【文本生成】评价指标：BERTScore
BERTScore
这是2020ICLR由Cornell University提出来的论文：BERTSCORE: EVALUATING TEXT GENERATION WITH BERT
主要是基于BERT预训练模型，使用contextual embedding来描述句子，计算两个句子之间的余弦相似度。
基于n-gram matching metric 的常见缺陷：
-
semantically-correct phrases are penalized because they differ from the surface form of the reference.
解决： In contrast to string matching (e.g., in BLEU) or matching heuristics (e.g., in METEOR), we compute similarity using contextualized token embeddings, which have been shown to be effective for paraphrase detection
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n-gram models fail to capture distant dependencies and penalize semantically-critical ordering changes.
解决： contextualized embeddings are trained to effectively capture distant dependencies and ordering
实验结果：
（1）In machine translation, BERTSCORE shows stronger system-level and segment-level correlations
with human judgments than existing metrics on multiple common benchmarks.
（2）BERTSCORE is well-correlated with human annotators for image captioning, surpassing SPICE.
直接下载预训练模型，运用eval方式输入batch sentence，输出cls的embedding，对sentence_can和sentence_ref做相似度矩阵和idf计算，用greedy方式选取最大的相似度，乘以该词的idf，输出BERTScore
论文中还用了+1 smoothing和rescaling来微调BERTScore
结论：在机器翻译常用F_BERT作为指标；在评估英文生成时推荐使用 24-layer RoBERTa large model；多语言任务推荐multilingual BERT，中文自动选择的是"bert-base-chinese"。
代码实例：非常简单！
!pip install bert_score
from bert_score import score
# data
cands = ['我们都曾经年轻过']
refs = ['虽然我们都年少，但还是懂事的']
P, R, F1 = score(cands, refs, lang="zh", verbose=True)
print(f"System level F1 score: {F1.mean():.3f}")
# System level F1 score: 0.959
其中P，R，F1的计算：
def greedy_cos_idf(ref_embedding, ref_masks, ref_idf, hyp_embedding, hyp_masks, hyp_idf, all_layers=False):
"""
Compute greedy matching based on cosine similarity.
"""
……
batch_size = ref_embedding.size(0)
sim = torch.bmm(hyp_embedding, ref_embedding.transpose(1, 2))
masks = torch.bmm(hyp_masks.unsqueeze(2).float(), ref_masks.unsqueeze(1).float())
masks = masks.float().to(sim.device)
sim = sim * masks
word_precision = sim.max(dim=2)[0]
word_recall = sim.max(dim=1)[0]
hyp_idf.div_(hyp_idf.sum(dim=1, keepdim=True))
ref_idf.div_(ref_idf.sum(dim=1, keepdim=True))
precision_scale = hyp_idf.to(word_precision.device)
recall_scale = ref_idf.to(word_recall.device)
P = (word_precision * precision_scale).sum(dim=1)
R = (word_recall * recall_scale).sum(dim=1)
F = 2 * P * R / (P + R)
……
return P, R, F
如果需要相似度矩阵图形：
import matplotlib.pyplot as plt
font = {'family': 'SimHei', 'size':'10'}
plt.rc('font', **font)
from bert_score import plot_example
cand = cands[0]
ref = refs[0]
plot_example(cand, ref, lang="zh")
# bert以字划分中文，这里不显示中文是因为colab服务器上没有中文字体
github地址：https://github.com/Tiiiger/bert_score/blob/3227a18cb2a546978a00beb94bf138fd72fef8cf/bert_score/score.py