Comments (5)
我没有特别理解你的意思,但是感觉你的“现在的”理解是对的。
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谢谢回复,我换一种表达方式:
- 系统一在访问节点x时的输入:
[CLS] Question [SEP] clues[x,G] [SEP] Para[x]
Sentence A Sentence B - 系统一Bert的输出向量表示为:T
(问题)但是其中出现了sem[x,Q,clues], 这个的作用是什么?
同样在系統2中也出现了对sem[x,Q,clues]的表述,在论文3.1节和3.2节。
原文的表述如下:
- (3.1) It is worth noting that for answer node x, Para[x] is probably missing. Thus we do not extract spans but can still calculate sem[x,Q,clues] based on "Sentence A"part.
- (3.2) To fully understand the relation between an entity x and the question Q,barely analyzing semantics sem[x,Q,clues] is insufficient.
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系统一的输出不是T,而是抽取的span(entities/ answer candidates)和 sem[x, Q, clues]。后者被用作GNN的feature。
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收到,谢谢解答~!我还有个问题:bert的输出不是向量吗?但是这个抽取的span看上去更像是一个索引(以标记候选实体),而sem貌似是一个向量组合,所以对"输出的是抽取的span(entities/ answer candidates)和 sem[x, Q, clues]", 还有些疑惑
(有些小白,谢谢答疑~)
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bert直接输出是向量,但是在下游finetune的时候一般需要加一层相关的输出层,比如mrc就是加这样的span prediction的层,可以看一下bert文章和cogqa的图。
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Related Issues (20)
- dump.rdb HOT 4
- RuntimeError: CUDA error: an illegal memory access was encountered HOT 12
- fullwiki data HOT 1
- 关于bert使用不加载链接,而是通过文件加载的问题 HOT 1
- 请问下"hotpot_dev_fullwiki_v1_merge.json"文件从哪来? HOT 2
- 论文中"提取一跳节点,但是不计算语义向量"的含义 HOT 2
- 数据集中的答案并不是来源于该条数据的材料--问题 HOT 1
- 代码中utils中的GENERAL_WD解释 HOT 1
- 不好意思一次性问了很多细节问题,希望您有时间可以解答下 HOT 1
- where can i find hotpot_train_v1.1_refined.json HOT 1
- NEG TOO LONG! HOT 1
- 将bert换成albert时,加载输入数据时出了个错误 HOT 2
- model2 = CognitiveGNN(model1.config.hidden_size) 'NoneType' object has no attribute 'config'
- model1 = BertForMultiHopQuestionAnswering.from_pretrained(BERT_MODEL, cache_dir=PYTORCH_PRETRAINED_BERT_CACHE / 'distributed_{}'.format(-1)) HOT 2
- 请问训练速度和什么有关呀?显卡?内存?
- Where can i get `hotpot_train_v1.1.json`? HOT 1
- 文件夹里enwiki-20171001-pages-meta-current-withlinks-abstracts应该有什么文件?
- System 1 with roberta?
- 感觉这代码没法复现
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