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(Semantic Role Labeling)
# 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
以下句子看似簡單，但包含幾個動詞：
"Mrs. and Mr. Tomaso went to Europe for vacation and visited Paris and first went to visit the Eiffel Tower."這個令人困惑的句子會讓Transformer 猶豫嗎？我們運行SRL.ipynb的第2個示例：
!echo '{"sentence": "Mrs. and Mr. Tomaso went to Europe for vacation and visited Paris and first went to visit the Eiffel Tower."}' | \
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 09:08:12,622 - INFO - transformers.file_utils - PyTorch version 1.5.1 available.
2020-12-20 09:08:12.774532: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.10.1
2020-12-20 09:08:14,547 - INFO - transformers.file_utils - TensorFlow version 2.4.0 available.
2020-12-20 09:08:15,761 - 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 09:08:15,761 - INFO - allennlp.common.file_utils - waiting to acquire lock on /root/.allennlp/cache/e20d5b792a8d456a1a61da245d1856d4b7778efe69ac3c30759af61940aa0f42.f72523a9682cb1f5ad3ecf834075fe53a1c25a6bcbf4b40c11e13b7f426a4724
2020-12-20 09:08:15,762 - INFO - filelock - Lock 139888470380440 acquired on /root/.allennlp/cache/e20d5b792a8d456a1a61da245d1856d4b7778efe69ac3c30759af61940aa0f42.f72523a9682cb1f5ad3ecf834075fe53a1c25a6bcbf4b40c11e13b7f426a4724.lock
2020-12-20 09:08:15,763 - 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 09:08:15,763 - INFO - filelock - Lock 139888470380440 released on /root/.allennlp/cache/e20d5b792a8d456a1a61da245d1856d4b7778efe69ac3c30759af61940aa0f42.f72523a9682cb1f5ad3ecf834075fe53a1c25a6bcbf4b40c11e13b7f426a4724.lock
2020-12-20 09:08:15,763 - 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 09:08:15,763 - INFO - allennlp.models.archival - extracting archive file /root/.allennlp/cache/e20d5b792a8d456a1a61da245d1856d4b7778efe69ac3c30759af61940aa0f42.f72523a9682cb1f5ad3ecf834075fe53a1c25a6bcbf4b40c11e13b7f426a4724 to temp dir /tmp/tmp7jeuj77a
2020-12-20 09:08:19,975 - INFO - allennlp.common.params - type = from_instances
2020-12-20 09:08:19,976 - INFO - allennlp.data.vocabulary - Loading token dictionary from /tmp/tmp7jeuj77a/vocabulary.
2020-12-20 09:08:19,976 - INFO - filelock - Lock 139888468747992 acquired on /tmp/tmp7jeuj77a/vocabulary/.lock
2020-12-20 09:08:20,002 - INFO - filelock - Lock 139888468747992 released on /tmp/tmp7jeuj77a/vocabulary/.lock
2020-12-20 09:08:20,003 - INFO - allennlp.common.params - model.type = srl_bert
2020-12-20 09:08:20,004 - INFO - allennlp.common.params - model.regularizer = None
2020-12-20 09:08:20,004 - INFO - allennlp.common.params - model.bert_model = bert-base-uncased
2020-12-20 09:08:20,004 - INFO - allennlp.common.params - model.embedding_dropout = 0.1
2020-12-20 09:08:20,004 - INFO - allennlp.common.params - model.initializer = <allennlp.nn.initializers.InitializerApplicator object at 0x7f3a52797748>
2020-12-20 09:08:20,004 - INFO - allennlp.common.params - model.label_smoothing = None
2020-12-20 09:08:20,004 - INFO - allennlp.common.params - model.ignore_span_metric = False
2020-12-20 09:08:20,004 - 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 09:08:20,298 - 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 09:08:20,298 - 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 09:08:20,492 - 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 09:08:23,170 - INFO - allennlp.nn.initializers - Initializing parameters
2020-12-20 09:08:23,171 - INFO - allennlp.nn.initializers - Done initializing parameters; the following parameters are using their default initialization from their code
2020-12-20 09:08:23,171 - INFO - allennlp.nn.initializers - bert_model.embeddings.LayerNorm.bias
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2020-12-20 09:08:23,173 - INFO - allennlp.nn.initializers - bert_model.encoder.layer.0.output.dense.bias
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2020-12-20 09:08:23,178 - INFO - allennlp.nn.initializers - bert_model.encoder.layer.6.attention.output.LayerNorm.bias
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2020-12-20 09:08:23,179 - INFO - allennlp.nn.initializers - bert_model.encoder.layer.6.attention.self.key.bias
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2020-12-20 09:08:23,180 - INFO - allennlp.nn.initializers - bert_model.encoder.layer.7.attention.output.LayerNorm.bias
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2020-12-20 09:08:23,209 - INFO - allennlp.nn.initializers - bert_model.encoder.layer.7.intermediate.dense.bias
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2020-12-20 09:08:23,210 - INFO - allennlp.nn.initializers - bert_model.encoder.layer.8.attention.self.key.bias
2020-12-20 09:08:23,210 - INFO - allennlp.nn.initializers - bert_model.encoder.layer.8.attention.self.key.weight
2020-12-20 09:08:23,210 - INFO - allennlp.nn.initializers - bert_model.encoder.layer.8.attention.self.query.bias
2020-12-20 09:08:23,210 - INFO - allennlp.nn.initializers - bert_model.encoder.layer.8.attention.self.query.weight
2020-12-20 09:08:23,210 - INFO - allennlp.nn.initializers - bert_model.encoder.layer.8.attention.self.value.bias
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2020-12-20 09:08:23,210 - INFO - allennlp.nn.initializers - bert_model.encoder.layer.8.intermediate.dense.bias
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2020-12-20 09:08:23,210 - INFO - allennlp.nn.initializers - bert_model.encoder.layer.9.attention.output.LayerNorm.bias
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2020-12-20 09:08:23,211 - INFO - allennlp.nn.initializers - bert_model.encoder.layer.9.attention.output.dense.bias
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2020-12-20 09:08:23,211 - INFO - allennlp.nn.initializers - bert_model.encoder.layer.9.intermediate.dense.bias
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2020-12-20 09:08:23,211 - INFO - allennlp.nn.initializers - bert_model.pooler.dense.bias
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2020-12-20 09:08:23,211 - INFO - allennlp.nn.initializers - tag_projection_layer.bias
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2020-12-20 09:08:23,664 - INFO - allennlp.common.params - dataset_reader.type = srl
2020-12-20 09:08:23,664 - INFO - allennlp.common.params - dataset_reader.lazy = False
2020-12-20 09:08:23,665 - INFO - allennlp.common.params - dataset_reader.cache_directory = None
2020-12-20 09:08:23,665 - INFO - allennlp.common.params - dataset_reader.max_instances = None
2020-12-20 09:08:23,665 - INFO - allennlp.common.params - dataset_reader.manual_distributed_sharding = False
2020-12-20 09:08:23,665 - INFO - allennlp.common.params - dataset_reader.manual_multi_process_sharding = False
2020-12-20 09:08:23,665 - INFO - allennlp.common.params - dataset_reader.token_indexers = None
2020-12-20 09:08:23,665 - INFO - allennlp.common.params - dataset_reader.domain_identifier = None
2020-12-20 09:08:23,665 - INFO - allennlp.common.params - dataset_reader.bert_model_name = bert-base-uncased
2020-12-20 09:08:23,987 - 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": "Mrs. and Mr. Tomaso went to Europe for vacation and visited Paris and first went to visit the Eiffel Tower."}
prediction: {"verbs": [{"verb": "went", "description": "[ARG0: Mrs. and Mr. Tomaso] [V: went] [ARG4: to Europe] [ARGM-PRP: for vacation] and visited Paris and first went to visit the Eiffel Tower .", "tags": ["B-ARG0", "I-ARG0", "I-ARG0", "I-ARG0", "B-V", "B-ARG4", "I-ARG4", "B-ARGM-PRP", "I-ARGM-PRP", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"verb": "visited", "description": "[ARG0: Mrs. and Mr. Tomaso] went to Europe for vacation and [V: visited] [ARG1: Paris] and first went to visit the Eiffel Tower .", "tags": ["B-ARG0", "I-ARG0", "I-ARG0", "I-ARG0", "O", "O", "O", "O", "O", "O", "B-V", "B-ARG1", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"verb": "went", "description": "[ARG0: Mrs. and Mr. Tomaso] went to Europe for vacation and visited Paris and [ARGM-TMP: first] [V: went] [ARGM-PRP: to visit the Eiffel Tower] .", "tags": ["B-ARG0", "I-ARG0", "I-ARG0", "I-ARG0", "O", "O", "O", "O", "O", "O", "O", "O", "O", "B-ARGM-TMP", "B-V", "B-ARGM-PRP", "I-ARGM-PRP", "I-ARGM-PRP", "I-ARGM-PRP", "I-ARGM-PRP", "O"]}, {"verb": "visit", "description": "[ARG0: Mrs. and Mr. Tomaso] went to Europe for vacation and visited Paris and first went to [V: visit] [ARG1: the Eiffel Tower] .", "tags": ["B-ARG0", "I-ARG0", "I-ARG0", "I-ARG0", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "B-V", "B-ARG1", "I-ARG1", "I-ARG1", "O"]}], "words": ["Mrs.", "and", "Mr.", "Tomaso", "went", "to", "Europe", "for", "vacation", "and", "visited", "Paris", "and", "first", "went", "to", "visit", "the", "Eiffel", "Tower", "."]}
2020-12-20 09:08:25,342 - INFO - allennlp.models.archival - removing temporary unarchived model dir at /tmp/tmp7jeuj77a輸出摘錄表明transformer 正確識別了句子中的動詞：
prediction: {
"verbs": [{
"verb": "went",
"description": "[ARG0: Mrs. and Mr. Tomaso] [V: went] [ARG4: to Europe] [ARGM-PRP: for vacation] and visited Paris and first went to visit the Eiffel Tower .",
"tags": ["B-ARG0", "I-ARG0", "I-ARG0", "I-ARG0", "B-V", "B-ARG4", "I-ARG4", "B-ARGM-PRP", "I-ARGM-PRP", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]
} 在AllenNLP 運行示例表明，一個參數被識別為行程的目標：
我們可以解釋動詞went的參數，然而，transformer發現動詞的修飾語是旅行的目的，Shi和Lin(2019)只是建立了一個簡單的BERT模型來獲得這一高質量的語法分析，這一結果也就不足為奇了。我們還可以注意到，“went”與“Europe”的聯系是正確的。transformer正確地識別出動詞“visit”與“Paris”相關:
transformer 可以將動詞“visited”直接與“Eiffel Tower”聯系起來，但它堅持自己的立場，做出了正確的決定。我們要求transformer 做的最終一個任務是識別動詞“went”的第二次使用的上下文。同樣，它沒有陷入將所有與動詞went相關的參數合並的陷阱，went在句子中使用了兩次，它再次正確地分割了序列，並產生了一個出色的結果:
動詞“went”用了兩次，但transformer沒有落入陷阱，它甚至發現“first”是動詞“went”的時間修飾語。AllenNLP在線界面的格式化文本輸出匯總了本示例獲得的優秀結果:
input 0: {
"sentence": "Mrs. and Mr. Tomaso went to Europe for vacation and visited Paris and first went to visit the Eiffel Tower."
}
prediction: {
"verbs": [{
"verb": "went",
"description": "[ARG0: Mrs. and Mr. Tomaso] [V: went] [ARG4: to Europe] [ARGM-PRP: for vacation] and visited Paris and first went to visit the Eiffel Tower .",
"tags": ["B-ARG0", "I-ARG0", "I-ARG0", "I-ARG0", "B-V", "B-ARG4", "I-ARG4", "B-ARGM-PRP", "I-ARGM-PRP", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]
}, {
"verb": "visited",
"description": "[ARG0: Mrs. and Mr. Tomaso] went to Europe for vacation and [V: visited] [ARG1: Paris] and first went to visit the Eiffel Tower .",
"tags": ["B-ARG0", "I-ARG0", "I-ARG0", "I-ARG0", "O", "O", "O", "O", "O", "O", "B-V", "B-ARG1", "O", "O", "O", "O", "O", "O", "O", "O", "O"]
}, {
"verb": "went",
"description": "[ARG0: Mrs. and Mr. Tomaso] went to Europe for vacation and visited Paris and [ARGM-TMP: first] [V: went] [ARGM-PRP: to visit the Eiffel Tower] .",
"tags": ["B-ARG0", "I-ARG0", "I-ARG0", "I-ARG0", "O", "O", "O", "O", "O", "O", "O", "O", "O", "B-ARGM-TMP", "B-V", "B-ARGM-PRP", "I-ARGM-PRP", "I-ARGM-PRP", "I-ARGM-PRP", "I-ARGM-PRP", "O"]
}, {
"verb": "visit",
"description": "[ARG0: Mrs. and Mr. Tomaso] went to Europe for vacation and visited Paris and first went to [V: visit] [ARG1: the Eiffel Tower] .",
"tags": ["B-ARG0", "I-ARG0", "I-ARG0", "I-ARG0", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "B-V", "B-ARG1", "I-ARG1", "I-ARG1", "O"]
}],
"words": ["Mrs.", "and", "Mr.", "Tomaso", "went", "to", "Europe", "for", "vacation", "and", "visited", "Paris", "and", "first", "went", "to", "visit", "the", "Eiffel", "Tower", "."]
}
