner. Code-switched text and/or speech may be received and processed by extracting text and/or speech features. These features may be fed into the at least one machine learner to identify the person's native language.

Automatic turn-level language identification for code-switched dialog

Systems and methods for identifying a person's native language, are presented. A native language identification system, comprising a plurality of artificial neural networks, such as time delay deep neural networks, is provided. Respective artificial neural networks of the plurality of artificial neural networks are trained as universal background models, using separate native language and non-native language corpora. The artificial neural networks may be used to perform voice activity detection and to extract sufficient statistics from the respective language corpora. The artificial neural networks may use the sufficient statistics to estimate respective T-matrices, which may in turn be used to extract respective i-vectors. The artificial neural networks may use i-vectors to generate a multilayer perceptron model, which may be used to identify a person's native language, based on an utterance by the person in his or her non-native language.

Native language identification with time delay deep neural networks trained separately on native and non-native english corpora

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San Jose, CA, United States of America

Robert A Pugh