[ADJ NP1] ((X3::Y1) (X1::Y2) ((X1 def) = +) ((X1 status) =c absolute) ((X1 num) = (X3 num)) ((X1 gen) = (X3 gen)) (X0 = X1)) Translation Lexicon N::N |: ["$WR"] -> ["BULL"] ((X1::Y1) ((X0 NUM) = s) ((Y0 lex) = "BULL")) N::N |: ["$WRH"] -> ["LINE"] ((X1::Y1) ((X0 NUM) = s) ((Y0 lex) = "LINE")) Hebrew Input בשורה הבאה Decoder English Output in the next line Translation Output Lattice (0 1 (1 1 (2 2 (1 2 "THE (0 2 "IN (0 4 "IN THE NEXT Preprocessing Morphology"> [ADJ NP1] ((X3::Y1) (X1::Y2) ((X1 def) = +) ((X1 status) =c absolute) ((X1 num) = (X3 num)) ((X1 gen) = (X3 gen)) (X0 = X1)) Translation Lexicon N::N |: ["$WR"] -> ["BULL"] ((X1::Y1) ((X0 NUM) = s) ((Y0 lex) = "BULL")) N::N |: ["$WRH"] -> ["LINE"] ((X1::Y1) ((X0 NUM) = s) ((Y0 lex) = "LINE")) Hebrew Input בשורה הבאה Decoder English Output in the next line Translation Output Lattice (0 1 (1 1 (2 2 (1 2 "THE (0 2 "IN (0 4 "IN THE NEXT Preprocessing Morphology">

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Hebrew-to-English XFER MT Project - Update Alon Lavie June 2, 2004.

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Presentation on theme: "Hebrew-to-English XFER MT Project - Update Alon Lavie June 2, 2004."— Presentation transcript:

1 Hebrew-to-English XFER MT Project - Update Alon Lavie June 2, 2004

2 Hebrew-to-English MT Update2 The Team Alon Lavie Shuly Wintner (Faculty at Haifa Univ.) Yaniv Eytani (MS student at Haifa Univ.) Erik Peterson and Kathrin Probst…

3 Transfer Engine English Language Model Transfer Rules {NP1,3} NP1::NP1 [NP1 "H" ADJ] -> [ADJ NP1] ((X3::Y1) (X1::Y2) ((X1 def) = +) ((X1 status) =c absolute) ((X1 num) = (X3 num)) ((X1 gen) = (X3 gen)) (X0 = X1)) Translation Lexicon N::N |: ["$WR"] -> ["BULL"] ((X1::Y1) ((X0 NUM) = s) ((Y0 lex) = "BULL")) N::N |: ["$WRH"] -> ["LINE"] ((X1::Y1) ((X0 NUM) = s) ((Y0 lex) = "LINE")) Hebrew Input בשורה הבאה Decoder English Output in the next line Translation Output Lattice (0 1 "IN" @PREP) (1 1 "THE" @DET) (2 2 "LINE" @N) (1 2 "THE LINE" @NP) (0 2 "IN LINE" @PP) (0 4 "IN THE NEXT LINE" @PP) Preprocessing Morphology

4 June 2, 2004Hebrew-to-English MT Update4 Main Tasks in Month-1 Hebrew Encoding Issues Hebrew Language Resources: –H-to-E Translation Lexicon –Morphological Analyzer Putting together a front-end to the XFER engine: morphology, format conversions Elicitation for Hebrew (two versions of EC) Installing system on local server in Haifa

5 June 2, 2004Hebrew-to-English MT Update5 Main Tasks in Month-2 Improving Hebrew Language Resources: –H-to-E Translation Lexicon: “full” spelling, reverse dict, compounds, enhanced English side –Morphological Analyzer: all analyses, lattice representation Manual Transfer Grammar Collecting development and testing data (and their reference translations) Development based on small dev-set Evaluation on test data

6 June 2, 2004Hebrew-to-English MT Update6 Hebrew Encoding Issues Input texts are (mostly) in standard Windows encoding for Hebrew Morphology analyzer and other resources already set to work in an “ascii-like” representation  Converter script converts the input into the ascii representation All further processing is done in the ascii representation Lexicon and grammar rules are also in ascii representation Elicitation is done in UTF8 Hebrew, output is converted to ascii representation

7 June 2, 2004Hebrew-to-English MT Update7 Translation Lexicon “Dahan” H-to-E and E-to-H dictionary available to us Excel spreadsheet format (from prev project) Coverage is not great but not bad –H-to-E is about 15K translation pairs –E-to-H is about 7K translation pairs POS information on both sides No proper names or named entities Issue with spelling convention “KTIB XSR”

8 June 2, 2004Hebrew-to-English MT Update8 Translation Lexicon Yaniv wrote scripts that –Extract the relevant fields from the excel file –Extract words in “deficient spelling” and transform into “full spelling” –Extract and special treat compound nouns –Merge with added lexicons (i.e. names) –Sort and remove duplicate entries –Convert to the XFER lexicon format Kathrin adapted script that “enhances” lexicon for English generation (plurals of nouns, tensed verb forms) [Show portion of full lexicon…]

9 June 2, 2004Hebrew-to-English MT Update9 Morphological Analyzer Morphology is a big deal for Hebrew Not just inflections and derivations, but also –Different words due to omission of vowels from the script –Attached prefixes for conj, det, prepositions, and some attached possessive suffixes Analyzer program from MS student at Technion already available, works on Windows and with minimal adaptation on Linux Coverage is reasonable… Produces all analyses or a disambiguated analysis for each word Entire sentence passed as input to morpher (not word- by-word)

10 June 2, 2004Hebrew-to-English MT Update10 Morphological Processing Split attached prefixes and suffixes into separate words for translation Produce f-structures as output Convert feature-value codes to our conventions Install morpher as a server running on our linux machines Yaniv wrote java scripts to handle input-output from the morpher Erik integrated a wrapper for running morpher as a server on our linux machines “All analyses mode”: all possible analyses for each input word returned, represented in the form of a input lattice

11 June 2, 2004Hebrew-to-English MT Update11 Morphology Example Input word: B$WRH 0 1 2 3 4 |--------B$WRH--------| |-----B-----|$WR|--H--| |--B--|-H--|--$WRH---|

12 June 2, 2004Hebrew-to-English MT Update12 Morphology Example Y0: ((SPANSTART 0) Y1: ((SPANSTART 0) Y2: ((SPANSTART 1) (SPANEND 4) (SPANEND 2) (SPANEND 3) (LEX B$WRH) (LEX B) (LEX $WR) (POS N) (POS PREP)) (POS N) (GEN F) (GEN M) (NUM S) (NUM S) (STATUS ABSOLUTE)) (STATUS ABSOLUTE)) Y3: ((SPANSTART 3) Y4: ((SPANSTART 0) Y5: ((SPANSTART 1) (SPANEND 4) (SPANEND 1) (SPANEND 2) (LEX $LH) (LEX B) (LEX H) (POS POSS)) (POS PREP)) (POS DET)) Y6: ((SPANSTART 2) Y7: ((SPANSTART 0) (SPANEND 4) (SPANEND 4) (LEX $WRH) (LEX B$WRH) (POS N) (POS LEX)) (GEN F) (NUM S) (STATUS ABSOLUTE))

13 June 2, 2004Hebrew-to-English MT Update13 Manual Transfer Grammar Written by Alon in a couple of days… Current grammar has 36 rules: –21 NP rules –one PP rule –6 verb complexes and VP) rules –8 higher-phrase and sentence-level rules Captures the most common (mostly local) structural differences between Hebrew and English [show portion of grammar…]

14 June 2, 2004Hebrew-to-English MT Update14 Elicitation for Hebrew Erik made sure Elicitation Tool works for Hebrew Various versions of EC used: –Two reduced versions of full EC –Two versions of Structural EC Shuly and Yaniv translated and aligned substantial portion of both Kathrin trained an initial learned grammar

15 June 2, 2004Hebrew-to-English MT Update15 Decoding Strong Decoder for H-to-E: –Kathrin and Alon adapted script for running Stephan’s decoder. –No real amounts of parallel text, so no translation model scores for the edges… –Kathrin constructed a new English LM for decoding the Hebrew-to-English system 160 Million words Includes English side of our translation lexicon [show portion of lattice…]

16 June 2, 2004Hebrew-to-English MT Update16 Sample Output (dev-data) maxwell anurpung comes from ghana for israel four years ago and since worked in cleaning in hotels in eilat a few weeks ago announced if management club hotel that for him to leave israel according to the government instructions and immigration police in a letter in broken english which spread among the foreign workers thanks to them hotel for their hard work and announced that will purchase for hm flight tickets for their countries from their money

17 June 2, 2004Hebrew-to-English MT Update17 Evaluation Results Test set of 62 sentences from Haaretz newspaper, 2 reference translations SystemBLEUNISTPRMETEOR No Gram0.06163.41090.38300.41530.3668 Learned0.07743.54510.39380.42190.3775 Manual0.10263.77890.40850.42410.3834

18 June 2, 2004Hebrew-to-English MT Update18 Further Issues Transfer: XFER engine cannot handle the construction of full lattices anymore (too many entries)  we need a pruning mechanism Further improvements in the translation lexicon and morphological analyzer Decoding: –Adding a source-language LM –Can we train a translation model? Manual Grammar development… Improved grammar learning…


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