&nbsp;

<h2><img alt="" src="https://storage.yandexcloud.net/datasouls-ods/images/2020/04/20/2c75bd49-b991-4f9c-83ad-144ae2fb7822/tp.png" />Challenges</h2>

Participants are asked to solve the tasks on NLP:

1. To classify the intents

2. To correct the spelling mistakes

3. To predict the scores in the chats

4. To detect the falsification in telephone calls statistics

You have to create algorithms that will solve the classification problem for tasks 1, 3, and 4, and return a corrected sentence&nbsp;for each sentence for task 2.

<h2>Quality Criteria</h2>

You have to present solutions&nbsp;docker&nbsp;сontainer format. <code>F1-score</code> is the quality metric for the tasks 1,&nbsp;3&nbsp;and&nbsp;4.&nbsp;For the task 2 quality metric is also <code>F1-score</code>, but with an increased penalty factor: the factor is doubled for skipping the error (<code>FN</code>&nbsp;is by a factor of 2), for false detection &ndash; increases in 30 times (<code>FP</code>&nbsp;is by a factor of 30).

<h2>Prizes</h2>

The total prize fund is 500 000 RUB. Teams consisting of up to 4 people of all ages and from all over the world&nbsp;are allowed.&nbsp;The winning teams will be awarded:&nbsp;

1st place - 250 000 RUB,&nbsp;

2nd place - 150 000 RUB,&nbsp;

3rd place - 100 000 RUB.&nbsp;

The winners are determined by the total metric from all 4 tasks of the competition.

<h2>Challenge Description</h2>

1. Classification of intents

The intent of the query is the user&#39;s intention and the purpose of using a certain search engine. For example, in the case of technical support, classifying the intents can help you automatically cluster queries by their subject to generate a response, or quickly redirect them to the right specialists.

2. Correction of the spelling mistakes

A useful task for your own spell check, and for correcting possible typos of users, for example, when accessing chatbots.

3. Prediction of the estimates in the chats

In this task, we propose to find a correlation between the responses of technical support specialists in chat rooms and their final ratings set by users.

4. Detection of falsification in the telephone calls statistics

Unscrupulous operators sometimes try to falsify the call statistics of their call centers. The key indicator that they try to fake is the duration of the call in seconds. It is suggested to determine the real numbers in reports.

<h2>Data</h2>

A full description of the data, as well as the data for training models, will be provided to participants at the offline event at Moscow State University.

<h2>Submisson&nbsp;Format&nbsp;</h2>

A full description of the format and examples of correct solutions will be provided to participants at the offline event at Moscow State University.

&nbsp;

Solve the tasks of chat-based help desk: classify intentions, correct typos, predict responses and fraud

Private

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#	Teams (24)	Score	Last	Entries
1	Mindful Squirrel	2.1844774074	7y	13
2	Magic City	2.1431034257	7y	13
3	Я и мои кореша	2.1187516154	7y	15
4	BoTaniki	2.1095784429	7y	4
5	[SH AI KA]	2.092300269	7y	7
6	Five Lakes	2.0809118831	7y	6
7	2 мастера и экcперт	2.0799395177	7y	9
8	Голова Петра Первого	2.0600961614	7y	12
9	K Nearest Neighbours	1.9983260928	7y	11
10	Textifiers	1.9936933043	7y	5
OOC	ksanvat	1.9930489547	7y	8
11	kollegi	1.9295774094	7y	10
12	3526F2D7BFD74D21BDFC3A820798C2F6	1.5972167007	7y	6
13	Dr. Brown	1.5639724945	7y	5
14	valeriya_bayda	1.2760862008	7y	9
15	Vlako	1.1156471453	7y	15
16	dockerispain	0.7876474476	7y	10
17	volkova.natalia2905	0.773033158	7y	6
OOC	romovpa	0.3493932754	7y	1
18	Богатый дедушка Мороз	0.3493932754	7y	5
19	Equity Charger	0.312661718	7y	9
20	khaiildrvn1999	0.312661718	7y	13
21	тупики	0.0546048515	7y	4
22	Нищий дедушка Мороз	0.0505437121	7y	10