Re-evaluating Bleu Alison Alvarez Machine Translation Seminar February 16, 2006

Spring 2006 MT Seminar Overview The Weaknesses of Bleu  Introduction  Precision and Recall  Fluency and Adequacy  Variations Allowed by Bleu  Bleu and Tides 2005 An Improved Model  Overview of the Model  Experiment  Results Conclusions

Spring 2006 MT Seminar Introduction Bleu has been shown to have high correlations with human judgments Bleu has been used by MT researchers for five years, sometimes in place of manual human evaluations But does the minimization of the error rate accurately show improvements in translation quality?

Spring 2006 MT Seminar Precision and Bleu Of my answers, how many are right/wrong? Precision = B  C / C or A/C A Reference Translation Hypothesis Translation B C

Spring 2006 MT Seminar Precision and Bleu Bleu is a precision based metric The modified precision score, p n : P n = ∑ s  c ∑ ngram  s Count matched (ngram) ∑ s  c ∑ ngram  s Count(ngram)

Spring 2006 MT Seminar Recall and Bleu Of the potential answers how many did I retrieve/miss? Recall = B  C / B or A/B A Reference Translation Hypothesis Translation B C

Spring 2006 MT Seminar Recall and Bleu Because Bleu uses multiple reference translations at once, recall cannot be calculated

Spring 2006 MT Seminar Fluency and Adequacy to Evaluators Fluency  “How do you judge the fluency of this translation”  Judged with no reference translation and to the standard of written English Adequacy  “How much of the meaning expressed in the reference is also expressed in the hypothesis translation?”

Spring 2006 MT Seminar Variations Bleu allows for variations in word and phrase order that lead to less fluency No constraints occur on the order of matching n-grams

Spring 2006 MT Seminar Variations

Spring 2006 MT Seminar Variations The above two translations have the same bigram score.

Spring 2006 MT Seminar Bleu and Tides 2005 Bleu scores showed significant divergence from human judgments in the 2005 Tides Evaluation It ranked the system considered the best by humans as sixth in performance

Spring 2006 MT Seminar Bleu and Tides 2005 Reference: Iran had already announced Kharazi would boycott the conference after Jordan’s King Abdullah II accused Iran of meddling in Iraq’s affairs System A: Iran has already stated that Kharazi’s statements to the conference because of the Jordanian King Abdullah II in which he stood accused Iran of interfering in Iraqi affairs. N-gram matches: 1-gram: 27; 2-gram: 20; 3-gram: 15; 4 gram: 10 Human scores: Adequacy: 3,2; Fluency 3,2 From Callison-Burch 2005

Spring 2006 MT Seminar Bleu and Tides 2005 Reference: Iran had already announced Kharazi would boycott the conference after Jordan’s King Abdullah II accused Iran of meddling in Iraq’s affairs System B: Iran already announced that Kharazi will not attend the conference because of statements made by Jordanian Monarch Abdullah II who has accused Iran of interfering in Iraqi affairs. N-gram matches: 1-gram: 24; 2-gram: 19; 3-gram: 15; 4 gram: 12 Human scores: Adequacy: 5,4; Fluency 5,4 From Callison-Burch 2005

Spring 2006 MT Seminar An Experiment with Bleu

Spring 2006 MT Seminar Bleu and Tides 2005 “This opens the possibility that in order to for Bleu to be valid only sufficiently similar systems should be compared with one another”

Spring 2006 MT Seminar Additional Flaws Multiple Human reference translations are expensive N-grams showing up in multiple reference translations are weighted the same Content words are weighed the same as common words  ‘The’ counts the same as ‘Parliament’ Bleu accounts for the diversity of human translations, but not synonyms

Spring 2006 MT Seminar An Extension of Bleu Described in Babych & Hartley, 2004 Adds weights to matched items using  tf/idf  S-score

Spring 2006 MT Seminar Addressing Flaws Can work with only one human translation  Can actually calculate recall  The paper is not very clear about this sentence is selected Content words are weighed the differently than common words  ‘The’ does not count the same as ‘Parliament’

Spring 2006 MT Seminar Calculating the tf/idf Score tf.idf(i,j) = (1 + log (tf i,j )) log (N / df i ), if tf i,j ≥ 1; where:  tf i,j is the number of occurrences of the word w i in the document d j ;  df i is the number of documents in the corpus where the word w i occurs; N is the total number of documents in the corpus. From Babych 2004

Spring 2006 MT Seminar Calculating the S-Score The S-score was calculated as:  P doc(i,j) is the relative frequency of the word in the text  P corp-doc(i) is the relative frequency of the same word in the rest of the corpus, without this text;  (N – df (i) ) / N is the proportion of texts in the corpus, where this word does not occur  P corp(i) is the relative frequency of the word in the whole corpus, including this particular text.

Spring 2006 MT Seminar Integrating the S-Score If for a lexical item in a text the S ‑ score > 1, all counts for the N-grams containing this item are increased by the S-score (not just by 1, as in the baseline BLEU approach). If the S-score ≤1; the usual N-gram count is applied: the number is increased by 1. From Babych 2004

Spring 2006 MT Seminar The Experiment Used 100 French-English texts from the DARPA-94 evaluation corpus Included two reference translations Results from 4 Different MT systems

Spring 2006 MT Seminar The Experiment Stage 1:  tf/idf & S-scores are calculated on the two reference translations Stage 2:  N-gram based evaluation using Precision, Recall of n- grams in MT output  N-gram matches were adjusted to N-gram weights or S-Score Stage 3:  Comparison with human scores

Spring 2006 MT Seminar Results for tf/idf System [ade] / [flu] BLEU [1&2] Prec. (w) 1/2 Recall (w) 1/2 Fscore (w) 1/2 CANDIDE / GLOBALINK / MS / REVERSO NA / NA SYSTRAN / Corr r(2) with [ade] – MT Corr r(2) with [flu] – MT

Spring 2006 MT Seminar Results for S-Score System [ade] / [flu] BLEU [1&2] Prec. (w) 1/2 Recall (w) 1/2 Fscore (w) 1/2 CANDIDE / GLOBALINK / MS / REVERSO NA / NA SYSTRAN / Corr r(2) with [ade] – MT Corr r(2) with [flu] – MT

Spring 2006 MT Seminar Results The n-gram model beats BLEU in adequacy The f-score metric is more strongly correlated with fluency Single Reference translations are stable (add stability chart?)

Spring 2006 MT Seminar Conclusions The Bleu model can be too coarse to show differentiate between very different MT systems Adequacy is harder to predict than fluency Adding weights and using recall and f- scores can bring higher correlations with adequacy and fluency scores

Spring 2006 MT Seminar References Chris Callison-Burch, Miles Osborne and Philipp Koehn Re-evaluating the Role of Bleu in Machine Translation Research, to appear in EACL-06. Kishore Papineni, Salim Roukos, Todd Ward and Wei-Jing Zhu BLEU: a Method for Automatic Evaluation of Machine Translation. In Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics (ACL-02). Philadelphia, PA. July pp Babych B, Hartley A Extending BLEU MT Evaluation Method with Frequency Weighting, In Proceedings of the 42th Annual Meeting of the Association for Computational Linguistics (ACL-04). Barcelona, Spain. July Dan Melamed, Ryan Green, and joseph P. Turian. Precision and recall of machine translation. In Proceedings of the Human Language Technology Conference (HLT), pages , Edmonton, Alberta, May HLT-NAACL. Deborah Coughlin Correlating automated andhuman assessments of machine translation quality.In Proceedings of MT Summit IX. LDC Linguistic data annotation specification:Assessment of fluency and adequacy in translations.Revision 1.5

Spring 2006 MT Seminar The Brevity Penalty is designed to compensate for overly terse translations BP = { c = length of corpus of hypothesis translations r = effective corpus length* Precision and Bleu 1 if c > r e 1-r/c if c ≤ r

Spring 2006 MT Seminar Thus, the total Bleu score is this: BLEU = BP * exp( ∑ w n log p n ) Precision and Bleu n n=1

Spring 2006 MT Seminar Flaws in the Use of Bleu Experiments with Bleu, but no manual evaluation (Callison-Burch 2005)