Amazon Technologies, Inc.

Educational Testing Service

Avijit Vajpayee

Vimal Bhat

Abhinav Jain

Hooman Mahyar

Arjun Cholkar

Swapna Somasundaran

Debanjan Ghosh

Zhikang Zhang

Louis Kirk Barker

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, methods, and computer-readable media are disclosed for systems and methods for automated generation and presentation of sign language avatars for video content. Example methods may include determining, by one or more computer processors coupled to memory, a first segment of video content, the first segment including a first set of frames, first audio content, and first subtitle data, where the first subtitle data comprises a first word and a second word. Methods may include determining, using a first machine learning model, a first sign gesture associated with the first word, determining first motion data associated with the first sign gesture, and determining first facial expression data. Methods may include generating an avatar configured to perform the first sign gesture using the first motion data, where a facial expression of the avatar while performing the first sign gesture is based on the first facial expression data.

Automated generation and presentation of sign language avatars for video content

Techniques are described for detecting desynchronization between an audio component and a video component of a media presentation. Feature sets may be determined for portions of the audio component and portions of the video component, which may then be used to generate correlations between portions of the audio component and portions of the video component. Synchronization may then be assessed based on the correlations.

Event based audio-video sync detection

Systems and methods are provided for detecting and correcting synchronization errors in multimedia content comprising a video stream and a non-original audio stream. Techniques for directly detecting synchronization of video and audio streams may be inadequate to detect synchronize errors for non-original audio streams, particularly where such non-original audio streams contain audio not reflective of events within the video stream, such as speaking dialog in a different language than the speakers of the video stream. To overcome this problem, the present disclosure enables synchronization of a non-original audio stream to another audio stream, such as an original audio stream, that is synchronized to the video stream. By comparison of signatures, the non-original and other audio stream are aligned to determine an offset that can be used to synchronize the non-original audio stream to the video stream.

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Seattle, WA, United States of America

Avijit Vajpayee