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ISI Vs. ICT ISI ICT model string-to-tree tree-to-string phrasal compatibility full partial features 11 7 extraction algorithm top-down bottom-up unaligned words attachment multiple single decoding algorithm bottom-up CKY bottom-up beam search rule binarization yes no nbest derivation generation yes no nbest list generation yes no treat BPs as special rules no yes improve fluency using BPs no yes 机器翻译原理与方法 (07) 基于句法的机器翻译方法 262
Comparison Chiang Galley Marcu Liu model formal syntax string-to-tree string-to-tree tree-to-string tree annotations syntacticallymotivated phrasal compatibility enable discontinuous source phrases feature functions single-level multi-level multi-level multi-level no yes yes yes yes no yes no yes yes no yes 8 1 11 7 机器翻译原理与方法 (07) 基于句法的机器翻译方法 263
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机器翻译原理与方法
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概述 • 基于短语的统
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统计机器翻译方法的
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基于句法的统计机器
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基于句法的统计机器
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语言学上基于句法的
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同步语法 (1) • 定义 :
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同步语法 (2) 单语双语
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内容提要 • 概述 • 同
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反向转录语法 ITG rules S
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基于反向转录语法的
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基于反向转录语法的
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统计机器翻译中语序
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BTG 约束导致搜索空间
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BTG 的主要问题 • 两条
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ME-BTG 模型 • 模型 • 特
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抽取重排序实例 S1 S2
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为什么使用边界单词
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CKY 解码算法机器翻译
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基于 ME-BTG 模型的翻译
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基于 ME-BTG 模型的翻译
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ME-BTG: 实验 Systems NIST MT 05
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同步上下文无关语法
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类似的表示形式机器
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同步上下文关语法的
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层次短语模型 (2) • 传
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用层次短语进行翻译
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用层次短语进行翻译
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用同步语法表示层次
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约束 : 降低复杂度 •
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规则举例机器翻译原
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特殊规则 • 实际的翻
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模型特征机器翻译原
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解码 • 类似于句法分
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基于树的方法 Tree-to-Str
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基于树到串对齐模板
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Tree-based Translation ● Recursiv
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模型机器翻译原理与
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训练 • 数据 : 源语言
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Tree-to-String Rule Extraction ●
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Tree-based Button-up Decoding
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解码 : 构造缺省 TAT 如
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候选译文归并 ( Recombina
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训练过程句子对齐的
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实验结果 Comparison of Phara
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利用句法短语修正 TAT
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例子 TAT table NP NR NN NP 布
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采用句法短语修正 TAT
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两棵句法树 NP-B NP-B NN NN
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例子 IP NP-B VP PU NN NN NN ADVP
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例子 IP NP-B VP PU NN NN NN ADVP
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Training Data (pairs) TAT 加大
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基于森林的方法 Forest-
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Syntatic Ambiguity
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Packed Forest
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Translation Forest
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Translation Forest
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Translation Forest
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Forest as Virtual ∞-best List
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Forest-based Rule Extraction ● Co
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Forest-based Rule Extraction ● Ex
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Forest-based Rule Extraction ● Ex
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Rule Probabilities and Rule counts
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Fractional Count
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小结
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Seperate Parsing and Translation
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Joint Parsing and Translation
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Evaluation String-based Translation
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小结
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串到树翻译模型 • 串
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ISI 的工作 • Scalable Infere
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同步生成树、串和对
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同步生成树、串和对
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规则与推导机器翻译
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最小规则与组合规则
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• Galley 翻译模型定义
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非句法短语的处理办
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构造兼容规则 (2) NPB *NP
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模型特征 • Galley (1) - EM
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Galley 的规则抽取方法
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Some Notions • The span of a node
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An Example 灰色结点构成
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抽取规则 1 机器翻译
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抽取规则 3 机器翻译
Page 211 and 212: 抽取规则 5 机器翻译
Page 219 and 220: 唯一最小推导机器翻
Page 221 and 222: 未对齐词的处理办法
Page 223 and 224: 推导森林机器翻译原
Page 225 and 226: Marcu 的规则抽取算法
Page 231 and 232: Example 假设左部的对齐
Page 233 and 234: 高效的 EM 训练算法 •
Page 235 and 236: Example 枪手被警方击毙
Page 237 and 238: Example NP DT The NNS gunmen 枪
Page 239 and 240: Example NP DT NNS IN NN The gunmen
Page 241 and 242: Example NP NP DT NNS IN DT NN VBN t
Page 243 and 244: Example PP NP NP DT NNS IN DT NN VB
Page 245 and 246: Example VP VP-C PP NP NP DT NNS VBD
Page 247 and 248: 规则的二叉化 ( Binarizat
Page 249 and 250: 线性的二叉化算法 •
Page 251 and 252: Galley 所采用的特征 •
Page 253 and 254: 组合多个 SPMT 模型的
Page 255 and 256: 实验结果 : Galley Vs. Och Oc
Page 257 and 258: 实验结果 : Marcu Vs. Och 机
Page 259 and 260: 内容提要 • 概述 • 同
Page 261: Views on String-to-Tree Vs. Tree-to
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