Language is the process of communicating intentions or meanings. Sentences have certain meanings, and humans communicate through understanding and deciphering words in the sentence. Then how are we supposed to reflect this process in deep learning models? Deep learning models are expressed in real number value computation, so it involves the process of converting words into real number values. Previous deep learning models simply substitute words into pre-defined real number values. But there is a downside to this approach since such model is unable to handle linguistic ambiguities like homonyms properly. Take phrases like “Bank Account” and “River Bank” for example. Although the two phrases contain the same word, they have very different meaning. Previously available deep learning models could not convey this difference because they use the same real number value for the word “bank” as shown in [Figure 1]. Therefore, it was necessary to develop a new model that can capture contextual information.
Learn how it could be game over for IoT technology if companies don't get these three things right.
Automation in the past mainly depended on hardware-based “machines” to replace human resources for hazardous or repetitive tasks that required precision. On the other hand, with recent advances in AI and cognitive technologies, such as OCR, and the advent of automation solutions, such as Robotic Process Automation (RPA), the use of software-based robots is currently expanding throughout the entire field of office automation.
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As AI technology advances, the demand for AI services is increasing. The process of building an AI service involves a lot of work. For example, one of them includes creating a correct answer for training and finding a model with high training efficiency. The performance of AI technology will be compromised if activities in the process, albeit small one, are performed negligently. As a result, many researchers and developers still carry out the work manually. Of the numerous tasks involved in the process, the most basic and the most labor- consuming task is labeling, which involves making a correct answer for the data.
With COVID 19 pandemic driving the need for digital workplace, it is becoming more important to integrate into one system information of various quantity and quality scattered across company and to search for right information for timely insights. In response to these demands, there is a growing interest in Question Answering (QA) system that is adept at smartly answering users’ questions with respect to a large volume of documents. This is a reason why the need has emerged for natural language processing and natural language understanding technology such as machine reading comprehension and semantic search that are associated with finding the right information quickly and accurately.
