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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 “true” 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 help 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. English) into resource-poor languages (e.g. Inuktitut) 机器翻译原理与方法 (05) 基于句法的机器翻译方法 260
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 (05) 基于句法的机器翻译方法 261
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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 (3) • 同步语
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反向转录语法 • Inversi
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反向转录语法机器翻
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基于反向转录语法的
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括号转录语法 • Bracket
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这里给出了四个词的
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真实自然语言的翻译
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基于最大熵括号转录
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ME-BTG : 基本思想 • 在 BT
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ME-BTG 训练单词对齐的
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重排序特征 • 单目特
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基于 ME-BTG 的统计机器
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基于 ME-BTG 模型的翻译
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内容提要 • 概述 • 同
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同步上下文无关语法
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同步上下文关语法的
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算法复杂度 • 机器翻
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层次短语模型 (1) • 层
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观察 : 短语的层次性
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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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层次短语的抽取机器
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非终结符标记 • 到目
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粘合规则 ( Glue Rules ) •
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模型 • 直接利用同步
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模型特征机器翻译原
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小结 • 形式上基于句
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树到串翻译模型 • 树
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基于树到串对齐模板
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树到串对齐模板 NP LCP
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Tree-based Translation ● Recursiv
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模型特征机器翻译原
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Tree-to-String Rule Extraction ●
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解码 • 自底向上 • 柱
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Tree-based Button-up Decoding
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Tree-based Button-up Decoding ● B
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剪枝策略 • 模板表剪
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实验 • Baseline : Pharaoh (Koe
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实验环境 • 评测工具
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与短语的兼容性 • TAT
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例子 TAT table NP NR NN NP 布
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例子 NP NR NN 布什总统 NP
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利用非句法双语短语
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解决方法 • 搜索结束
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例子 IP NP-B VP PU NN NN NN ADVP
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加大数据规模 • Bilingu
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小结 • 基于树到串对
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Parsing Mistake Propagation
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1-best ➜ n-best trees?
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Patten Matching on Forest
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Translation Forest
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Translation Forest
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N-best Trees vs. Forest
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Forest-based Rule Extraction ● Co
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Forest-based Rule Extraction ● Ex
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Fractional Count
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Results on forest training and deco
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基于串的方法 Joint Parsi
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Joint Parsing and Translation
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Search Space Comparison String-base
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内容提要 • 概述 • 同
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ISI 的工作 • Ulrich Germann,
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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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句法翻译概率表机器
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与短语模型的兼容性
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构造兼容规则 (1) *NPB*_N
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构造兼容规则 (3) • 兼
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训练 • 规则抽取 - Input
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将树到串对齐表示为
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一些概念定义 • 一个
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抽取规则算法 • Step 1:
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抽取规则 2 机器翻译
Page 209 and 210: 抽取规则 4 机器翻译
Page 219 and 220: 未对齐词导致的问题
Page 221 and 222: 源语言未对齐词的多
Page 223 and 224: 新的规则抽取算法 •
Page 225 and 226: Marcu 的规则抽取算法
Page 227 and 228: Marcu 的规则抽取算法
Page 229 and 230: 估计规则概率按规则
Page 231 and 232: EM 训练算法可以采用
Page 233 and 234: 解码 • 采用自底向上
Page 235 and 236: Example NNS gunmen 枪手被警
Page 237 and 238: Example NP DT The NNS gunmen by IN
Page 239 and 240: Example NP NP DT NNS IN DT NN the g
Page 241 and 242: Example NP NP DT NNS IN DT NN VBN .
Page 243 and 244: Example VP-C PP NP NP DT NNS VBN IN
Page 245 and 246: Example S VP VP-C PP NP NP DT NNS V
Page 247 and 248: 规则的二叉化 ( Binarizat
Page 249 and 250: Galley 的实验 : 不同规
Page 251 and 252: Marcu 的实验 : SPMT Models •
Page 253 and 254: Marcu 采用的特征 • proot
Page 255 and 256: 实验结果 : Marcu Vs. Och 机
Page 257 and 258: Analysis • Galley - Good results
Page 259: 总结 • 同步语法 • 形
Page 263: 讨论机器翻译原理与
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