Chapter 252 Alternative academic habits

What does less time mean?

It means an unprecedented improvement in efficiency.

Eve Carly had never quite understood how Lin Hui suddenly rose to prominence.

Now she has made up her mind.

However, in this case, Eve Carly felt that some of the previous estimates of Lin Hui were a bit conservative.

Training on a corpus of the same size can save at least 70% of the time by introducing a pre-training mechanism based on transfer ideas than conventional training.

This data is quite exaggerated.

If it is as Eve Carly guessed.

What Lin Hui has come up with is not only exaggerated data, but its functions will also be outrageous.

You must know that things developed based on the idea of ​​migration can be "migrated" in a sense, which is portability.

This is so fucking outrageous.

Research involving text summarization and even the entire field of natural language processing used to be more or less self-centered.

But if it is portable, it is entirely possible to penetrate into other fields.

Thinking of this, Eve Carly suddenly felt that Lin Hui's focus must not be on the small fish pond of natural language processing.

Lin Hui is playing a big game of chess.

Although I have known Lin Hui for a short time, as someone who has frequent academic exchanges with Lin Hui.

Eve Carly was certain that Lin Hui's academic ambitions were enormous.

Previously, Eve Kali felt that Lin Hui could open a new door in the direction of natural language processing.

It now seems that the direction Lin Hui will influence in the future is definitely not just the direction of natural language processing.

When it comes to the entire field of machine learning, Lin Hui will make great achievements.

It may even be far more than that, and Eve Carly is looking forward to all this.

There is nothing more exciting than witnessing the rise of a genius.

(If there is, it may only be witnessing the destruction of a "god".)

Even though Lin Hui doesn't have any titles yet.

However, Lin Hui's achievements in the past have been dazzling enough.

Eve Carly believes that Lin Hui will realize his ambition bit by bit.

Why can Eve Carly make such a judgment?

Lin Hui's brilliant academic achievements in the past are just one of the reasons why Eve Carly came to this conclusion.

This is not the most important reason.

What really allowed Eve Carly to conclude that Lin Hui could realize his ambition was that Lin Hui had his own academic style.

Compared to visible academic achievements.

Academic style is a very metaphysical thing that cannot be seen or even touched.

It sounds like something illusory.

But academic style does exist.

Discussions about the term "academic style" often appear in various academic exchanges and daily discussions among scientific researchers.

Regardless of academic route or academic habits, these things will affect the formation of academic style to some extent.

To measure whether a scientific researcher is above or below the standard in academic terms, it generally depends on whether he or she has an independent academic style.

In general, researchers who are just scratching the surface in academic terms generally do not have their own academic style.

His research results are more arbitrary, and his research topics are mainly "follow-up research".

Researchers above the level generally have a stable academic style.

Stability of academic style does not mean everything though.

But at least it means that the researcher has a relatively clear plan for the academic route.

Perhaps Lin Hui himself didn't notice his academic style.

But Eve Carly felt that Lin Hui had his own academic style.

And the style is very obvious.

The fact that Lin Hui has an academic style can also reflect the stability of his academic line.

Therefore, Eve Carly believed that Lin Hui could realize his ambition step by step.

And what kind of academic style does Lin Hui have?

Eve Carly is too specific and cannot be accurately described at the moment.

But in terms of academic habits, Eve Carly felt that Lin Hui had a very distinctive characteristic.

That is Lin Hui is always committed to winning at the starting line.

Of course, winning at the starting line is just a metaphor. The exact expression should be

——When solving academic problems and practical engineering problems, Lin Hui is very inclined to nip possible problems in the bud.

Coming to this judgment, Eve Carly naturally has the corresponding basis.

Take the pre-training mentioned by Lin Hui in the supplementary content of the paper not long ago.

In the past, when it came to "training", people often thought that the model generated by the training was adjusted by machine learning experts.

There are few people like Lin Hui who have such thoughtful ideas about the training process.

After all, corpus training is already a very advanced issue in language model construction.

In addition to this example, there is also the first conversation with Lin Hui after coming to China.

At that time, the two of them talked about how to deal with issues related to "the dimensionality explosion that may result from processing the corpus after vectorization."

The original dimensionality reduction methods envisioned by Eve Carly include converting high-dimensional models into low-dimensional models, reducing high-dimensional data obtained after analysis into low-dimensional data, etc.

The idea proposed by Lin Hui is to vectorize the corpus to obtain the original high-dimensional vector data and directly perform dimensionality reduction processing.

You must know that when it came to the issue of dimensionality explosion before, few researchers thought of directly making a fuss about the original data with relatively high dimensions.

After all, this involves abstracting corpus information into vector raw data, which is almost a particularly advanced step in corresponding research.

Eve Carly felt that these could support her previous judgment.

Based on her previous judgment, further inferences can be made on this basis.

If a scientific research project involves multiple links, each link has room for action.

Then Lin Hui will definitely put the main effort into the initial stage or open up a new track before the initial stage.

What's the use of knowing this?

Of course it is useful, even very useful.

Previously, Eve Carly was very unclear as to why Lin Hui wanted to acquire the patent she created, namely "A New Method for Text Judgment, Screening and Comparison".

After Lin Hui proposed the generative text summarization algorithm.

Current automatic summarization implementation methods are mainly divided into extractive methods and generative methods:

There are many differences in principles and practical performance between these two summarization methods.

But both are essentially automatic text summarization.

For all automatic text summarization, its technical framework can be summarized as:

Content representation → weight calculation → content selection → content organization.

Content representation is the process of dividing original text into text units, mainly preprocessing work such as word segmentation, words, sentences, etc.;

The main purpose of content representation is to process raw text into a form that is easy for algorithms to analyze through preprocessing.

Weight calculation is to calculate the corresponding weight score for the text unit (that is, the original text after preprocessing). There are various ways to calculate the weight, such as calculating the weight based on feature scores, sequence annotations, classification models, etc. to extract content features.
Chapter completed!