Improving Listening Skill: 10 Ways to Better Listening

本文提供了10种有效提升倾听技能的方法,包括不打断说话者、保持主题连贯、复述理解、全神贯注听讲、维持眼神交流、避免过早下结论、控制情绪反应、提问获取更多信息、提出深入问题以及遵循黄金法则,即像希望被倾听一样倾听他人。
At one point or another you've probably heard the following "You were given two ears and one mouth for a reason; to listen twice as much as you speak." How many of us actually listen twice as much as we speak?
We are so busy thinking about what we are going to say next that we don't even hear everything a speaker says. We use a speaker's comments as a spring board to jump to what we want to talk about. We listen at 125-250 words per minute, but think at 1000-3000 words per minute.
Conversation: a vocal competition in which the one who is catching his breath is called the listener. -- Anonymous
So, how do we improve our listening skills? Here are 10 ways to be a better listener.
10 Ways to Be a Better Listener
1. Don't interrupt the speaker.
2. Don't change the subject in the middle of a conversation. Make sure that subject is finished before moving on.
3. Check your understanding by paraphrasing what the speaker said. So if I understand you correctly, you said ............
4. Pay full attention to the speaker.
5. Maintain eye contact with the speaker.
6. Don't jump to conclusions before the speaker is finished speaking.
7. Watch for emotional reactions and keep them in check.
8. Don't say: That reminds me. . . or That's nothing, let me tell you about... Listening is not thinking about one-upping the speaker.
9. Ask probing questions to gain understanding.
10. Remember the golden rule, it applies here too. Listen to others as you would like to have them listen to you.
### 改进Transformer模型优化技术的方法 #### 更好的初始化方法概述 为了提升Transformer模型的优化效果,研究者提出了一种新的参数初始化方案[^1]。这种新方法允许模型无需依赖预热(warm-up)策略以及层归一化(Layer Normalization, LN),并能有效训练非常深的网络结构。 #### Adam优化器中的挑战与解决方案 传统的Adam优化算法基于滑动平均机制来估算参数的一阶矩和二阶矩。然而,在处理复杂如Transformer这样的大型架构时,这种方法可能导致更新不稳定。因此,文中建议控制\(v_t\)(即梯度平方的指数加权移动平均值)的变化范围,以此减小每次权重调整间的差异程度,进而提高收敛稳定性。 具体来说,对于每一层 \( f \),目标函数定义如下: \[ \min_{w} E[\|f(w)-y\|^2] \] 其中 \( w \) 表示待学习的参数向量;而 \( y \) 是期望输出。通过对上述表达式的泰勒级数近似展开,可以获得更易于求解的形式。 ```python import torch.nn as nn class ImprovedInitModel(nn.Module): def __init__(self): super(ImprovedInitModel, self).__init__() # 使用特定方式初始化各层参数 for m in self.modules(): if isinstance(m, (nn.Conv2d, nn.Linear)): nn.init.xavier_uniform_(m.weight) def train_model(model, optimizer, criterion, data_loader): model.train() for inputs, targets in data_loader: outputs = model(inputs) loss = criterion(outputs, targets) optimizer.zero_grad() loss.backward() optimizer.step() # 创建改进后的Transformer实例 model = ImprovedInitModel() # 定义损失函数和优化器 criterion = ... optimizer = ... # 开始训练循环 train_model(model, optimizer, criterion, ...) ``` 此代码片段展示了如何应用Xavier均匀分布来进行神经元连接权重的初始赋值操作,这是实现更好初始化的一种常见实践手段之一。当然,实际项目中可能还需要考虑更多细节配置以适应不同应用场景下的需求。
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