Improving Trees and Alignments for Syntax- Based Machine - PowerPoint PPT Presentation

Improving Trees and Alignments for Syntax- Based Machine Translation Kevin Knight USC/Information Sciences Institute joint work with Steven DeNeefe, Daniel Marcu, Wei Wang, and Jonathan May SRI, July 12, 2007

Syntactic Approaches to MT • Use of syntactic information (noun, verb, etc) in the translation process: – Manually constructed rule-based systems – Statistical systems • Wu & Wong, 1998 • Yamada & Knight, 2001-2002 • Galley et al, 2004 – Contrast with phrase-based statistical approaches

Phrase-Based Output . 被枪手警方击毙 Decoder Gunman of police killed . Hypothesis #1

Phrase-Based Output . 被枪手警方击毙 Decoder Gunman of police attack . Hypothesis #7

Phrase-Based Output . 被枪手警方击毙 Decoder Gunman by police killed . Hypothesis #12

Phrase-Based Output . 被枪手警方击毙 Decoder Killed gunman by police . Hypothesis #134

Phrase-Based Output . 被枪手警方击毙 Decoder Gunman killed the police . Hypothesis #9,329

Phrase-Based Output . 被枪手警方击毙 Decoder Gunman killed by police . Hypothesis #50,654 Problematic – - Output lacks English auxiliary and determiner - Re-ordering relies on luck, instead of on Chinese passive marker

Syntax-Based Output . 被枪手警方击毙 Decoder The gunman killed by police . Hypothesis #1 DT NN VBD IN NN NPB PP NP-C VP S

Syntax-Based Output . 被枪手警方击毙 Decoder Gunman by police shot . Hypothesis #16 NN IN NN VBD NPB PP NP-C VP S

Syntax-Based Output . 被枪手警方击毙 Decoder The gunman was killed by police . Hypothesis #1923 DT NN AUX VBN IN NN NPB PP NP-C VP S

Why Might Syntax Help? • Phrase-based MT output is “n-grammatical”, not grammatical – Every sentence needs a subject and a verb • Re-ordering is poorly explained as “distortion” -- better explained as syntactic transformation – Arabic to English, VSO � SVO • Function words have syntactic effects even if they are not themselves translated

Why Might Syntax Hurt? • Less freedom to glue available phrase-based pieces of output translations together -- search space has fewer output strings • Search space is more difficult to navigate • Rule extraction from bilingual text has limitations this talk

Why Might Syntax Hurt? • Less freedom to glue available phrase-based pieces of output translations together -- search space has fewer output strings • Search space is more available syntax-based difficult to navigate translations • Rule extraction from bilingual text has limitations this talk

Why Might Syntax Hurt? • Less freedom to glue available phrase-based pieces of output translations together -- search space has fewer output strings • Search space is more available syntax-based difficult to navigate translations • Rule extraction from bilingual text has limitations

Comparing Phrase-Based Extraction with Syntax-Based Extraction • Quantitatively compare – A typical phrase-based bilingual extraction algorithm ( ATS , Och & Ney 2004) – A typical syntax-based bilingual extraction algorithm ( GHKM , Galley et al 2004) – These algorithms picked from two good- scoring NIST-06 systems • Identify areas of improvement for syntax- based rule coverage

Phrase-Based and Syntax-Based Pattern Extraction estring … alignment cstring ATS [Och & Ney, 2004] phrase pairs consistent with word alignment etree … alignment cstring GHKM [Galley et al 2004] syntax transformation rules consistent with word alignment

ATS (Och & Ney, 2004) PHRASE PAIRS ACQUIRED: � 有 felt � 有责任 felt obliged felt obliged to do � 有责任尽 � 责任 obliged i felt obliged to do my part � 责任尽 obliged to do � 尽 do � 一份 part � 一份力 part 我有责任尽一份力

GHKM (Galley et al, 2004) RULES ACQUIRED: S NP-C VP � 有 VP-C VBD(felt) VBD SG-C � 责任 VP VBN(obliged) VBN VP(x0:VBD TO VP-C VB NP-C VP-C(x1:VBN x2:SG-C) � x0 x1 x2 NPB NPB PRP PRP$ NN VP(VBD(felt) VP-C(VBN(obliged)) i felt obliged to do my part x0:SG-C) � 有责任 x0 S(x0:NP-C x1:VP) � x0 x1 我有责任尽一份力

GHKM (Galley et al, 2004) RULES ACQUIRED: S NP-C VP � 有 VP-C VBD(felt) VBD SG-C � 责任 VP VBN(obliged) VBN VP(x0:VBD TO VP-C VB NP-C VP-C(x1:VBN x2:SG-C) � x0 x1 x2 NPB NPB VP(VBD(felt) PRP PRP$ NN VP-C(VBN(obliged)) i felt obliged to do my part x0:SG-C) � 责任 x0 � 有 � � 有有责任有责任责任 S(x0:NP-C x1:VP) � x0 x1 我有责任尽一份力 minimal rules tile the tree/string/alignment triple. composed rules are made by combining those tiles.

GHKM Syntax Rules Phrasal Translation Non-constituent Phrases Non-contiguous Phrases hay , NP S poner , NP está , cantando VP VP PRO VP VB NP PRT VBZ VBG there VB NP put on is singing are Context-Sensitive Multilevel Re-Ordering Lexicalized Re-Ordering Word Insertion NP S NP1, , NP2 VB, NP1, NP2 NPB NNS NP2 PP NP1 VP DT NNS P NP1 VB NP2 the of

Improving Trees and Alignments for Syntax- Based Machine - PowerPoint PPT Presentation

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