为SciCo开发的Longformer模型

该模型是论文 SciCo: Hierarchical Cross-Document Coreference for Scientific Concepts (AKBC 2021) 中讨论的统一模型，将层次交叉文档共指消解（H-CDCR）任务作为一个多类问题进行了建模。该模型以两个提及m1和m2及其相应的上下文作为输入，并输出4个分数：

• 0: 无关
• 1: m1和m2共指
• 2: m1是m2的父节点
• 3: m1是m2的子节点

我们提供以下代码作为将全局注意力集中在特殊标记，和上的示例:

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

tokenizer = AutoTokenizer.from_pretrained('allenai/longformer-scico')
model = AutoModelForSequenceClassification.from_pretrained('allenai/longformer-scico')

start_token = tokenizer.convert_tokens_to_ids("<m>")
end_token = tokenizer.convert_tokens_to_ids("</m>")

def get_global_attention(input_ids):
    global_attention_mask = torch.zeros(input_ids.shape)
    global_attention_mask[:, 0] = 1  # global attention to the CLS token
    start = torch.nonzero(input_ids == start_token) # global attention to the <m> token
    end = torch.nonzero(input_ids == end_token) # global attention to the </m> token
    globs = torch.cat((start, end))
    value = torch.ones(globs.shape[0])
    global_attention_mask.index_put_(tuple(globs.t()), value)
    return global_attention_mask
    
m1 = "In this paper we present the results of an experiment in <m> automatic concept and definition extraction </m> from written sources of law using relatively simple natural methods."
m2 = "This task is important since many natural language processing (NLP) problems, such as <m> information extraction </m>, summarization and dialogue."

inputs = m1 + " </s></s> " + m2  

tokens = tokenizer(inputs, return_tensors='pt')
global_attention_mask = get_global_attention(tokens['input_ids'])

with torch.no_grad():
    output = model(tokens['input_ids'], tokens['attention_mask'], global_attention_mask)
    
scores = torch.softmax(output.logits, dim=-1)
# tensor([[0.0818, 0.0023, 0.0019, 0.9139]]) -- m1 is a child of m2

注意：这个模型和 paper 中提到的原始模型之间有细微差别。原始模型在标记（相当于[CLS]）之上包括一个单一线性层，而这个模型包括一个两层的多层感知机（MLP），以便与LongformerForSequenceClassification保持一致。原始代码库可以在 here 中找到。

引用

@inproceedings{
    cattan2021scico,
    title={SciCo: Hierarchical Cross-Document Coreference for Scientific Concepts},
    author={Arie Cattan and Sophie Johnson and Daniel S Weld and Ido Dagan and Iz Beltagy and Doug Downey and Tom Hope},
    booktitle={3rd Conference on Automated Knowledge Base Construction},
    year={2021},
    url={https://openreview.net/forum?id=OFLbgUP04nC}
}