Abstract: Methods and systems for hierarchical natural language understanding are described. A representation of an utterance is inputted to a first machine learning model to obtain information on the first utterance. According to the information on the utterance a determination that the representation of the utterance is to be inputted to a second machine learning model that performs a dedicated natural language task is performed. In response to determining that the representation of the utterance is to be inputted to a second machine learning model, the utterance is inputted to the second machine learning model to obtain an output of the dedicated natural language task.

Abstract: A method and system for extracting and labeling Named-Entity Recognition (NER) data in a target language for use in a multi-lingual software module has been developed. First, a textual sentence is translated to the target language using a translation module. A named entity is identified and extracted within the translated sentence. The named entity is identified by either: exact mapping; a semantically similar translated named entity that meets a predetermined minimum threshold of similarity; or utilizing a rule-based library for the target language. Once identified, the named entity is labeled with a pre-determined category and stored in a retrievable electronic database.

Abstract: Computing systems may support image classification and image detection services, and these services may utilize object detection/image classification machine learning models. The described techniques provide for normalization of confidence scores corresponding to manipulated target images and for non-max suppression within the range of confidence scores for manipulated images. In one example, the techniques provide for generating different scales of a test image, and the system performs normalization of confidence scores corresponding to each scaled image and non-max suppression per scaled image These techniques may be used to provide more accurate image detection (e.g., object detection and/or image classification) and may be used with models that are not trained on modified image sets. The model may be trained on a standard (e.g. non-manipulated) image set but used with manipulated target images and the described techniques to provide accurate object detection.

Abstract: A natural language processing system that trains task models for particular natural language tasks programmatically generates additional utterances for inclusion in the training set, based on the existing utterances in the training set and the existing state of a task model as generated from the original (non-augmented) training set. More specifically, the training augmentation module 220 identifies specific textual units of utterances and generates variants of the utterances based on those identified units. The identification is based on determined importances of the textual units to the output of the task model, as well as on task rules that correspond to the natural language task for which the task model is being generated. The generation of the additional utterances improves the quality of the task model without the expense of manual labeling of utterances for training set inclusion.

Abstract: In general, techniques are described for extending network connectivity software utilities, such as traceroute, to provide complete visibility into a network topology between a source device and a destination device, even when an intermediate network device may be actively utilizing multiple network links when forwarding packets toward the destination. In one example, a network device coupled to a plurality of paths and positioned between a source network device and destination network device may receive a traceroute packet. The network device may also, for each of the plurality of paths, modify a payload of the traceroute packet to include a respective identifier for a corresponding path of the plurality of paths to construct a respective modified traceroute packet for the corresponding path. The network device may also forward the respective modified traceroute packets on the corresponding paths.

Abstract: A method and system for extracting and labeling Named-Entity Recognition (NER) data in a target language for use in a multi-lingual software module has been developed. First, a textual sentence is translated to the target language using a translation module. A named entity is identified and extracted within the translated sentence. The named entity is identified by either: exact mapping; a semantically similar translated named entity that meets a predetermined minimum threshold of similarity; or utilizing a rule-based library for the target language. Once identified, the named entity is labeled with a pre-determined category and stored in a retrievable electronic database.

Abstract: Methods and systems for hierarchical natural language understanding are described. A representation of an utterance is inputted to a first machine learning model to obtain information on the first utterance. According to the information on the utterance a determination that the representation of the utterance is to be inputted to a second machine learning model that performs a dedicated natural language task is performed. In response to determining that the representation of the utterance is to be inputted to a second machine learning model, the utterance is inputted to the second machine learning model to obtain an output of the dedicated natural language task.

Abstract: A system performs named entity recognition for performing natural language processing, for example, for conversation engines. The system uses context information in named entity recognition. The system includes the context of a sentence during model training and execution. The system generates high quality contextual data for training NER models. The system utilizes labeled and unlabeled contextual data for training NER models. The system provides NER models for execution in production environments. The system uses heuristics to determine whether to use a context-based NER model or a simple NER model that does not use context information. This allows the system to use simple NER models when the likelihood of improving the accuracy of prediction based on context is low.

Abstract: A system performs named entity recognition for performing natural language processing, for example, for conversation engines. The system uses context information in named entity recognition. The system includes the context of a sentence during model training and execution. The system generates high quality contextual data for training NER models. The system utilizes labeled and unlabeled contextual data for training NER models. The system provides NER models for execution in production environments. The system uses heuristics to determine whether to use a context-based NER model or a simple NER model that does not use context information. This allows the system to use simple NER models when the likelihood of improving the accuracy of prediction based on context is low.

Abstract: Embodiments described herein provide unsupervised density-based clustering to infer table structure from document. Specifically, a number of words are identified from a block of text in an noneditable document, and the spatial coordinates of each word relative to the rectangular region are identified. Based on the word density of the rectangular region, the words are grouped into clusters using a heuristic radius search method. Words that are grouped into the same cluster are determined to be the element that belong to the same cell. In this way, the cells of the table structure can be identified. Once the cells are identified based on the word density of the block of text, the identified cells can be expanded horizontally or grouped vertically to identify rows or columns of the table structure.

Abstract: Computing systems may support image classification and image detection services, and these services may utilize object detection/image classification machine learning models. The described techniques provide for normalization of confidence scores corresponding to manipulated target images and for non-max suppression within the range of confidence scores for manipulated images. In one example, the techniques provide for generating different scales of a test image, and the system performs normalization of confidence scores corresponding to each scaled image and non-max suppression per scaled image These techniques may be used to provide more accurate image detection (e.g., object detection and/or image classification) and may be used with models that are not trained on modified image sets. The model may be trained on a standard (e.g. non-manipulated) image set but used with manipulated target images and the described techniques to provide accurate object detection.

Abstract: A natural language processing system that trains task models for particular natural language tasks programmatically generates additional utterances for inclusion in the training set, based on the existing utterances in the training set and the existing state of a task model as generated from the original (non-augmented) training set. More specifically, the training augmentation module 220 identifies specific textual units of utterances and generates variants of the utterances based on those identified units. The identification is based on determined importances of the textual units to the output of the task model, as well as on task rules that correspond to the natural language task for which the task model is being generated. The generation of the additional utterances improves the quality of the task model without the expense of manual labeling of utterances for training set inclusion.

Abstract: Computing systems may support image classification and image detection services, and these services may utilize object detection/image classification machine learning models. The described techniques provide for normalization of confidence scores corresponding to manipulated target images and for non-max suppression within the range of confidence scores for manipulated images. In one example, the techniques provide for generating different scales of a test image, and the system performs normalization of confidence scores corresponding to each scaled image and non-max suppression per scaled image These techniques may be used to provide more accurate image detection (e.g., object detection and/or image classification) and may be used with models that are not trained on modified image sets. The model may be trained on a standard (e.g. non-manipulated) image set but used with manipulated target images and the described techniques to provide accurate object detection.

Abstract: Computing systems may support image classification and image detection services, and these services may utilize object detection/image classification machine learning models. The described techniques provide for normalization of confidence scores corresponding to manipulated target images and for non-max suppression within the range of confidence scores for manipulated images. In one example, the techniques provide for generating different scales of a test image, and the system performs normalization of confidence scores corresponding to each scaled image and non-max suppression per scaled image These techniques may be used to provide more accurate image detection (e.g., object detection and/or image classification) and may be used with models that are not trained on modified image sets. The model may be trained on a standard (e.g. non-manipulated) image set but used with manipulated target images and the described techniques to provide accurate object detection.

Abstract: Embodiments described herein provide unsupervised density-based clustering to infer table structure from document. Specifically, a number of words are identified from a block of text in an noneditable document, and the spatial coordinates of each word relative to the rectangular region are identified. Based on the word density of the rectangular region, the words are grouped into clusters using a heuristic radius search method. Words that are grouped into the same cluster are determined to be the element that belong to the same cell. In this way, the cells of the table structure can be identified. Once the cells are identified based on the word density of the block of text, the identified cells can be expanded horizontally or grouped vertically to identify rows or columns of the table structure.

Abstract: In general, techniques are described for extending network connectivity software utilities, such as traceroute, to provide complete visibility into a network topology between a source device and a destination device, even when an intermediate network device may be actively utilizing multiple network links when forwarding packets toward the destination. In one example, a network device coupled to a plurality of paths and positioned between a source network device and destination network device may receive a traceroute packet. The network device may also, for each of the plurality of paths, modify a payload of the traceroute packet to include a respective identifier for a corresponding path of the plurality of paths to construct a respective modified traceroute packet for the corresponding path. The network device may also forward the respective modified traceroute packets on the corresponding paths.

Abstract: In some embodiments, an apparatus includes a layer-2 device operably coupled to a source device and a destination device and disposed within a data path (1) between the source device and the destination device, and (2) includes at least one layer-3 device. The layer-2 device receives a first test data unit from the source device, and defines a quality datum associated with processing the first test data unit. The layer-2 device defines a second test data unit based on the first test data unit that includes the quality datum associated with processing the first test data unit. The layer-2 device sends the second test data unit to the layer-3 device. The layer-3 device defines a quality datum associated with processing the second test data unit at the layer-3 device and defines a third test data unit based on the second test data unit.

Abstract: In some embodiments, an apparatus includes a layer-2 device operably coupled to a source device and a destination device and disposed within a data path (1) between the source device and the destination device, and (2) includes at least one layer-3 device. The layer-2 device receives a first test data unit from the source device, and defines a quality datum associated with processing the first test data unit. The layer-2 device defines a second test data unit based on the first test data unit that includes the quality datum associated with processing the first test data unit. The layer-2 device sends the second test data unit to the layer-3 device. The layer-3 device defines a quality datum associated with processing the second test data unit at the layer-3 device and defines a third test data unit based on the second test data unit.