Speech Translation Device Automatic Information Extraction
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Solution Overview
Problem
Current speech translation devices require manual reentry of information from human-to-human dialogues into human-machine interfaces, leading to inefficiencies and time wastage, and often result in errors due to imperfect recognition and translation processes.
Innovation Solution
A speech translation device with integrated information extraction capabilities that automatically extracts relevant information during human-to-human communication, using modules like semantic parsing, named entity tagging, and information retrieval, and provides multimodal error correction techniques to enhance accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual reentry of information from human-to-human dialogues into human-machine interfaces is performed, then information can be transferred between systems, but it leads to inefficiencies and time wastage
Solution Approach 1:
The system automatically extracts information from the human-to-human dialogue and transfers it to the human-machine interface without requiring manual reentry. The information extraction module autonomously processes the dialogue, identifies relevant information, and populates the interface forms automatically, making the system serve itself rather than requiring human intervention for data transfer.
Solution Approach 2:
The system performs preliminary extraction and processing of information from the dialogue during the conversation itself, before the information needs to be entered into the human-machine interface. This preliminary action captures the information in its raw form from the dialogue and prepares it for automatic transfer, eliminating the need for subsequent manual entry.
2Reliability
If manual reentry of information is performed, then information can be transferred, but it results in errors due to imperfect recognition and translation processes
Solution Approach 1:
The system provides feedback to the user about the extracted information and allows for correction. The user can review the extracted information and make corrections if necessary, and the system can ask clarifying questions during the dialogue to ensure accurate information extraction. This feedback loop improves the reliability of information transfer by allowing verification and correction of extracted data.
Solution Approach 2:
The system uses an intermediary information extraction module that acts as a mediator between the human-to-human dialogue and the human-machine interface. This intermediary component processes the dialogue, extracts relevant information, and formats it for the interface, providing a buffer that improves accuracy by allowing for careful extraction and verification rather than direct transfer.
3Productivity
If the speech translation device extracts information automatically during dialogue, then manual data entry is reduced, but the device complexity increases
Solution Approach 1:
The information extraction module is designed to handle multiple types of information extraction tasks within a single integrated system. It can extract various types of information (names, locations, dates, relationships) from dialogues and transfer them to different types of human-machine interfaces, making the system multi-functional and reducing the need for separate specialized systems for each extraction task.
Solution Approach 2:
The system merges the speech translation functionality with the information extraction functionality into a single integrated device. By combining these functions, the system eliminates the need for separate manual transfer processes and reduces overall system complexity compared to having separate systems that would require manual integration.
4Ease of operation
If the system extracts information from dialogue, then workflow is improved, but errors in recognition and translation still occur
Solution Approach 1:
The system provides feedback to the user about the extracted information and allows for correction. The user can review the extracted information and make corrections if necessary, and the system can ask clarifying questions during the dialogue to ensure accurate information extraction. This feedback loop improves the reliability of information transfer by allowing verification and correction of extracted data.
Solution Approach 2:
The system automatically extracts information from the human-to-human dialogue and transfers it to the human-machine interface without requiring manual reentry. The information extraction module autonomously processes the dialogue, identifies relevant information, and populates the interface forms automatically, making the system serve itself rather than requiring human intervention for data transfer.
Data Source
AI summary
Computer-implemented systems and methods for extracting information during a human-to-human mono-lingual or multi-lingual dialog between two speakers are disclosed. Information from either the recognized speech (or the translation thereof) by the second speaker and/or the recognized speech by the first speaker (or the translation thereof) is extracted. The extracted information is then entered into an electronic form stored in a data store.