azure-language-understanding - 如何训练具有非常密切相关话语的 luis，应该以 2 种不同的意图进行训练

Question

我有在 LUIS 中训练的非常大的数据。大多数意图具有彼此非常密切相关的话语，因此 LUIS 无法在训练后指出正确的意图。

比如取两个intent1和intent2

示例 1：

意图 1：信用卡交易未显示在我的 TE 中

意图2：HR mini 中没有显示信用卡（这是错误的预测，指向意图1）

示例 2：

intent1：我的电子邮件被锁定

意图2：锁定（这是错误的预测，指向意图1）

intent1 训练有更多的话语，大约 50 个，其中有诸如信用、交易、电子邮件之类的词，但 intent2 没有训练很多。

因此，在我询问意图 2 查询时进行训练后，Luis 预测错误并将我带到意图 1。

有人可以帮助我了解我们如何用这些话语仔细训练模型吗

标签： azure-language-understandingtraining-data

Answer 1

Given that you have a large amount of data, I would suggest you follow the best practices for building LUIS apps.

• For example, ensure the vocabulary for each intent is just for that intent and not overlapping with a different intent.

  Book a flight and Book a hotel use the same vocabulary of book a. This format is the same so it should be the same intent with the different words of flight and hotel as extracted entities.

• The incorrect prediction results show intents that have utterances, which are used as examples for a specific intent, but are predicted for different intents. What you can do is edit the utterance so that it is more specific to the intent and train the app or combine intents if utterances are too closely aligned and train again.

Hope this helps!!