Advanced Signal Processing 05/06 Reinisch Bernhard Statistical Machine Translation Phrase Based Model

2/37 ASP 06/07 Reinisch Bernhard Translation Model – Phrase-based Overview ● The quality of the MT systems have improved with the use of phrase translation – Phrases from word-based alignments – Syntactic phrases – Phrases from phrase alignments – IBM word-based statistical MT systems enhanced with phrase translation ● Best to extract phrase translations pairs? – Evaluation Framework / Outcome

3/37 ASP 06/07 Reinisch Bernhard Translation Model – Phrase-based Word based approaches ● Try to model word-to-word correspondences ● Models are often restricted – source word -> exactly one target word – Hidden Markov models in speech recognition ● Enhanced to “One-to-many” alignment model – Solve lexical problems like ● “Zahnarzttermin” -> “dentist’s appointment” ● Order of words will be changed

4/37 ASP 06/07 Reinisch Bernhard Translation Model – Phrase-based Statistical machine translation (1) ● argmax … search/decoding problem (generation of the output sentence) ● Pr(e 1 ) … language model ● Pr(f 1 |e 1 ) … translation model

5/37 ASP 06/07 Reinisch Bernhard Translation Model – Phrase-based Statistical machine translation (2) Taken from [2]

6/37 ASP 06/07 Reinisch Bernhard Translation Model – Phrase-based Learning translation lexica ● Following describes methods for learning single-word and phrase-based translation lexica – Statistical alignment models ● Used for learning word alignments ● Symmetrization – Bilingual phrases – Alignment templates

7/37 ASP 06/07 Reinisch Bernhard Translation Model – Phrase-based Statistical alignment models (1) ● In the alignment model – A “hidden” parameter is introduced a – a describes the mapping from source position j to target position a j ● “a” is represented as a matrix with binary values – 1 entry … words are aligned – 0 entry … words are not aligned – source word -> no target word (empty word e o )

8/37 ASP 06/07 Reinisch Bernhard Translation Model – Phrase-based Statistical alignment models (2) ● In general the model depends on a set of unknown parameters ● Exist several different specific statistical alignment models – First compute word alignments i.e. model 4 – Train this hidden parameters θ ● Alignment with highest probability – called Viterbi alignment

9/37 ASP 06/07 Reinisch Bernhard Translation Model – Phrase-based Symmetrization (1) ● Baseline alignment model (i.e. model 4) does not allow multiple target words – “Zahnarzttermin” -> “dentist’s appointment” ● Outcome should be such alignment matrix Taken from [2]

10/37 ASP 06/07 Reinisch Bernhard Translation Model – Phrase-based Symmetrization (2) ● To solve this problem – Training in both directions – For a sentence pair -> two Viterbi alignments – Now both alignments tables A1 and A2 have to combined (symmetized) ● Simple union of both tables (some refined methods) – Result then is used to train single word based translation lexica

11/37 ASP 06/07 Reinisch Bernhard Translation Model – Phrase-based Symmetrization (2) – By computing for relative frequencies using: ● N(e|f) … how many times e and f are aligned ● N(f) … how many time the word f occurs

12/37 ASP 06/07 Reinisch Bernhard Translation Model – Phrase-based Bilingual phrases ● Now we need an algorithm that relationships between whole phrases of source sentence m and target sentence n – “phrase extract” algorithm and take as input alignment matrix A Taken from [2]

13/37 ASP 06/07 Reinisch Bernhard Translation Model – Phrase-based Alignment templates (1) ● A more systematic approach – Considers whole phrases ● Whole group of adjacent words in the source ● maps to a whole group of words in the target – The context of words have greater influence – The changes of word order can be learned ● The Idea is to model two different alignment levels – Word level alignments – Phrase level alignments

14/37 ASP 06/07 Reinisch Bernhard Translation Model – Phrase-based Alignment templates (2) Alignments templates z –“F”… source class sequence –“E”…target class sequence –“A”… describes the alignment between source and target “F” and “E” are classes –The advantage is a better generalization

15/37 ASP 06/07 Reinisch Bernhard Translation Model – Phrase-based Alignment templates (3) Taken from [2]

16/37 ASP 06/07 Reinisch Bernhard Translation Model – Phrase-based Alignment templates (4) ● For the training we need the probability of applying an alignment template ● The “phrase extraction” have to be modified ● Can be estimated by relative frequencies ● Finished the “Learning translation lexica”-task

17/37 ASP 06/07 Reinisch Bernhard Translation Model – Phrase-based Translation model (1) For notation we decompose the sentences –f 1 J …source sentence –e 1 I …target sentence –sequence of phrases (k=1,…,K) Further considerations (only one segmentation)

18/37 ASP 06/07 Reinisch Bernhard Translation Model – Phrase-based Translation model (2) ● The model have to allow reordering of the phrases

19/37 ASP 06/07 Reinisch Bernhard Translation Model – Phrase-based Translation model (3) Taken from [2]

20/37 ASP 06/07 Reinisch Bernhard Translation Model – Phrase-based Translation model (4) Taken from [2]

21/37 ASP 06/07 Reinisch Bernhard Translation Model – Phrase-based Alignment template approach results ● Evaluation of the approach by a translation task (“Verbmobil Task”) ● Additional preprocessing – word-joinings – word-splitting Taken from [2]

22/37 ASP 06/07 Reinisch Bernhard Translation Model – Phrase-based Alignment template approach conclusions ● Overall we see a better performance ● So it is important to model word groups in source and target language ● By using two abstraction levels – Phrase level alignments – Word level alignments – -> greater influence of the context and can be learned explicitly

23/37 ASP 06/07 Reinisch Bernhard Translation Model – Phrase-based Syntactic phrases (1) ● A collection of all phrase pairs will also include non-intuitive phrases – “Okay, the”, “house the”, etc… – Intuitively such phrases do not help – Restricting to syntactically motivated phrases ● The idea of syntactic trees and phrases as subtrees

24/37 ASP 06/07 Reinisch Bernhard Translation Model – Phrase-based Syntactic phrases (2) ● The input sentence is preprocessed by a syntactic parser ● Different operations will be performed on each node – reordering child nodes – inserting extra words at each node – translating leaf words

25/37 ASP 06/07 Reinisch Bernhard Translation Model – Phrase-based Syntactic phrases (3) Taken from [4]

26/37 ASP 06/07 Reinisch Bernhard Translation Model – Phrase-based Syntactic phrases (4) Taken from [6]

27/37 ASP 06/07 Reinisch Bernhard Translation Model – Phrase-based Syntactic phrases (5) ● Reordering – Every given child sequence has a probability of reordering (N nodes -> N! pos. reorderings) – The probability of reordering is given by the model (table etc) ● Inserting – Extra word can be inserted (left/right) – Another table for insert probability ● Translating – Operation is applied to every leaf – Assumption that this operation only depends on the word itself

28/37 ASP 06/07 Reinisch Bernhard Translation Model – Phrase-based Experiments ● Now we have three models ● [1] build a system to compare them and measure performance under different aspects – Weighting syntactic phrases – Maximum phrase length ● Setup – Free corpus Europarl – German to English – Performance measured using BLEU score

29/37 ASP 06/07 Reinisch Bernhard Translation Model – Phrase-based Comparison of core methods ● AP… template alignment ● M4 … IBM Model 4 for word based translation ● Syn … syntactic phrases ● Training corpus size [sentences] Taken from [1]

30/37 ASP 06/07 Reinisch Bernhard Translation Model – Phrase-based Weighting syntactic phrases (1) ● The restriction on syntactic phrases is harmful, because too many phrases are eliminated ● Intuitively that can not be – Improvements in data collection, during translation, penalizing ● Results suggest – Collection of only syntactically phrases – Performance not better – But smaller table sizes

31/37 ASP 06/07 Reinisch Bernhard Translation Model – Phrase-based Weighting syntactic phrases (2) ● Example: – “es gibt” literally translates in “it gives” but really means “there is” – Not syntactic relationship – Also “with regard to”, “note that” syntactically complex but easy translation

32/37 ASP 06/07 Reinisch Bernhard Translation Model – Phrase-based Maximum phrase length ● How long do phrases have to be to achieve high performance? ● All experiments with “Phrases from word-based alignments” approach Taken from [1]

33/37 ASP 06/07 Reinisch Bernhard Translation Model – Phrase-based Simpler Underlying word-based models (1) ● The core of this framework is IBM model 4 for collecting phrase pairs ● Model 4 is computationally expensive, parameters problems (approximations) ● What about IBM models 1-3 – Faster and easier to implement – Model 1 and 2 compute word alignments efficiently

34/37 ASP 06/07 Reinisch Bernhard Translation Model – Phrase-based Simpler Underlying word-based models (2) ● How much is performance affected, if the base word alignment on these simpler methods? ● M1 worst performance ● But M2 & M3 provide similar performance to the M4 model Taken from [1]

35/37 ASP 06/07 Reinisch Bernhard Translation Model – Phrase-based Conclusions ● Intuitively phrase bases approaches gives better performance than word-based approaches ● Also experiments show us that – “straight forward” forward syntax based models have disadvantages ● The “best” outcome with small word phrases ● Phrase extraction and the alignment heuristic have a great influence

36/37 ASP 06/07 Reinisch Bernhard Translation Model – Phrase-based References ● [1] Philipp Koehn, Franz Josef Och, Daniel Marcu; Statistical Phrase- Based Translation ● [2] Franz Josef Och, Hermann Ney; The Alignment Template Approach to Statistical Machine Translation ● [3] Franz Josef Och, Christoph Tillmann, Hermann Ney; Improved Alignment Models for Statistical Machine Translation ● [4] Kenji Yamada, Kevin Knight; A Syntax-based Translation Model ● [5] Daniel Marcu, William Wong; A Phrase-Based, Joint Probability Model for Statistical Machine Translation ● [6] Amitabha Mukerjee, Ankit Soni and Achla M. Raina; Detecting Complex Predicates in Hindi using POS Projection across Parallel Corpora ● [7]

37/37 ASP 06/07 Reinisch Bernhard Translation Model – Phrase-based Advanced Signal Processing 05/06 Reinisch Bernhard Statistical Machine Translation Phrase Based Models