自然語言處理NLP星空智能對話機器人系列：深入理解Transformer自然語言處理 SRL

# Gavin大咖金句

Gavin：理論上將Transformer能夠更好的處理一切以“set of units”存在的數據，而計算機視覺、語音、自然語言處理等屬於這種類型的數據，所以理論上講Transformer會在接下來數十年對這些領域形成主導性的統治力。

Gavin：A feedforward network with a single layer is sufficient to represent any function, but the layer may be infeasibly large and may fail to learn and generalize correctly. — Ian Goodfellow, DLB

Gavin：Transformer是人工智能領域的新一代的引擎，本質是研究結構關系、工業界實踐的核心是基於Transformer實現萬物皆流。

Gavin：Non-linearity是Transformer的魔法

 

 

 

 在本文中，我們將運行一個基於BERT的Transformer解決問題的示例，讓我們從一個復雜的樣本開始，基於BERT變換器分析這個樣本

樣本4將我們帶入更棘手的SRL領域，該示例將“Alice”與動詞“liked”分開，形成了一種長期依賴關系，必須跳過“whose
husband went jogging every Sunday.” 

句子的樣本為：

"Alice, whose husband went jogging every Sunday, liked to go to a dancing class in the meantime."

 

人類可以隔離“Alice”，並找到謂詞：

"Alice, whose husband went jogging every Sunday, liked to go to a dancing class in the meantime."

 

BERT模型能像我們一樣找到謂詞嗎？ 讓我們首先運行SRL.ipynb中的代碼 

!echo '{"sentence": "Alice, whose husband went jogging every Sunday, liked to go to a dancing class in the meantime."}' | \
allennlp predict https://storage.googleapis.com/allennlp-public-models/bert-base-srl-2020.03.24.tar.gz -

運行結果如下：

2020-12-20 09:08:43,153 - INFO - transformers.file_utils - PyTorch version 1.5.1 available.
2020-12-20 09:08:43.324358: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.10.1
2020-12-20 09:08:45,080 - INFO - transformers.file_utils - TensorFlow version 2.4.0 available.
2020-12-20 09:08:46,294 - INFO - allennlp.common.file_utils - checking cache for https://storage.googleapis.com/allennlp-public-models/bert-base-srl-2020.03.24.tar.gz at /root/.allennlp/cache/e20d5b792a8d456a1a61da245d1856d4b7778efe69ac3c30759af61940aa0f42.f72523a9682cb1f5ad3ecf834075fe53a1c25a6bcbf4b40c11e13b7f426a4724
2020-12-20 09:08:46,294 - INFO - allennlp.common.file_utils - waiting to acquire lock on /root/.allennlp/cache/e20d5b792a8d456a1a61da245d1856d4b7778efe69ac3c30759af61940aa0f42.f72523a9682cb1f5ad3ecf834075fe53a1c25a6bcbf4b40c11e13b7f426a4724
2020-12-20 09:08:46,295 - INFO - filelock - Lock 139693451663232 acquired on /root/.allennlp/cache/e20d5b792a8d456a1a61da245d1856d4b7778efe69ac3c30759af61940aa0f42.f72523a9682cb1f5ad3ecf834075fe53a1c25a6bcbf4b40c11e13b7f426a4724.lock
2020-12-20 09:08:46,295 - INFO - allennlp.common.file_utils - cache of https://storage.googleapis.com/allennlp-public-models/bert-base-srl-2020.03.24.tar.gz is up-to-date
2020-12-20 09:08:46,295 - INFO - filelock - Lock 139693451663232 released on /root/.allennlp/cache/e20d5b792a8d456a1a61da245d1856d4b7778efe69ac3c30759af61940aa0f42.f72523a9682cb1f5ad3ecf834075fe53a1c25a6bcbf4b40c11e13b7f426a4724.lock
2020-12-20 09:08:46,295 - INFO - allennlp.models.archival - loading archive file https://storage.googleapis.com/allennlp-public-models/bert-base-srl-2020.03.24.tar.gz from cache at /root/.allennlp/cache/e20d5b792a8d456a1a61da245d1856d4b7778efe69ac3c30759af61940aa0f42.f72523a9682cb1f5ad3ecf834075fe53a1c25a6bcbf4b40c11e13b7f426a4724
2020-12-20 09:08:46,295 - INFO - allennlp.models.archival - extracting archive file /root/.allennlp/cache/e20d5b792a8d456a1a61da245d1856d4b7778efe69ac3c30759af61940aa0f42.f72523a9682cb1f5ad3ecf834075fe53a1c25a6bcbf4b40c11e13b7f426a4724 to temp dir /tmp/tmpmnefy_l0
2020-12-20 09:08:50,552 - INFO - allennlp.common.params - type = from_instances
2020-12-20 09:08:50,552 - INFO - allennlp.data.vocabulary - Loading token dictionary from /tmp/tmpmnefy_l0/vocabulary.
2020-12-20 09:08:50,552 - INFO - filelock - Lock 139692801159064 acquired on /tmp/tmpmnefy_l0/vocabulary/.lock
2020-12-20 09:08:50,580 - INFO - filelock - Lock 139692801159064 released on /tmp/tmpmnefy_l0/vocabulary/.lock
2020-12-20 09:08:50,580 - INFO - allennlp.common.params - model.type = srl_bert
2020-12-20 09:08:50,581 - INFO - allennlp.common.params - model.regularizer = None
2020-12-20 09:08:50,581 - INFO - allennlp.common.params - model.bert_model = bert-base-uncased
2020-12-20 09:08:50,581 - INFO - allennlp.common.params - model.embedding_dropout = 0.1
2020-12-20 09:08:50,581 - INFO - allennlp.common.params - model.initializer = <allennlp.nn.initializers.InitializerApplicator object at 0x7f0cc3c6a7b8>
2020-12-20 09:08:50,581 - INFO - allennlp.common.params - model.label_smoothing = None
2020-12-20 09:08:50,581 - INFO - allennlp.common.params - model.ignore_span_metric = False
2020-12-20 09:08:50,581 - INFO - allennlp.common.params - model.srl_eval_path = /usr/local/lib/python3.6/dist-packages/allennlp_models/structured_prediction/tools/srl-eval.pl
2020-12-20 09:08:50,888 - INFO - transformers.configuration_utils - loading configuration file https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-uncased-config.json from cache at /root/.cache/torch/transformers/4dad0251492946e18ac39290fcfe91b89d370fee250efe9521476438fe8ca185.7156163d5fdc189c3016baca0775ffce230789d7fa2a42ef516483e4ca884517
2020-12-20 09:08:50,889 - INFO - transformers.configuration_utils - Model config BertConfig {
  "architectures": [
    "BertForMaskedLM"
  ],
  "attention_probs_dropout_prob": 0.1,
  "hidden_act": "gelu",
  "hidden_dropout_prob": 0.1,
  "hidden_size": 768,
  "initializer_range": 0.02,
  "intermediate_size": 3072,
  "layer_norm_eps": 1e-12,
  "max_position_embeddings": 512,
  "model_type": "bert",
  "num_attention_heads": 12,
  "num_hidden_layers": 12,
  "pad_token_id": 0,
  "type_vocab_size": 2,
  "vocab_size": 30522
}

2020-12-20 09:08:50,928 - INFO - transformers.modeling_utils - loading weights file https://cdn.huggingface.co/bert-base-uncased-pytorch_model.bin from cache at /root/.cache/torch/transformers/f2ee78bdd635b758cc0a12352586868bef80e47401abe4c4fcc3832421e7338b.36ca03ab34a1a5d5fa7bc3d03d55c4fa650fed07220e2eeebc06ce58d0e9a157
2020-12-20 09:08:53,601 - INFO - allennlp.nn.initializers - Initializing parameters
2020-12-20 09:08:53,602 - INFO - allennlp.nn.initializers - Done initializing parameters; the following parameters are using their default initialization from their code
2020-12-20 09:08:53,602 - INFO - allennlp.nn.initializers -    bert_model.embeddings.LayerNorm.bias
2020-12-20 09:08:53,602 - INFO - allennlp.nn.initializers -    bert_model.embeddings.LayerNorm.weight
2020-12-20 09:08:53,602 - INFO - allennlp.nn.initializers -    bert_model.embeddings.position_embeddings.weight
2020-12-20 09:08:53,602 - INFO - allennlp.nn.initializers -    bert_model.embeddings.token_type_embeddings.weight
2020-12-20 09:08:53,602 - INFO - allennlp.nn.initializers -    bert_model.embeddings.word_embeddings.weight
2020-12-20 09:08:53,602 - INFO - allennlp.nn.initializers -    bert_model.encoder.layer.0.attention.output.LayerNorm.bias
2020-12-20 09:08:53,602 - INFO - allennlp.nn.initializers -    bert_model.encoder.layer.0.attention.output.LayerNorm.weight
2020-12-20 09:08:53,602 - INFO - allennlp.nn.initializers -    bert_model.encoder.layer.0.attention.output.dense.bias
2020-12-20 09:08:53,602 - INFO - allennlp.nn.initializers -    bert_model.encoder.layer.0.attention.output.dense.weight
2020-12-20 09:08:53,602 - INFO - allennlp.nn.initializers -    bert_model.encoder.layer.0.attention.self.key.bias
2020-12-20 09:08:53,602 - INFO - allennlp.nn.initializers -    bert_model.encoder.layer.0.attention.self.key.weight
2020-12-20 09:08:53,602 - INFO - allennlp.nn.initializers -    bert_model.encoder.layer.0.attention.self.query.bias
2020-12-20 09:08:53,603 - INFO - allennlp.nn.initializers -    bert_model.encoder.layer.0.attention.self.query.weight
2020-12-20 09:08:53,603 - INFO - allennlp.nn.initializers -    bert_model.encoder.layer.0.attention.self.value.bias
2020-12-20 09:08:53,603 - INFO - allennlp.nn.initializers -    bert_model.encoder.layer.0.attention.self.value.weight
2020-12-20 09:08:53,603 - INFO - allennlp.nn.initializers -    bert_model.encoder.layer.0.intermediate.dense.bias
2020-12-20 09:08:53,603 - INFO - allennlp.nn.initializers -    bert_model.encoder.layer.0.intermediate.dense.weight
2020-12-20 09:08:53,603 - INFO - allennlp.nn.initializers -    bert_model.encoder.layer.0.output.LayerNorm.bias
2020-12-20 09:08:53,603 - INFO - allennlp.nn.initializers -    bert_model.encoder.layer.0.output.LayerNorm.weight
2020-12-20 09:08:53,603 - INFO - allennlp.nn.initializers -    bert_model.encoder.layer.0.output.dense.bias
2020-12-20 09:08:53,603 - INFO - allennlp.nn.initializers -    bert_model.encoder.layer.0.output.dense.weight
2020-12-20 09:08:53,603 - INFO - allennlp.nn.initializers -    bert_model.encoder.layer.1.attention.output.LayerNorm.bias
2020-12-20 09:08:53,603 - INFO - allennlp.nn.initializers -    bert_model.encoder.layer.1.attention.output.LayerNorm.weight
2020-12-20 09:08:53,603 - INFO - allennlp.nn.initializers -    bert_model.encoder.layer.1.attention.output.dense.bias
2020-12-20 09:08:53,603 - INFO - allennlp.nn.initializers -    bert_model.encoder.layer.1.attention.output.dense.weight
2020-12-20 09:08:53,603 - INFO - allennlp.nn.initializers -    bert_model.encoder.layer.1.attention.self.key.bias
2020-12-20 09:08:53,603 - INFO - allennlp.nn.initializers -    bert_model.encoder.layer.1.attention.self.key.weight
2020-12-20 09:08:53,603 - INFO - allennlp.nn.initializers -    bert_model.encoder.layer.1.attention.self.query.bias
2020-12-20 09:08:53,603 - INFO - allennlp.nn.initializers -    bert_model.encoder.layer.1.attention.self.query.weight
2020-12-20 09:08:53,603 - INFO - allennlp.nn.initializers -    bert_model.encoder.layer.1.attention.self.value.bias
2020-12-20 09:08:53,603 - INFO - allennlp.nn.initializers -    bert_model.encoder.layer.1.attention.self.value.weight
2020-12-20 09:08:53,603 - INFO - allennlp.nn.initializers -    bert_model.encoder.layer.1.intermediate.dense.bias
2020-12-20 09:08:53,603 - INFO - allennlp.nn.initializers -    bert_model.encoder.layer.1.intermediate.dense.weight
2020-12-20 09:08:53,603 - INFO - allennlp.nn.initializers -    bert_model.encoder.layer.1.output.LayerNorm.bias
2020-12-20 09:08:53,604 - INFO - allennlp.nn.initializers -    bert_model.encoder.layer.1.output.LayerNorm.weight
2020-12-20 09:08:53,604 - INFO - allennlp.nn.initializers -    bert_model.encoder.layer.1.output.dense.bias
2020-12-20 09:08:53,604 - INFO - allennlp.nn.initializers -    bert_model.encoder.layer.1.output.dense.weight
2020-12-20 09:08:53,604 - INFO - allennlp.nn.initializers -    bert_model.encoder.layer.10.attention.output.LayerNorm.bias
2020-12-20 09:08:53,604 - INFO - allennlp.nn.initializers -    bert_model.encoder.layer.10.attention.output.LayerNorm.weight
2020-12-20 09:08:53,604 - INFO - allennlp.nn.initializers -    bert_model.encoder.layer.10.attention.output.dense.bias
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2020-12-20 09:08:53,604 - INFO - allennlp.nn.initializers -    bert_model.encoder.layer.10.attention.self.key.bias
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2020-12-20 09:08:53,604 - INFO - allennlp.nn.initializers -    bert_model.encoder.layer.10.attention.self.query.weight
2020-12-20 09:08:53,604 - INFO - allennlp.nn.initializers -    bert_model.encoder.layer.10.attention.self.value.bias
2020-12-20 09:08:53,604 - INFO - allennlp.nn.initializers -    bert_model.encoder.layer.10.attention.self.value.weight
2020-12-20 09:08:53,604 - INFO - allennlp.nn.initializers -    bert_model.encoder.layer.10.intermediate.dense.bias
2020-12-20 09:08:53,604 - INFO - allennlp.nn.initializers -    bert_model.encoder.layer.10.intermediate.dense.weight
2020-12-20 09:08:53,604 - INFO - allennlp.nn.initializers -    bert_model.encoder.layer.10.output.LayerNorm.bias
2020-12-20 09:08:53,604 - INFO - allennlp.nn.initializers -    bert_model.encoder.layer.10.output.LayerNorm.weight
2020-12-20 09:08:53,604 - INFO - allennlp.nn.initializers -    bert_model.encoder.layer.10.output.dense.bias
2020-12-20 09:08:53,604 - INFO - allennlp.nn.initializers -    bert_model.encoder.layer.10.output.dense.weight
2020-12-20 09:08:53,604 - INFO - allennlp.nn.initializers -    bert_model.encoder.layer.11.attention.output.LayerNorm.bias
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2020-12-20 09:08:53,605 - INFO - allennlp.nn.initializers -    bert_model.encoder.layer.11.intermediate.dense.bias
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2020-12-20 09:08:53,605 - INFO - allennlp.nn.initializers -    bert_model.encoder.layer.2.attention.output.LayerNorm.bias
2020-12-20 09:08:53,605 - INFO - allennlp.nn.initializers -    bert_model.encoder.layer.2.attention.output.LayerNorm.weight
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2020-12-20 09:08:53,605 - INFO - allennlp.nn.initializers -    bert_model.encoder.layer.2.attention.output.dense.weight
2020-12-20 09:08:53,605 - INFO - allennlp.nn.initializers -    bert_model.encoder.layer.2.attention.self.key.bias
2020-12-20 09:08:53,605 - INFO - allennlp.nn.initializers -    bert_model.encoder.layer.2.attention.self.key.weight
2020-12-20 09:08:53,605 - INFO - allennlp.nn.initializers -    bert_model.encoder.layer.2.attention.self.query.bias
2020-12-20 09:08:53,605 - INFO - allennlp.nn.initializers -    bert_model.encoder.layer.2.attention.self.query.weight
2020-12-20 09:08:53,605 - INFO - allennlp.nn.initializers -    bert_model.encoder.layer.2.attention.self.value.bias
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2020-12-20 09:08:53,606 - INFO - allennlp.nn.initializers -    bert_model.encoder.layer.2.intermediate.dense.weight
2020-12-20 09:08:53,606 - INFO - allennlp.nn.initializers -    bert_model.encoder.layer.2.output.LayerNorm.bias
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2020-12-20 09:08:53,606 - INFO - allennlp.nn.initializers -    bert_model.encoder.layer.3.attention.output.LayerNorm.bias
2020-12-20 09:08:53,606 - INFO - allennlp.nn.initializers -    bert_model.encoder.layer.3.attention.output.LayerNorm.weight
2020-12-20 09:08:53,606 - INFO - allennlp.nn.initializers -    bert_model.encoder.layer.3.attention.output.dense.bias
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2020-12-20 09:08:53,606 - INFO - allennlp.nn.initializers -    bert_model.encoder.layer.3.attention.self.key.bias
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2020-12-20 09:08:53,606 - INFO - allennlp.nn.initializers -    bert_model.encoder.layer.3.intermediate.dense.bias
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2020-12-20 09:08:53,606 - INFO - allennlp.nn.initializers -    bert_model.encoder.layer.3.output.LayerNorm.bias
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2020-12-20 09:08:53,607 - INFO - allennlp.nn.initializers -    bert_model.encoder.layer.4.attention.output.LayerNorm.bias
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2020-12-20 09:08:53,686 - INFO - allennlp.nn.initializers -    tag_projection_layer.weight
2020-12-20 09:08:54,186 - INFO - allennlp.common.params - dataset_reader.type = srl
2020-12-20 09:08:54,187 - INFO - allennlp.common.params - dataset_reader.lazy = False
2020-12-20 09:08:54,187 - INFO - allennlp.common.params - dataset_reader.cache_directory = None
2020-12-20 09:08:54,187 - INFO - allennlp.common.params - dataset_reader.max_instances = None
2020-12-20 09:08:54,187 - INFO - allennlp.common.params - dataset_reader.manual_distributed_sharding = False
2020-12-20 09:08:54,187 - INFO - allennlp.common.params - dataset_reader.manual_multi_process_sharding = False
2020-12-20 09:08:54,187 - INFO - allennlp.common.params - dataset_reader.token_indexers = None
2020-12-20 09:08:54,187 - INFO - allennlp.common.params - dataset_reader.domain_identifier = None
2020-12-20 09:08:54,187 - INFO - allennlp.common.params - dataset_reader.bert_model_name = bert-base-uncased
2020-12-20 09:08:54,497 - INFO - transformers.tokenization_utils - loading file https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-uncased-vocab.txt from cache at /root/.cache/torch/transformers/26bc1ad6c0ac742e9b52263248f6d0f00068293b33709fae12320c0e35ccfbbb.542ce4285a40d23a559526243235df47c5f75c197f04f37d1a0c124c32c9a084
input 0:  {"sentence": "Alice, whose husband went jogging every Sunday, liked to go to a dancing class in the meantime."}
prediction:  {"verbs": [{"verb": "went", "description": "Alice , [ARG1: whose husband] [V: went] [ARG2: jogging] [ARGM-TMP: every Sunday] , liked to go to a dancing class in the meantime .", "tags": ["O", "O", "B-ARG1", "I-ARG1", "B-V", "B-ARG2", "B-ARGM-TMP", "I-ARGM-TMP", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"verb": "jogging", "description": "Alice , [ARG0: whose husband] went [V: jogging] [ARGM-TMP: every Sunday] , liked to go to a dancing class in the meantime .", "tags": ["O", "O", "B-ARG0", "I-ARG0", "O", "B-V", "B-ARGM-TMP", "I-ARGM-TMP", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"verb": "liked", "description": "[ARG0: Alice , whose husband went jogging every Sunday] , [V: liked] [ARG1: to go to a dancing class in the meantime] .", "tags": ["B-ARG0", "I-ARG0", "I-ARG0", "I-ARG0", "I-ARG0", "I-ARG0", "I-ARG0", "I-ARG0", "O", "B-V", "B-ARG1", "I-ARG1", "I-ARG1", "I-ARG1", "I-ARG1", "I-ARG1", "I-ARG1", "I-ARG1", "I-ARG1", "O"]}, {"verb": "go", "description": "[ARG0: Alice , whose husband went jogging every Sunday] , liked to [V: go] [ARG4: to a dancing class] [ARGM-TMP: in the meantime] .", "tags": ["B-ARG0", "I-ARG0", "I-ARG0", "I-ARG0", "I-ARG0", "I-ARG0", "I-ARG0", "I-ARG0", "O", "O", "O", "B-V", "B-ARG4", "I-ARG4", "I-ARG4", "I-ARG4", "B-ARGM-TMP", "I-ARGM-TMP", "I-ARGM-TMP", "O"]}, {"verb": "dancing", "description": "Alice , whose husband went jogging every Sunday , liked to go to a [V: dancing] [ARG0: class] in the meantime .", "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "B-V", "B-ARG0", "O", "O", "O", "O"]}], "words": ["Alice", ",", "whose", "husband", "went", "jogging", "every", "Sunday", ",", "liked", "to", "go", "to", "a", "dancing", "class", "in", "the", "meantime", "."]}

2020-12-20 09:08:55,842 - INFO - allennlp.models.archival - removing temporary unarchived model dir at /tmp/tmpmnefy_l0

 

 原始輸出相當長，有詳細的描述，讓我們關注感興趣的部分，看看模型是否找到謂詞。是的！它發現了動詞“like”。如原始輸出的摘錄所示： 

[ARG0: Alice , whose husband went jogging every Sunday] , [V: liked] 

現在讓我們看看運行AllenNLP在線用戶界面上的示例。transformer首先找到了Alice's husband：

 

 

Transformer 解釋為：
- 謂語或動詞為“went”

- “whose husband”是一個參數

- “jogging”是與“went”相關的另一個參數

- “every Sunday”是原始輸出中表示為[ARGM-TMP: every Sunday]

 

 Transformer 發現了Alice's husband was doing

 

 

 

 

我們可以看到，動詞“jogging”被識別，並與“whose husband”有關，用“every Sunday”的時態修飾，Transformer 並沒有停在那里，它現在可以檢測出Alice 喜歡什么

 

 

 

 

 

描述Alice的參數有點長，但十分正確。如果我們回到SRL.ipynb的輸出，我們可以看到原始詳細信息證實了分析是正確的：

[ARG0: Alice , whose husband went jogging every Sunday] , [V: liked]
[ARG1: to go to a dancing class in the meantime]

 

transformer 還正確檢測和分析動詞“go”：

 

 我們可以看到，“in the meantime”的時間修飾也被識別，當我們想到BERT模型訓練時使用的簡單序列+動詞輸入時，這是一個相當好的表現。最后，transformer將最后一個動詞“dancing”標識為與“class”相關：

 

 現在我們來看看AllenNLP在線用戶界面生成的格式化文本輸出

input 0: {
    "sentence": "Alice, whose husband went jogging every Sunday, liked to go to a dancing class in the meantime."
}
prediction: {
    "verbs": [{
        "verb": "went",
        "description": "Alice , [ARG1: whose husband] [V: went] [ARG2: jogging] [ARGM-TMP: every Sunday] , liked to go to a dancing class in the meantime .",
        "tags": ["O", "O", "B-ARG1", "I-ARG1", "B-V", "B-ARG2", "B-ARGM-TMP", "I-ARGM-TMP", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]
    }, {
        "verb": "jogging",
        "description": "Alice , [ARG0: whose husband] went [V: jogging] [ARGM-TMP: every Sunday] , liked to go to a dancing class in the meantime .",
        "tags": ["O", "O", "B-ARG0", "I-ARG0", "O", "B-V", "B-ARGM-TMP", "I-ARGM-TMP", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]
    }, {
        "verb": "liked",
        "description": "[ARG0: Alice , whose husband went jogging every Sunday] , [V: liked] [ARG1: to go to a dancing class in the meantime] .",
        "tags": ["B-ARG0", "I-ARG0", "I-ARG0", "I-ARG0", "I-ARG0", "I-ARG0", "I-ARG0", "I-ARG0", "O", "B-V", "B-ARG1", "I-ARG1", "I-ARG1", "I-ARG1", "I-ARG1", "I-ARG1", "I-ARG1", "I-ARG1", "I-ARG1", "O"]
    }, {
        "verb": "go",
        "description": "[ARG0: Alice , whose husband went jogging every Sunday] , liked to [V: go] [ARG4: to a dancing class] [ARGM-TMP: in the meantime] .",
        "tags": ["B-ARG0", "I-ARG0", "I-ARG0", "I-ARG0", "I-ARG0", "I-ARG0", "I-ARG0", "I-ARG0", "O", "O", "O", "B-V", "B-ARG4", "I-ARG4", "I-ARG4", "I-ARG4", "B-ARGM-TMP", "I-ARGM-TMP", "I-ARGM-TMP", "O"]
    }, {
        "verb": "dancing",
        "description": "Alice , whose husband went jogging every Sunday , liked to go to a [V: dancing] [ARG0: class] in the meantime .",
        "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "B-V", "B-ARG0", "O", "O", "O", "O"]
    }],
    "words": ["Alice", ",", "whose", "husband", "went", "jogging", "every", "Sunday", ",", "liked", "to", "go", "to", "a", "dancing", "class", "in", "the", "meantime", "."]
}

 

 

# 星空智能對話機器人系列博客
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