词嵌入测验

当您参加测验时，就会出现答案和解释。

1. 学习到的词嵌入如何表示一个词以进行数值比较？
   • As a plain-text dictionary entry only
   • As a vector of learned numerical features
   • As a complete recorded pronunciation only
   • As a manually assigned grammar rule
2. 像 word2vec 这样的模型如何学习单词的含义？
   • 通过阅读字典中的手写定义
   • 通过计算每个单词中的字母
   • 通过观察在大量文本中哪些其他单词往往出现在它附近
   • 通过要求人工评分者对每个单词进行评分
3. 经典的“国王-男人+女人”向量示例说明了什么？
   • Some learned representations capture relationships through vector offsets
   • Every word relation obeys an exact arithmetic law
   • Vector arithmetic verifies every factual statement
   • The vector stores a word's exact spelling only
4. word2vec/GloVe 嵌入与现代 Transformer 模型内的嵌入之间的主要区别是什么？
   • 变形金刚根本不使用数字
   • GloVe 仅适用于图像
   • 现代模型无法代表意义
   • Word2vec 为每个单词提供一个固定向量，而 Transformer 则生成依赖于上下文的向量
5. 哪项任务最直接依赖于比较嵌入向量？
   • 计算文档中的总字数
   • 将文本转换为大写
   • 语义搜索，查找与查询具有相似含义的文档
   • 检查磁盘上文件的大小
6. Which scale of dimensionality is found in common released classic word-vector configurations?
   • Exactly one coordinate for every model
   • Tens or hundreds of learned coordinates per word
   • Exactly two coordinates in all word2vec models
   • At least ten million coordinates per word by definition
7. In a toy word-vector comparison, what is the cosine similarity of the nonzero vectors one-zero and zero-one?
   • 一
   • Minus one
   • 两个
   • 零
8. What should the first coordinate of an arbitrary learned word embedding be assumed to mean?
   • A learned feature without a universal predetermined linguistic label
   • The word's character count in every model
   • A fixed sentiment score in every dataset
   • The document's factual reliability by definition