6 results about "Representation language" patented technology

The present disclosure is related to techniques for converting a natural language utterance to a logical form query and deriving a natural language interpretation of the logical form query. The techniques include accessing a Meaning Resource Language (MRL) query and converting the MRL query into a MRL structure including logical form statements. The converting includes extracting operations and associated attributes from the MRL query and generating the logical form statements from the operations and associated attributes. The techniques further include translating each of the logical form statements into a natural language expression based on a grammar data structure that includes a set of rules for translating logical form statements into corresponding natural language expressions, combining the natural language expressions into a single natural language expression, and providing the single natural language expression as an interpretation of the natural language utterance.

The application discloses a 3D multi-target detection method based on semantic driving single image for ATS, which processes input RGB images, extracts 3D bounding boxes of each object in the images, generates all potential 2D projections of 3D objects in a scene, extracts keywords, phrases and semantic information in the description to form feature information P representing the language description t ; fuses 2D image information and 3D geometric information of the object to obtain complete object representation a ; associates language features extracted from the language description with the detected 3D object, captures the semantic correspondence between the text and the visual modal; filters the target according to the generated matching score to obtain all targets meeting the natural language description. The application improves the accuracy and efficiency of retrieval and recognition, realizes higher recognition accuracy, significantly reduces the computational complexity, can improve the accuracy and speed of cross-modal retrieval, and greatly improves the accurate recognition and positioning ability of traffic events.

Techniques for augmenting training data include accessing training data comprising a plurality of training examples comprising a first training example comprising a first natural language utterance and a first logical form for the first natural language utterance. A second natural language utterance is generated by adding or replacing one or more values in the first natural language utterance. A logical form for the second natural language utterance is generated. A second training example is generated, comprising the second natural language utterance and the logical form for the second natural language utterance. The training data is augmented by adding the second training example to the plurality of training examples to generate an augmented training data set. A machine learning model is trained to generate logical forms for utterances using the augmented training data set.

Techniques are disclosed herein for using named entity recognition to resolve entity expression while transforming natural language to a meaning representation language. In one aspect, a method includes accessing natural language text, predicting, by a first machine learning model, a class label for a token in the natural language text, predicting, by a second machine-learning model, operators for a meaning representation language and a value or value span for each attribute of the operators, in response to determining that the value or value span for a particular attribute matches the class label, converting a portion of the natural language text for the value or value span into a resolved format, and outputting syntax for the meaning representation language. The syntax comprises the operators with the portion of the natural language text for the value or value span in the resolved format.

Techniques are disclosed herein for addressing catastrophic forgetting and over-generalization while training a model to transform natural language to a logical form such as a meaning representation language. The techniques include accessing training data comprising natural language examples, augmenting the training data to generate expanded training data, training a machine learning model on the expanded training data, and providing the trained machine learning model. The augmenting includes (i) generating contrastive examples by revising natural language of examples identified to have caused regression during training of a machine learning model with the training data, (ii) generating alternative examples by modifying operators of examples identified within the training data that belong to a concept that exhibits bias, or (iii) a combination of (i) and (ii).

The invention provides a user feedback detection method, which comprises the following steps of: modeling preset language information according to a causal architecture to obtain a structural causal graph; the structural causal diagram is represented by the following formula. Wherein semantic content factors in the causal architecture, semantic style factors in the causal architecture, text information included in the language information and label information included in the language information are expressed. And performing content focusing on the language information according to a preset large language model to obtain a focusing representation. And modeling according to the focusing representation and the structural causal graph to obtain a causal decoupling model. And detecting the user feedback according to the causal decoupling model to obtain a detection result. Therefore, accurate recognition of user feedback by the language model in dialect, slang, non-standard sentence pattern and other multi-style scenes is realized.