选择组件从一系列候选的回复中预测机器人的答复。
ResponseSelector
NLU的解析输出将具有一个名为response_selector的属性，其中包含每个response selector组件的输出。每个response selector都由它的retrieval_intent参数标识，并存储两个信息：
响应：对应检索意图、预测置信度和相关响应下的预测响应关键字。
排名：排名前10位候选答案的置信度。
ResponseSelector输入为用户信息的稀疏特征或稠密特征，其输出为一种字典，其关键字作为响应选择器的检索意图，值包含预测响应、置信度和检索意图下的响应关键字。
{
"response_selector": {
"faq": {
"response": {
"id": 1388783286124361986,
"confidence": 0.7,
"intent_response_key": "chitchat/ask_weather",
"responses": [
{
"text": "It's sunny in Berlin today",
"image": "https://i.imgur.com/nGF1K8f.jpg"
},
{
"text": "I think it's about to rain."
}
],
"utter_action": "utter_chitchat/ask_weather"
},
"ranking": [
{
"id": 1388783286124361986,
"confidence": 0.7,
"intent_response_key": "chitchat/ask_weather"
},
{
"id": 1388783286124361986,
"confidence": 0.3,
"intent_response_key": "chitchat/ask_name"
}
]
}
}
}如果特定response selector的retrieval_intent参数保留为其默认值，则相应的response selector将在返回的输出中标识为默认值。
{
"response_selector": {
"default": {
"response": {
"id": 1388783286124361986,
"confidence": 0.7,
"intent_response_key": "chitchat/ask_weather",
"responses": [
{
"text": "It's sunny in Berlin today",
"image": "https://i.imgur.com/nGF1K8f.jpg"
},
{
"text": "I think it's about to rain."
}
],
"utter_action": "utter_chitchat/ask_weather"
},
"ranking": [
{
"id": 1388783286124361986,
"confidence": 0.7,
"intent_response_key": "chitchat/ask_weather"
},
{
"id": 1388783286124361986,
"confidence": 0.3,
"intent_response_key": "chitchat/ask_name"
}
]
}
}
}response selector组件可用于构建响应检索模型，以从一组候选响应中直接预测机器人响应。对话管理器使用该模型的预测来说出预测的响应。它将用户输入和响应标签嵌入到同一空间中，并遵循与DIETClassifier完全相同的神经网络架构和优化。
要使用此组件，您的培训数据应包含检索意图。要定义这些，请查看有关NLU培训示例的文档和有关定义检索意图的响应语句的文档。
该算法包括DIETClassifier使用的几乎所有超参数。如果要调整模型，请首先修改以下参数：
epochs：此参数设置算法将看到训练数据的次数（默认值：300）。一次epoch等于所有训练样例的一次前向传播和一次反向传播。有时模型需要更多的 epoch 才能正确学习。有时更多的时代不会影响性能。epoch 的数量越少，模型训练得越快。
hidden_layers_sizes：此参数允许您定义前馈层的数量及其用户消息和意图的输出维度（默认值：text: ], label: []）。列表中的每个条目都对应一个前馈层。例如，如果你设置text: [256, 128]，我们将在transformer前面添加两个前馈层。输入标记的向量（来自用户消息）将传递到这些层。第一层的输出维度为 256，第二层的输出维度为 128。如果使用空列表（默认行为），则不会添加前馈层。确保仅使用正整数值。通常，使用 2 的幂数。此外，通常的做法是列表中的值递减：下一个值小于或等于之前的值。
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embedding_dimension：此参数定义模型内部使用的嵌入层的输出维度（默认值：20）。我们在模型架构中使用多个嵌入层。例如，完整话语和意图的向量在比较和计算损失之前被传递到嵌入层。
number_of_transformer_layers：此参数设置要使用的transformer层数（默认值：2）。transformer层数对应于用于模型的 transformer块。
transformer_size：此参数设置 transformer中的单元数（默认值：256）。来自transformer的矢量将具有给定的transformer_size.
connection_density：此参数定义了模型中所有前馈层设置为非零值的内核权重的分数（默认值：0.2）。该值应介于 0 和 1 之间。如果设置connection_density 为 1，则不会将任何内核权重设置为 0，该层充当标准前馈层。您不应设置connection_density为 0，因为这会导致所有内核权重为 0，即模型无法学习。
constrain_similarities：此参数设置为True对所有相似项应用 sigmoid 交叉熵损失。这有助于将输入标签和负标签之间的相似性保持在较小的值。这应该有助于更好地将模型推广到现实世界的测试集。
model_confidence：此参数允许用户配置在推理期间如何计算置信度。它只能将一个值作为输入，即softmax。在softmax中，置信度在范围内[0, 1]。计算出的相似度用softmax激活函数归一化。
---------------------------------+------------------+--------------------------------------------------------------+
| Parameter | Default Value | Description |
+=================================+==================+==============================================================+
| hidden_layers_sizes | text: [] | Hidden layer sizes for layers before the embedding layers |
| | label: [] | for user messages and labels. The number of hidden layers is |
| | | equal to the length of the corresponding list. |
+---------------------------------+------------------+--------------------------------------------------------------+
| share_hidden_layers | False | Whether to share the hidden layer weights between user |
| | | messages and labels. |
+---------------------------------+------------------+--------------------------------------------------------------+
| transformer_size | 256 | Number of units in transformer. |
+---------------------------------+------------------+--------------------------------------------------------------+
| number_of_transformer_layers | 2 | Number of transformer layers. |
+---------------------------------+------------------+--------------------------------------------------------------+
| number_of_attention_heads | 4 | Number of attention heads in transformer. |
+---------------------------------+------------------+--------------------------------------------------------------+
| use_key_relative_attention | False | If 'True' use key relative embeddings in attention. |
+---------------------------------+------------------+--------------------------------------------------------------+
| use_value_relative_attention | False | If 'True' use value relative embeddings in attention. |
+---------------------------------+------------------+--------------------------------------------------------------+
| max_relative_position | None | Maximum position for relative embeddings. |
+---------------------------------+------------------+--------------------------------------------------------------+
| unidirectional_encoder | False | Use a unidirectional or bidirectional encoder. |
+---------------------------------+------------------+--------------------------------------------------------------+
| batch_size | [64, 256] | Initial and final value for batch sizes. |
| | | Batch size will be linearly increased for each epoch. |
| | | If constant `batch_size` is required, pass an int, e.g. `8`. |
+---------------------------------+------------------+--------------------------------------------------------------+
| batch_strategy | "balanced" | Strategy used when creating batches. |
| | | Can be either 'sequence' or 'balanced'. |
+---------------------------------+------------------+--------------------------------------------------------------+
| epochs | 300 | Number of epochs to train. |
+---------------------------------+------------------+--------------------------------------------------------------+
| random_seed | None | Set random seed to any 'int' to get reproducible results. |
+---------------------------------+------------------+--------------------------------------------------------------+
| learning_rate | 0.001 | Initial learning rate for the optimizer. |
+---------------------------------+------------------+--------------------------------------------------------------+
| embedding_dimension | 20 | Dimension size of embedding vectors. |
+---------------------------------+------------------+--------------------------------------------------------------+
| dense_dimension | text: 128 | Dense dimension for sparse features to use. |
| | label: 20 | |
+---------------------------------+------------------+--------------------------------------------------------------+
| concat_dimension | text: 128 | Concat dimension for sequence and sentence features. |
| | label: 20 | |
+---------------------------------+------------------+--------------------------------------------------------------+
| number_of_negative_examples | 20 | The number of incorrect labels. The algorithm will minimize |
| | | their similarity to the user input during training. |
+---------------------------------+------------------+--------------------------------------------------------------+
| similarity_type | "auto" | Type of similarity measure to use, either 'auto' or 'cosine' |
| | | or 'inner'. |
+---------------------------------+------------------+--------------------------------------------------------------+
| loss_type | "cross_entropy" | The type of the loss function, either 'cross_entropy' |
| | | or 'margin'. If type 'margin' is specified, |
| | | "model_confidence=cosine" will be used which is deprecated |
| | | as of 2.3.4. See footnote (1). |
+---------------------------------+------------------+--------------------------------------------------------------+
| ranking_length | 10 | Number of top intents to report. Set to 0 to report all |
| | | intents. |
+---------------------------------+------------------+--------------------------------------------------------------+
| renormalize_confidences | False | Normalize the reported top intents. Applicable only with loss|
| | | type 'cross_entropy' and 'softmax' confidences. |
+---------------------------------+------------------+--------------------------------------------------------------+
| maximum_positive_similarity | 0.8 | Indicates how similar the algorithm should try to make |
| | | embedding vectors for correct labels. |
| | | Should be 0.0 < ... < 1.0 for 'cosine' similarity type. |
+---------------------------------+------------------+--------------------------------------------------------------+
| maximum_negative_similarity | -0.4 | Maximum negative similarity for incorrect labels. |
| | | Should be -1.0 < ... < 1.0 for 'cosine' similarity type. |
+---------------------------------+------------------+--------------------------------------------------------------+
| use_maximum_negative_similarity | True | If 'True' the algorithm only minimizes maximum similarity |
| | | over incorrect intent labels, used only if 'loss_type' is |
| | | set to 'margin'. |
+---------------------------------+------------------+--------------------------------------------------------------+
| scale_loss | False | Scale loss inverse proportionally to confidence of correct |
| | | prediction. |
+---------------------------------+------------------+--------------------------------------------------------------+
| regularization_constant | 0.002 | The scale of regularization. |
+---------------------------------+------------------+--------------------------------------------------------------+
| negative_margin_scale | 0.8 | The scale of how important it is to minimize the maximum |
| | | similarity between embeddings of different labels. |
+---------------------------------+------------------+--------------------------------------------------------------+
| connection_density | 0.2 | Connection density of the weights in dense layers. |
| | | Value should be between 0 and 1. |
+---------------------------------+------------------+--------------------------------------------------------------+
| drop_rate | 0.2 | Dropout rate for encoder. Value should be between 0 and 1. |
| | | The higher the value the higher the regularization effect. |
+---------------------------------+------------------+--------------------------------------------------------------+
| drop_rate_attention | 0.0 | Dropout rate for attention. Value should be between 0 and 1. |
| | | The higher the value the higher the regularization effect. |
+---------------------------------+------------------+--------------------------------------------------------------+
| use_sparse_input_dropout | True | If 'True' apply dropout to sparse input tensors. |
+---------------------------------+------------------+--------------------------------------------------------------+
| use_dense_input_dropout | True | If 'True' apply dropout to dense input tensors. |
+---------------------------------+------------------+--------------------------------------------------------------+
| evaluate_every_number_of_epochs | 20 | How often to calculate validation accuracy. |
| | | Set to '-1' to evaluate just once at the end of training. |
+---------------------------------+------------------+--------------------------------------------------------------+
| evaluate_on_number_of_examples | 0 | How many examples to use for hold out validation set. |
| | | Large values may hurt performance, e.g. model accuracy. |
+---------------------------------+------------------+--------------------------------------------------------------+
| intent_classification | True | If 'True' intent classification is trained and intents are |
| | | predicted. |
+---------------------------------+------------------+--------------------------------------------------------------+
| entity_recognition | True | If 'True' entity recognition is trained and entities are |
| | | extracted. |
+---------------------------------+------------------+--------------------------------------------------------------+
| use_masked_language_model | False | If 'True' random tokens of the input message will be masked |
| | | and the model has to predict those tokens. It acts like a |
| | | regularizer and should help to learn a better contextual |
| | | representation of the input. |
+---------------------------------+------------------+--------------------------------------------------------------+
| tensorboard_log_directory | None | If you want to use tensorboard to visualize training |
| | | metrics, set this option to a valid output directory. You |
| | | can view the training metrics after training in tensorboard |
| | | via 'tensorboard --logdir <path-to-given-directory>'. |
+---------------------------------+------------------+--------------------------------------------------------------+
| tensorboard_log_level | "epoch" | Define when training metrics for tensorboard should be |
| | | logged. Either after every epoch ('epoch') or for every |
| | | training step ('batch'). |
+---------------------------------+------------------+--------------------------------------------------------------+
| featurizers | [] | List of featurizer names (alias names). Only features |
| | | coming from the listed names are used. If list is empty |
| | | all available features are used. |
+---------------------------------+------------------+--------------------------------------------------------------+
| checkpoint_model | False | Save the best performing model during training. Models are |
| | | stored to the location specified by `--out`. Only the one |
| | | best model will be saved. |
| | | Requires `evaluate_on_number_of_examples > 0` and |
| | | `evaluate_every_number_of_epochs > 0` |
+---------------------------------+------------------+--------------------------------------------------------------+
| split_entities_by_comma | True | Splits a list of extracted entities by comma to treat each |
| | | one of them as a single entity. Can either be `True`/`False` |
| | | globally, or set per entity type, such as: |
| | | ``` |
| | | ... |
| | | - name: DIETClassifier |
| | | split_entities_by_comma: |
| | | address: True |
| | | ... |
| | | ... |
| | | ``` |
+---------------------------------+------------------+--------------------------------------------------------------+
| constrain_similarities | False | If `True`, applies sigmoid on all similarity terms and adds |
| | | it to the loss function to ensure that similarity values are |
| | | approximately bounded. Used only if `loss_type=cross_entropy`|
+---------------------------------+------------------+--------------------------------------------------------------+
| model_confidence | "softmax" | Affects how model's confidence for each intent |
| | | is computed. Currently, only one value is supported: |
| | | 1. `softmax` - Similarities between input and intent |
| | | embeddings are post-processed with a softmax function, |
| | | as a result of which confidence for all intents sum up to 1. |
| | | This parameter does not affect the confidence for entity |
| | | prediction. |
+---------------------------------+------------------+--------------------------------------------------------------+该组件还可以被配置为针对特定检索意图训练响应选择器。参数retrieval_intent设置此响应选择器模型所针对的检索意图的名称。默认值为“无”，即模型针对所有检索意图进行训练。
在默认配置中，组件使用检索意图和响应键（例如faq/ask_name）作为训练标签。或者，也可以通过将use_text_as_label切换为True，将其配置为使用响应的文本作为训练标签。在此模式下，组件将使用第一个具有文本属性的可用响应进行训练。如果没有找到，则返回使用检索意图和响应键作为标签。