我试图训练一个单词嵌入到一个只有主语改变的重复句子列表中。我期望被试能像预期的那样在单词嵌入中提供很强的相关性。然而，主语向量之间的夹角并不总是大于主语与随机词之间的夹角。

Man is going to write a very long novel that no one can read.
Woman is going to write a very long novel that no one can read.
Boy is going to write a very long novel that no one can read.

代码基于pytorch教程：

import torch
from torch import nn
import torch.nn.functional as F
import numpy as np

class EmbedTrainer(nn.Module):
    def __init__(self, d_vocab, d_embed, d_context):
        super(EmbedTrainer, self).__init__()
        self.embed = nn.Embedding(d_vocab, d_embed)
        self.fc_1 = nn.Linear(d_embed * d_context, 128)
        self.fc_2 = nn.Linear(128, d_vocab)

    def forward(self, x):
        x = self.embed(x).view((1, -1)) # flatten after embedding
        x = self.fc_2(F.relu(self.fc_1(x)))
        x = F.log_softmax(x, dim=1)
        return x

text = " ".join(["{} is going to write a very long novel that no one can read.".format(x) for x in ["Man", "Woman", "Boy"]])
text_split = text.split()
trigrams = [([text_split[i], text_split[i+1]], text_split[i+2]) for i in range(len(text_split)-2)]
dic = list(set(text.split()))
tok_to_ids = {w:i for i, w in enumerate(dic)}
tokens_text = text.split(" ")
d_vocab, d_embed, d_context = len(dic), 10, 2

""" Train """
loss_func = nn.NLLLoss()
model = EmbedTrainer(d_vocab, d_embed, d_context)
print(model)
optimizer = torch.optim.SGD(model.parameters(), lr=0.001)

losses = []
epochs = 10
for epoch in range(epochs):
    total_loss = 0
    for input, target in trigrams:
        tok_ids = torch.tensor([tok_to_ids[tok] for tok in input], dtype=torch.long)
        target_id = torch.tensor([tok_to_ids[target]], dtype=torch.long)
        model.zero_grad()
        log_prob = model(tok_ids)
        #if total_loss == 0: print("train ", log_prob, target_id)
        loss = loss_func(log_prob, target_id)
        total_loss += loss.item()
        loss.backward()
        optimizer.step()
    print(total_loss)
    losses.append(total_loss)

embed_map = {}
for word in ["Man", "Woman", "Boy", "novel"]:
    embed_map[word] = model.embed.weight[tok_to_ids[word]]
    print(word, embed_map[word])

def angle(a, b):
    from numpy.linalg import norm
    a, b = a.detach().numpy(), b.detach().numpy()
    return np.dot(a, b) / norm(a) / norm(b)

print("man.woman", angle(embed_map["Man"], embed_map["Woman"]))
print("man.novel", angle(embed_map["Man"], embed_map["novel"]))