More documents

Recommendations

Info

总结 • 同步语法 • 形式化基于句法的翻译模型 – 最大熵括号转录语法模型 – 层次化短语模型 • 语言学基于句法的翻译模型 – 树到串对齐模板模型 – ISI 的串到树模型机器翻译原理与方法 (07) 基于句法的机器翻译方法 260
Views on String-to-Tree Vs. Tree-to-String • Galley – The target language (i.e. English) has syntactic resources (parsers and treebanks) that are considerably more available than for the source language – There is less benefit in modeling the syntax of the source language, since the input sentence is fixed during decoding and is generally already grammatical • Liu – Source analysis may be important – Ill-formed source trees make the Tree-to-String decoder difficult to seek the “tr ue” translation. We can never expect good syntactically-motivated reordering under ill-formed source tree structures. In contrast, the input of String-to-Tree decoding is source string. The decoder may build a reasonable target tree with the he lp of language model even though the rules are learned from ill-formed target trees in training data. – Tree-to-String decoding is useful for translating resource-rich languages (e.g. En glish) into resource-poor languages (e.g. Inuktitut) 机器翻译原理与方法 (07) 基于句法的机器翻译方法 261
Page 1 and 2:
机器翻译原理与方法
Page 3 and 4:
概述 • 基于短语的统
Page 5 and 6:
统计机器翻译方法的
Page 7 and 8:
基于句法的统计机器
Page 9 and 10:
基于句法的统计机器
Page 11 and 12:
语言学上基于句法的
Page 13 and 14:
同步语法 (1) • 定义 :
Page 15 and 16:
同步语法 (2) 单语双语
Page 17 and 18:
内容提要 • 概述 • 同
Page 19 and 20:
反向转录语法 ITG rules S
Page 21 and 22:
基于反向转录语法的
Page 23 and 24:
基于反向转录语法的
Page 25 and 26:
统计机器翻译中语序
Page 27 and 28:
BTG 约束导致搜索空间
Page 29 and 30:
Page 31 and 32:
BTG 的主要问题 • 两条
Page 33 and 34:
ME-BTG 模型 • 模型 • 特
Page 35 and 36:
抽取重排序实例 S1 S2
Page 37 and 38:
为什么使用边界单词
Page 39 and 40:
CKY 解码算法机器翻译
Page 41 and 42:
基于 ME-BTG 模型的翻译
Page 43 and 44:
基于 ME-BTG 模型的翻译
Page 45 and 46:
ME-BTG: 实验 Systems NIST MT 05
Page 47 and 48:
同步上下文无关语法
Page 49 and 50:
Page 51 and 52:
Page 53 and 54:
Page 55 and 56:
类似的表示形式机器
Page 57 and 58:
同步上下文关语法的
Page 59 and 60:
Page 61 and 62:
Page 63 and 64:
Page 65 and 66:
层次短语模型 (2) • 传
Page 67 and 68:
用层次短语进行翻译
Page 69 and 70:
用层次短语进行翻译
Page 71 and 72:
用同步语法表示层次
Page 73 and 74:
约束 : 降低复杂度 •
Page 75 and 76:
规则举例机器翻译原
Page 77 and 78:
特殊规则 • 实际的翻
Page 79 and 80:
模型特征机器翻译原
Page 81 and 82:
解码 • 类似于句法分
Page 83 and 84:
Page 85 and 86:
基于树的方法 Tree-to-Str
Page 87 and 88:
基于树到串对齐模板
Page 89 and 90:
Tree-based Translation ● Recursiv
Page 91 and 92:
Page 93 and 94:
Page 95 and 96:
Page 97 and 98:
模型机器翻译原理与
Page 99 and 100:
训练 • 数据 : 源语言
Page 101 and 102:
Tree-to-String Rule Extraction ●
Page 103 and 104:
Page 105 and 106:
Page 107 and 108:
Tree-based Button-up Decoding
Page 109 and 110:
Page 111 and 112:
Page 113 and 114:
Page 115 and 116:
解码 : 构造缺省 TAT 如
Page 117 and 118:
候选译文归并 ( Recombina
Page 119 and 120:
训练过程句子对齐的
Page 121 and 122:
实验结果 Comparison of Phara
Page 123 and 124:
利用句法短语修正 TAT
Page 125 and 126:
例子 TAT table NP NR NN NP 布
Page 127 and 128:
采用句法短语修正 TAT
Page 129 and 130:
两棵句法树 NP-B NP-B NN NN
Page 131 and 132:
例子 IP NP-B VP PU NN NN NN ADVP
Page 133 and 134:
例子 IP NP-B VP PU NN NN NN ADVP
Page 135 and 136:
Training Data (pairs) TAT 加大
Page 137 and 138:
基于森林的方法 Forest-
Page 139 and 140:
Syntatic Ambiguity
Page 141 and 142:
Packed Forest
Page 143 and 144:
Translation Forest
Page 145 and 146:
Translation Forest
Page 147 and 148:
Translation Forest
Page 149 and 150:
Forest as Virtual ∞-best List
Page 151 and 152:
Forest-based Rule Extraction ● Co
Page 153 and 154:
Forest-based Rule Extraction ● Ex
Page 155 and 156:
Forest-based Rule Extraction ● Ex
Page 157 and 158:
Rule Probabilities and Rule counts
Page 159 and 160:
Fractional Count
Page 161 and 162:
小结
Page 163 and 164:
Seperate Parsing and Translation
Page 165 and 166:
Joint Parsing and Translation
Page 167 and 168:
Page 169 and 170:
Page 171 and 172:
Page 173 and 174:
Page 175 and 176:
Page 177 and 178:
Evaluation String-based Translation
Page 179 and 180:
小结
Page 181 and 182:
串到树翻译模型 • 串
Page 183 and 184:
ISI 的工作 • Scalable Infere
Page 185 and 186:
同步生成树、串和对
Page 187 and 188:
同步生成树、串和对
Page 189 and 190:
规则与推导机器翻译
Page 191 and 192:
最小规则与组合规则
Page 193 and 194:
• Galley 翻译模型定义
Page 195 and 196:
非句法短语的处理办
Page 197 and 198:
构造兼容规则 (2) NPB *NP
Page 199 and 200:
模型特征 • Galley (1) - EM
Page 201 and 202:
Galley 的规则抽取方法
Page 203 and 204:
Some Notions • The span of a node
Page 205 and 206:
An Example 灰色结点构成
Page 207 and 208:
抽取规则 1 机器翻译
Page 209 and 210: 抽取规则 3 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: 内容提要 • 概述 • 同
Page 263 and 264: Comparison Chiang Galley Marcu Liu
show all

æºå¨ç¿»è¯åçä¸æ¹æ³ - ä¸­ç§é¢è®¡ç®æèªç¶è¯­è¨å¤çç ç©¶ç»- ä¸­å½ç§å­¦é¢ ...

You also want an ePaper? Increase the reach of your titles

Delete template?

Save as template?

æºå¨ç¿»è¯åçä¸æ¹æ³ - ä¸ç§é¢è®¡ç®æèªç¶è¯è¨å¤çç ç©¶ç»- ä¸å½ç§å¦é¢ ...