Abstract: A semiconductor device and a manufacturing method thereof are disclosed in the present invention. The semiconductor device includes a source structure; a gate structure disposed above the source structure; a first opening penetrates through the gate structure in a vertical direction; a semiconductor structure; a gate dielectric layer; an insulation structure; and a void. The semiconductor structure is partially disposed in the first opening, and at least a portion of the gate structure is located at two opposite sides of the semiconductor structure in a horizontal direction. The gate dielectric layer is disposed in the first opening and located between the semiconductor structure and the gate structure. At least a portion of the insulation structure is disposed in the first opening, and the void is located in the insulation structure.

Abstract: A semiconductor device and a manufacturing method thereof are disclosed in the present invention. The semiconductor device includes a source structure; a gate structure disposed above the source structure; a first opening penetrates through the gate structure in a vertical direction; a semiconductor structure; a gate dielectric layer; an insulation structure; and a void. The semiconductor structure is partially disposed in the first opening, and at least a portion of the gate structure is located at two opposite sides of the semiconductor structure in a horizontal direction. The gate dielectric layer is disposed in the first opening and located between the semiconductor structure and the gate structure. At least a portion of the insulation structure is disposed in the first opening, at least a portion of the semiconductor structure is located between the insulation structure and the gate dielectric layer, and the void is located in the insulation structure.

Abstract: A computer implemented method includes receiving speech data representative of speech in a first language The speech data is divided into chunks of speech data, each chunk comprising multiple temporally consecutive frames of acoustic information. Each temporally consecutive chunk of data is processed using beam search on each frame to identify candidate language tokens representing a second language different from the first language. A best candidate language token(s) is selected for each chunk as processed. The selected best candidate language token or tokens for each chunk of data is committed as a prefix for a next temporally consecutive chunk of data.

Abstract: A computer-implemented method, computer program product and computing system for: receiving speech in a source language to define source language speech; performing a first token-based translation of the source language speech into text of an intermediate language to define intermediate language text; and performing a second token-based translation from the intermediate language text into text of a target language to define target language text.

Abstract: A computer-implemented method, computer program product and computing system for: receiving speech in a source language to define source language speech; performing a first token-based transcription of the source language speech into text of the source language using a first look-ahead encoder to define source language text; and performing a first token-based translation of the source language speech into text of a target language using a second look-ahead encoder to define target language text, wherein the first look-ahead encoder is smaller than the second look-ahead encoder.

Abstract: A computer implemented method includes receiving speech data representative of speech in a first language The speech data is divided into chunks of speech data, each chunk comprising multiple temporally consecutive frames of acoustic information. Each temporally consecutive chunk of data is processed using beam search on each frame to identify candidate language tokens representing a second language different from the first language. A best candidate language token(s) is selected for each chunk as processed. The selected best candidate language token or tokens for each chunk of data is committed as a prefix for a next temporally consecutive chunk of data.

Abstract: Representation learning for text and speech has improved many language-related tasks. However, existing methods only learn from one input modality, while a unified representation for both speech and text is needed for tasks such as end-to-end speech translation. Consequently, these methods cannot exploit various large-scale text and speech data and their performance is limited by the scarcity of parallel speech translation data. To address these problems, embodiments of a fused acoustic and text masked language model (FAT-MLM) are disclosed. FAT-MLM embodiments jointly learn a unified representation for both acoustic and text input from various types of corpora including parallel data for speech recognition and machine translation, and pure speech and text data. Within this cross-modal representation learning framework, an end-to-end model is further presented for fused acoustic and text speech translation.

Abstract: A semiconductor device and a manufacturing method thereof are disclosed in the present invention. The semiconductor device includes a source structure; a gate structure disposed above the source structure; a first opening penetrates through the gate structure in a vertical direction; a semiconductor structure; a gate dielectric layer; an insulation structure; and a void. The semiconductor structure is partially disposed in the first opening, and at least a portion of the gate structure is located at two opposite sides of the semiconductor structure in a horizontal direction. The gate dielectric layer is disposed in the first opening and located between the semiconductor structure and the gate structure. At least a portion of the insulation structure is disposed in the first opening, at least a portion of the semiconductor structure is located between the insulation structure and the gate dielectric layer, and the void is located in the insulation structure.

Abstract: Representation learning for text and speech has improved many language-related tasks. However, existing methods only learn from one input modality, while a unified representation for both speech and text is needed for tasks such as end-to-end speech translation. Consequently, these methods cannot exploit various large-scale text and speech data and their performance is limited by the scarcity of parallel speech translation data. To address these problems, embodiments of a fused acoustic and text masked language model (FAT-MLM) are disclosed. FAT-MLM embodiments jointly learn a unified representation for both acoustic and text input from various types of corpora including parallel data for speech recognition and machine translation, and pure speech and text data. Within this cross-modal representation learning framework, an end-to-end model is further presented for fused acoustic and text speech translation.

Abstract: Embodiments of the present disclosure relate to a method and apparatus for generating a dialogue model. The method may include: acquiring a corpus sample set, a corpus sample including input information and target response information; classifying corpus samples in the corpus sample set, setting discrete hidden variables for the corpus samples based on a classification result to generate a training sample set, a training sample including the input information, the target response information, and a discrete hidden variable; and training a preset neural network using the training sample set to obtain the dialogue model, the dialogue model being used to represent a corresponding relationship between inputted input information and outputted target response information.

Abstract: Embodiments of the present disclosure relate to a method and apparatus for generating a dialogue model. The method may include: acquiring a corpus sample set, a corpus sample including input information and target response information; classifying corpus samples in the corpus sample set, setting discrete hidden variables for the corpus samples based on a classification result to generate a training sample set, a training sample including the input information, the target response information, and a discrete hidden variable; and training a preset neural network using the training sample set to obtain the dialogue model, the dialogue model being used to represent a corresponding relationship between inputted input information and outputted target response information.