Educational Testing Service

Xinhao Wang

Klaus Zechner

Keelan Evanini

Yao Qian

Lei Chen

Martin S Chodorow

Vikram Ramanarayanan

Swapna Somasundaran

David Suendermann-Oeft

Su-Youn Yoon

Chong Min Lee

Shasha Xie

Jidong Tao

Zhou Yu

Matthew Mulholland

Christopher Hamill

James Bruno

Data is received by an automated spoken language learning and assessment system that includes a passage of text comprising a response to stimulus material. Thereafter, at least one machine learning model is used to detect absent key points within the passage of text and/or location spans of key points in the passage of text. The at least one machine learning model can be trained using a corpus with annotated key points and a span for each key point. In addition, each of the detected key points is scored by at least one key point quality model to result in a corresponding key point score. Diagnostic feedback targeting content development skills is then determined based on the detecting and using the key point scores. Data can then be provided which characterizes such diagnostic feedback. Related apparatus, systems, techniques and articles are also described.

Targeted content feedback in spoken language learning and assessment

Data is received that encapsulates a spoken response to a prompt text comprising a string of words. Thereafter, the received data is transcribed into a string of words. The string of words is then compared with a prompt so that a similarity grid representation of the comparison can be generated that characterizes a level of similarity between the string of words in the spoken response and the string of words in the prompt text. The grid representation is then scored using at least one machine learning model. The score indicates a likelihood of the spoken response having been off-topic. Data providing the encapsulated score can then be provided. Related apparatus, systems, techniques and articles are also described.

Detection of off-topic spoken responses using machine learning

Data is received that encapsulates a spoken response to a test question. Thereafter, the received data is transcribed into a string of words. The string of words is then compared with at least one source string so that a similarity grid representation of the comparison can be generated that characterizes a level of similarity between the string of words and the at least one source string. The grid representation is then scored using at least one machine learning model. The score indicates a likelihood of the spoken response having been plagiarized. Data providing the encapsulated score can then be provided. Related apparatus, systems, techniques and articles are also described.

Detection of plagiarized spoken responses using machine learning

Systems and methods are provided for automatic detection of plagiarized spoken responses during standardized testing of language proficiency. A first spoken response to a speaking task that elicits spontaneous speech and a second spoken response to a source-based speaking task that is assumed to be non-plagiarized are digitally recorded. A first and second sets of speaking proficiency features are calculated for the first and the second spoken response, respectively. The first spoken response is classifying as plagiarized or non-plagiarized based on the comparison between the first and the second set of speaking proficiency features. Corresponding apparatuses, systems, and methods are also disclosed.

Systems and methods for automatic detection of plagiarized spoken responses

Computer-based systems and methods are provided for generating a narrative computer scoring model for assessing story narratives. In one embodiment, supervised machine learning is used to generate the narrative computer scoring model. For example, a collection of training story narratives with assigned scores may be used to train the model. In one embodiment, each training story narrative is processed to extract features that signify content relevance, collocation of commonly used words, coherency, detailing, and expressions of sentiment. These features, as well as others, may be selectively used to train a narrative computer scoring model. Once trained, the model can be used to automatically evaluate story narratives and assign appropriate scores.

Systems and methods for scoring story narrations

Systems and methods are provided for a processor-implemented method of analyzing quality of sound acquired via a microphone. An input metric is extracted from a sound recording at each of a plurality of time intervals. The input metric is provided at each of the time intervals to a neural network that includes a memory component, where the neural network provides an output metric at each of the time intervals, where the output metric at a particular time interval is based on the input metric at a plurality of time intervals other than the particular time interval using the memory component of the neural network. The output metric is aggregated from each of the time intervals to generate a score indicative of the quality of the sound acquired via the microphone.

Processor-implemented systems and methods for determining sound quality

Computer-implemented systems and methods are provided for automatically scoring the content of moderately predictable responses. For example, a computer performing the content scoring analysis can receive a response (either in text or spoken form) to a prompt. The computer can determine the content correctness of the response by analyzing one or more content features. One of the content features is analyzed by applying one or more regular expressions, determined based on training responses associated with the prompt. Another content feature is analyzed by applying one or more context free grammars, determined based on training responses associated with the prompt. Another content feature is analyzed by applying a keyword list, determined based on the test prompt eliciting the response and/or stimulus material. Another content feature is analyzed by applying one or more probabilistic n-gram models, determined based on training responses associated with the prompt. Another content feature is analyzed by comparing a POS response vector, determined based on the response, to one or more POS training vectors, determined based on training responses associated with the prompt. Another content feature is analyzed by comparing a response n-gram count to one or more training n-gram counts using an n-gram matching evaluation metric (e.g., BLEU). Another content feature is analyzed by comparing the response to one to training responses associated with the prompt using a dissimilarity metric (e.g., edit distance and word error rate).

Systems and methods for content scoring of spoken responses

Systems and methods are provided for automated detection of plagiarized spoken responses. A spoken response is processed to generate a text that is representative of the spoken response. The text is processed to remove disfluencies in the text and to identify a plurality of sentences in the text. A first numerical measure indicative of a number of words and phrases of the text that are included verbatim in a source text is determined. The source text has been designated as a source of plagiarized content. A second numerical measure indicative of an amount of the text that paraphrases portions of the source text is determined. A third numerical measure indicative of a similarity between sentences of the text and sentences of the source text is determined. A model is applied to the first, second, and third numerical measures to classify the spoken response as being plagiarized or non-plagiarized.

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San Carlos, CA, United States of America

Xinhao Wang