People usually form a certain impression in their minds after reading an article. This ability that is easy for humans is not the case for computers.
According to Xinzhiyuan, the study of natural language processing has been based on sentence-level reading comprehension for a long time. For example, give the computer a sentence, understand the subject and the object in the sentence, make up the supplement, who did what, and so on. However, the problem of understanding long texts has always been a difficult point of research, because it involves higher-dimensional research content such as coherence, context and reasoning between sentences.
For example, the following text: The Rhine is a European river that begins in the Swiss canton of Graubünden in the southeastern Swiss Alps, forms part of the Swiss-Austrian, Swiss-Liechtenstein border, Swiss-German and then the Franco-German border, Then flows through the Rhineland and eventually empties into the North Sea in the Netherlands. The biggest city on the river Rhine is Cologne, Germany with a population of more than 1,050,000 people. It is the second-longest river in Central and Western Europe (after The Danube), at about 1,230 km (760 mi), with an average discharge of about 2,900 m3/s (100,000 cu ft/s). (About: the Rhine is a famous river in Europe, starting in the Swiss Alps, flowing through Switzerland, Austria, Liechtenstein, France, Germany, the Netherlands, and finally into the North Sea in the Netherlands. The largest city on the Rhine is Cologne, Germany. It is The second longest river in Central and Western Europe, behind the Danube, about 1230 km.)
If you ask questions about this paragraph: What river is bigger than the Rhine? (Which river is longer than the Rhine?) People can easily give the answer: Danube (Danube). But now even the best system model gives results that are not satisfactory, and its answer is: Cologne.
It can be seen that it is so difficult to make a computer really understand the content of a text and reason about it like a human being. In answering this question, in addition to dealing with the reference "t" in the text, the computer needs the algorithm and the model to further reason the two expressions "larger than" and "after", so that Danube is the correct answer. In addition, because the text does not explicitly mention that Danube is "river", it increases the difficulty of reasoning of the system.
But the development of big data has made scholars see the dawn of this research direction. The ever-increasing text data, coupled with deep learning algorithms and massive cloud computing resources, enable researchers to do peer-to-peer learning for long texts, ie, modeling sentences, phrases, and contexts. It hides certain reasoning ability. Therefore, the current field of natural language processing puts chapter-based understanding on the agenda of research and has become one of the research focuses in this field.
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