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Catch the Link! Combining Clues for Word Alignment Jörg Tiedemann Uppsala University

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1 Catch the Link! Combining Clues for Word Alignment Jörg Tiedemann Uppsala University

2 Outline yBackground xWhat do we want? xWhat do we have? xWhat do we need? yClue Alignment xWhat is a clue? xHow do we find clues? xHow do we use clues? xWhat do we get?

3 automatically language independent What do we want? Source Trans- lation 1 Sentence aligner Parallel corpus Trans- lation 2 Word aligner Token links Type links Aligned corpus

4 What do we have? ytokeniser (ca 99%) yPOS tagger (ca 96%) ylemmatiser (ca 99%) yshallow parser (ca 92%), parser (> 80%) ysentence aligner (ca 96%) yword aligner x75% precision x45% recall

5 zWord alignment challenges: ynon-linear mapping ygrammatical/lexical differences ytranslation gaps ytranslation extensions yidiomatic expressions ymulti-word equivalences What’s the problem with Word Alignment? (1) Our Hasid is in his late twenties. (2) Vår chassid är bortåt de trettio. (Saul Bellow “To Jerusalem and back: a personal account”) (1) I take the middle seat, which I dislike, but I am not really put out. (2) Jag tar mittplatsen, vilket jag inte tycker om, men det gör mig inte så mycket. (Saul Bellow “To Jerusalem and back: a personal account”) (1) Armén kommer att reformeras och effektiviseras. (2) The army will be reorganized with the aim of making it more effective. (The Declarations of the Swedish Government, 1988) (1) Neutralitetspolitiken stöds av ett starkt försvar till värn för vårt oberoende. (2) Our policy of neutrality is underpinned by a strong defence. (The Declarations of the Swedish Government, 1988) (1) Alsop says, "I have a horror of the bad American practice of choosing up sides in other people's politics,..." (2) Alsop förklarar: "Jag fasar för den amerikanska ovanan att välja sida i andra människors politik,...” (Saul Bellow “To Jerusalem and back: a personal account”)

6 So what? What are the real problems? zWord alignment yuses simple, fixed tokenisation yfails to identify appropriate translation units yignores contextual dependencies yignores relevant linguistic information yuses poor morphological analyses

7 What do we need? zflexible tokenisation zpossible multi-word units zlinguistic tools for several languages zintegration of linguistic knowledge zcombination of knowledge resources zalignment in context

8 Let’s go! zClue Alignment! finding clues combining clues aligning words

9 Word Alignment Clues y The United Nations conference has started today. y Idag började FN-konferensen. DT NNP NNP NN VBZ VBN RB RGOS NP VP ADVP [ ][ ][ ] ADVP VC NP conference konferensen

10 Word Alignment Clues  Def.: A word alignment clue C i (s,t) is a probability which indicates an association between two lexical items, s and t, from parallel texts. zDef.: A lexical item is a set of words with associated features attached to it.

11 How do we find clues? (1) zClues can be estimated from association scores: yC i (s,t) = w i * A i (s,t) xco-occurrence: Dice coefficient: A 1 (s,t) = Dice (s,t) Mutual information: A 2 (s,t) = I (s;t) xstring similarity longest common sub-seq.ratio: A 3 (s,t) = LCSR (s,t)

12 How do we find clues? (2) zClues can be estimated from training data: yC i (s,t) = w i * P (f t |f s )  w i * freq(f t,f s )/freq(f s )  f s, f t are features of s and t, e.g. part-of-speech sequences of s, t phrase category (NP, VP etc), syntactic function word position context features

13 How do we use clues? (1) yClues are simply sets of association measures yThe crucial point: we have to combine them! If C i (s,t) = P(a i ), define the total clue as  C all (s,t) = P(A) = P(a 1  a 2 ...  a n ) Clues are not mutually exclusive! Ù P(a 1  a 2 ) = P(a 1 ) + P(a 2 ) - P(a 1  a 2 ) Assume independence! Ù P(a 1  a 2 ) = P(a 1 ) * P(a 2 )

14 How do we use clues? (2) zClues can refer to any set of tokens from source and target language segments. Ù overlaps Ù inclusions zDef.: A clue shares its indication with all member tokens! Ù allow clue combinations at the level of single tokens

15 Clue overlaps - an example xThe United Nations conference has started today. xIdag började FN-konferensen. Clue 1 (co-occurrence) United Nations FN-konferensen 0.4 Nations conference FN-konferensen 0.5 United FN-konferense 0.3 Clue 2 (string similarity) conferenceFN-konferensen0.57 NationsFN-konferensen0.29 Clue all UnitedFN-konferensen0.58 NationsFN-konferensen0.787 conferenceFN-konferensen0.785

16 The Clue Matrix Idag började FN-konferensen The United Nations Conference has started today 0.5 Clue 2 (string similarity) conferenceFN-konferensen0.57 NationsFN-konferensen0.29 todayidag0.4 Clue 1 (co-occurrence) The United NationsFN-konferensen0.5 United NationsFN-konferensen0.4 hasbörjade0.2 startedbörjade0.6 started todayidag0.3 Nations conferencebörjade

17 Clue Alignment (1) ygeneral principles: xcombine all clues and fill the matrix xhighest score = best link xallow overlapping links only if there is no better link for both tokens if tokens are next to each other xlinks which overlap at one point form a link cluster

18 Clue Alignment (2) zthe alignment procedure: 1. find the best link 2. remove the best link (set its value to 0) 3. check for overlaps accept: add to set of link clusters dismiss otherwise 4. continue with 1 until no more links are found (or all values are below a certain threshold)

19 Clue Alignment (3) Idag började FN-konferensen The United Nations conference has started today 0.5 Best link: NationsFN-konferensen0.787 Link clusters: NationsFN-konferensen Best link: startedbörjade Link clusters: NationsFN-konferensen startedbörjade Best link: UnitedFN-konferensen0.7 Link clusters: United NationsFN-konferensen startedbörjade Best link: todayidag0.58 Link clusters: United NationsFN-konferensen startedbörjade todayidag 0 Best link: conference FN-konferensen 0.57 Link clusters: United Nations conference FN-konferensen started började today idag 0 Best link: TheFN-konferensen 0.5 Link clusters: The United Nations conference FN-konferensen started började today idag Link clusters: The United Nations conference FN-konferensen has started började today idag Best link: hasbörjade 0.2 0

20 Bootstrapping zagain: clues can be estimated from training data zself-training: use available links as training data zgoal: learn new clues for the next step zrisk: increased noise (lower precision)

21 Learning Clues yPOS-clue: xassumption: word pairs with certain POS-tags are more likely to be translations of each other than other word pairs xfeatures: POS-tag sequences yposition clue: xassumption: translations are relatively close to each other (esp. in related languages) xfeatures: relative word positions

22 So much for the theory! Results?! yThe setup: Corpus and basic tools: Saul Bellow’s “To Jerusalem and back: a personal account ”, English/Swedish, about 170,000 words English POS-tagger (Grok), trained on Brown, PTB English shallow parser (Grok), trained on PTB English stemmer, suffix truncation Swedish POS-tagger (TnT), trained on SUC Swedish CFG parser (Megyesi), rule-based Swedish lemmatiser, database taken from SUC

23 Results!?! … not yet xbasic clues: Dice coefficient (  0.3) LCSR (0.4),  3 characters/string xlearned clues: POS clue position clue xclue alignment threshold = 0.4 xuniform normalisation (0.5)

24 Results!!! Come on! Preliminary results (… work in progress …) zEvaluation: 500 random samples have been linked manually (Gold standard) zMetrics: precision PWA & recall PWA (Ahrenberg et al, 2000)

25 Give me more numbers! zThe impact of parsing. zHow much do we gain? yAlignment results with n-grams, (shallow) parsing, and both:

26 One more thing. zStemming, lemmatisation and all that … yDo we need morphological analyses for Swedish and English?

27 Conclusions yCombining clues helps to find links yLinguistic knowledge helps xPOS tags are valuable clues xword position gives hints for related languages xparsing helps with the segmentation problem xlemmatisation gives higher recall yWe need more experiments, tests with other language pairs, more/other clues yrecall & precision is still low

28

29 POS clues - examples scoresourcetarget VBZ WRB RH0S VBP RB RG0S VBD DT NNP NN PRP VBZ NNS NNP VB 0.6 RBR RGCS 0.5 DT JJ JJ AQP0SNDS

30 Position clues - examples scoremapping x -> x -> x -> x -> x -> x -> x -> 6 7 8

31 Open Questions yNormalisation!  How do we estimate the w i ’s? yNon-contiguous phrases xWhy not allow long distance clusters? yIndependence assumption xWhat is the impact of dependencies? yAlignment clues xWhat is a bad clue, what is a good one? xContextual clues

32 Clue alignment - example be ko var ställ scher min fru undrar road för jag de en lunch. amused , my wife asks why i ordered the kosher lunch

33 Alignment - examples the Middle East Mellersta Östern afford kosta på at least åtminstone an American satellite en satellit common sense sunda förnuftet Jerusalem area Jerusalemområdet kosher lunch koscherlunch leftist anti-Semitism vänsterantisemitism left-wing intellectuals vänsterintellektuella literary history litteraturhistoriska manuscript collection handskriftsamling Marine orchestra marinkårsorkester marionette theater marionetteatern mathematical colleagues matematikkolleger mental character mentalitet far too alldeles

34 Alignment - examples a banquet en bankett a battlefield ett slagfält a day dagen the Arab states arabstaterna the Arab world arabvärlden the baggage carousel bagagekarusellen the Communist dictatorships kommunistdiktaturerna The Fatah terrorists Al Fatah-terroristerna the defense minister försvarsministern the defense minister försvarsminister the daughter dotter the first President förste president

35 Alignment - examples American imperial interests amerikanska imperialistintressenas Chicago schools Chicagos skolor decidedly anti-Semitic avgjort antisemitiska his identity sin identitet his interest sitt intresse his interviewer hans intervjuare militant Islam militanta muhammedanismen no longer inte längre sophisticated arms avancerade vapen still clearly uppenbarligen ännu dozen Russian dussin ryska exceedingly intelligent utomordentligt intelligent few drinks några drinkar goyish democracy gojernas demokrati industrialized countries industrialiserade länderna has become har blivit

36 Gold standard - MWUs link: Secretary of State -> Utrikesminister link type: regular unit type: multi -> single source text: Secretary of State Henry Kissinger has won the Middle Eastern struggle by drawing Egypt into the American camp. target text: Utrikesminister Henry Kissinger har vunnit slaget om Mellanöstern genom att dra in Egypten i det amerikanska lägret.

37 Gold standard - fuzzy links link: unrelated -> inte tillhör hans släkt link type: fuzzy unit type: single -> multi source text: And though he is not permitted to sit beside women unrelated to him or to look at them or to communicate with them in any manner (all of which probably saves him a great deal of trouble), he seems a good-hearted young man and he is visibly enjoying himself. target text: Och fastän han inte får sitta bredvid kvinnor som inte tillhör hans släkt eller se på dem eller meddela sig med dem på något sätt (alltsammans saker som utan tvivel besparar honom en mängd bekymmer) verkar han vara en godhjärtad ung man, och han ser ut att trivas gott.

38 Gold standard - null links link: do -> link type: null unit type: single -> null source text:"How is it that you do not know English?" target text:"Hur kommer det sig att ni inte talar engelska?"

39 Gold standard - morphology link: the masses -> massorna link type: regular unit type: multi -> single source text: Arafat was unable to complete the classic guerrilla pattern and bring the masses into the struggle. target text: Arafat har inte kunnat fullborda det klassiska gerillamönstret och föra in massorna i kampen.

40 Evaluation metrics z C src – number of overlapping source tokens in (partially) correct link proposals, C src =0 for incorrect link proposals z C trg – number of overlapping target tokens in (partially) correct link proposals, C trg =0 for incorrect link proposals z S src – number of source tokens proposed by the system z S trg – number of target tokens proposed by the system z G src – number of source tokens in the gold standard z G trg – number of target tokens in the gold standard

41 Evaluation metrics - example

42 Corpus markup (Swedish) Det är som ett besök i barndomen

43 Corpus markup (English) It is my childhood revisited.

44 … is that all? zHow good are the new clues? yAlignment results with learned clues only: (neither LCSR nor Dice)


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