[文学阅读] METEOR: An Automatic Metric for MT Evaluation with Improved Correlation with Human Judgments

METEOR: An Automatic Metric for MT Evaluation with Improved Correlation with Human Judgments

Satanjeev Banerjee   Alon Lavie 

Language Technologies Institute  

Carnegie Mellon University  

Pittsburgh, PA 15213  

banerjee+@cs.cmu.edu  alavie@cs.cmu.edu

Important Snippets:

1. In  order  to  be  both  effective  and  useful,  an automatic metric for MT evaluation has to satisfy several basic criteria.  The primary and most intuitive requirement is that the metric have very high correlation with
quantified human notions of MT quality.  Furthermore, a good metric should be as sensitive as possible to differences in MT quality between  different  systems,  and  between  different versions of the same system.  The metric should be 

consistent  (same  MT  system  on  similar  texts should produce similar scores), reliable (MT systems that score similarly can be trusted to perform similarly) and general (applicable to different MT tasks in a wide range of domains and scenarios).  Needless
to say, satisfying all of the above criteria is  extremely  difficult,  and  all  of  the metrics  that have been proposed so far fall short of adequately addressing  most  if  not  all  of  these requirements.

2. It  is  based  on  an explicit word-to-word  matching  between  the  MT  output being evaluated and one or more reference translations.    Our  current  matching  supports  not  only matching  between  words that are  identical
in the two  strings  being  compared,  but  can  also  match words  that  are  simple  morphological  variants  of each other

3. Each possible matching is scored based on a combination of several features.  These  currently  include  uni-gram-precision,  uni-gram-recall, and a direct measure of how out-of-order the words of the MT output are with respect to
the reference.

4.Furthermore, our results demonstrated that recall plays a more important role than precision  in  obtaining  high-levels  of  correlation  with human judgments.

5.BLEU does not take recall into account directly.

6.BLEU  does  not  use  recall  because  the notion of recall is unclear when matching simultaneously  against  a  set  of  reference  translations (rather than a single reference).  To compensate for recall, BLEU uses a Brevity
Penalty, which penalizes translations for being “too short”.

7.BLEU  and  NIST  suffer  from  several  weaknesses:

>The Lack of Recall

>Use  of Higher Order  N-grams

>Lack  of  Explicit  Word-matching  Between Translation and Reference

>Use  of  Geometric  Averaging  of  N-grams

8.METEOR was designed to explicitly address the weaknesses in BLEU identified above.  It evaluates a  translation  by  computing  a  score  based  on  explicit  word-to-word  matches  between  the  translation and a reference
translation. If more than one reference translation is available, the given translation  is  scored  against  each  reference  independently,  and  the  best  score  is  reported.

9.Given a pair of translations to be compared (a system  translation  and  a  reference  translation), METEOR  creates  an alignment between  the  two strings. We define an alignment as a mapping be-tween unigrams, such that
every unigram in each string  maps  to  zero  or  one  unigram  in  the  other string, and to no unigrams in the same string.

10.This  alignment  is  incrementally  produced through a series of stages, each stage consisting of  two distinct phases.

11.In the first phase an external module lists all the possible  unigram  mappings  between  the  two strings.

12.Different modules map unigrams based  on  different  criteria.  The  “exact”  module maps  two  unigrams  if  they  are  exactly  the  same (e.g.  “computers”  maps  to  “computers”  but  not “computer”). The “porter stem”
module maps two unigrams  if  they  are  the  same after they  are stemmed  using  the  Porter  stemmer  (e.g.:  “com-puters”  maps  to  both  “computers”  and  to  “com-puter”).  The  “WN  synonymy”  module  maps  two unigrams if they are synonyms of each
other.

13.In  the  second  phase  of  each  stage,  the  largest subset of these unigram mappings is selected such 

that  the  resulting  set  constitutes  an alignment as defined above

14. METEOR selects that set that has the least number of unigram mapping crosses.

15.By default the first stage uses the “exact” mapping  module,  the  second  the  “porter  stem” module and the third the “WN synonymy” module.

16. unigram precision (P)

unigram  recall  (R)

Fmean by combining the precision and recall via a harmonic-mean

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To  take  into  account  longer matches, METEOR computes a penalty for a given alignment as follows.

chunks such that  the  uni-grams  in  each  chunk  are  in  adjacent  positions  in the system translation, and are also mapped to uni-grams that are in adjacent positions in the reference translation.

Conclusion: METEOR prefer recall to precision while BLEU is converse.Meanwhile, it incorporates many information.

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