User:Aidana/Proposal - Revision history

Aidana: /* Work plan */

2016-03-24T19:28:50Z

Work plan

Aidana: /* Coding challenge finished */

2016-03-24T19:16:31Z

Coding challenge finished

Aidana: /* Coding challenge finished */

2016-03-24T19:16:11Z

Coding challenge finished

Aidana: /* Coding challenge finished */

2016-03-24T19:15:38Z

Coding challenge finished

Aidana: /* Coding challenge finished */

2016-03-24T19:15:01Z

Coding challenge finished

Aidana: /* Initial data:Coding challenge */

2016-03-24T19:11:13Z

Initial data:Coding challenge

Aidana: /* Initial data */

2016-03-24T19:03:17Z

Initial data

Aidana at 17:03, 23 March 2016

2016-03-23T17:03:38Z

Aidana: /* Deatiled Workplan */

2016-03-21T13:12:18Z

Deatiled Workplan

Aidana: /* Why is it that they are interested in the Apertium project? */

2016-03-21T13:11:23Z

Why is it that they are interested in the Apertium project?

@@ Line 113: / Line 113: @@
 ==Work plan==
+===Detecting problems===
+While doing coding challenge, I detect some problems in most of rules:
+# Wrong prepositions: need more disambiguation rules
+# Extra prepostions: after running macros some variables are not cleaned and  not correctly assigned. Partly solved after coding challenge
+# Generating invalid forms: with # and extra tags "<#pres". I am planning to detect more of them by using different corpuses. These errors are appeared because of wrong t1x rules.
+# Some interchunk rules do not do agreement between subject and verb right.
+For first time, I am planning to use corpus, which consist of 5625 sentences, it is Kazakh-English bilingual corpus.
 ===Major goals===
 I plan to work on transfer rules and work on vocabulary, to be more precise, I plan to reach:

@@ Line 82: / Line 82: @@
 NIST score = 4.2546  BLEU score = 0.2206 for system "zzz"
- ------------------------------------------------------------------------
 Individual N-gram scoring
@@ Line 91: / Line 90: @@
  BLEU:  0.6069   0.2476   0.1671   0.1287   0.1065   0.0800   0.0571   0.0345   0.0143  "zzz"
- ------------------------------------------------------------------------
 Cumulative N-gram scoring
 -gram   2-gram   3-gram   4-gram   5-gram   6-gram   7-gram   8-gram   9-gram

@@ Line 82: / Line 82: @@
 NIST score = 4.2546  BLEU score = 0.2206 for system "zzz"
-# ------------------------------------------------------------------------
+ ------------------------------------------------------------------------
 Individual N-gram scoring
@@ Line 91: / Line 91: @@
  BLEU:  0.6069   0.2476   0.1671   0.1287   0.1065   0.0800   0.0571   0.0345   0.0143  "zzz"
-# ------------------------------------------------------------------------
+ ------------------------------------------------------------------------
 Cumulative N-gram scoring
 -gram   2-gram   3-gram   4-gram   5-gram   6-gram   7-gram   8-gram   9-gram

@@ Line 75: / Line 75: @@
 *added rlx rule in apertium-kaz
 BLEU:
-Evaluation of kaz-to-eng translation using:
+  Evaluation of kaz-to-eng translation using:
     src set "apertium" (1 docs, 43 segs)
     ref set "apertium" (1 refs)
     tst set "apertium" (1 systems)
-NIST score = 2.2109  BLEU score = 0.0386 for system "apertium"
+NIST score = 4.2546  BLEU score = 0.2206 for system "zzz"
+# ------------------------------------------------------------------------
 Individual N-gram scoring
 -gram   2-gram   3-gram   4-gram   5-gram   6-gram   7-gram   8-gram   9-gram
         ------   ------   ------   ------   ------   ------   ------   ------   ------
- NIST:  2.0950   0.1140   0.0019   0.0000   0.0000   0.0000   0.0000   0.0000   0.0000  "apertium"
+ NIST:  3.6697   0.4962   0.0723   0.0163   0.0000   0.0000   0.0000   0.0000   0.0000  "zzz"
- BLEU:  0.3333   0.0607   0.0174   0.0063   0.0023   0.0013   0.0007   0.0004   0.0002  "apertium"
+ BLEU:  0.6069   0.2476   0.1671   0.1287   0.1065   0.0800   0.0571   0.0345   0.0143  "zzz"
+# ------------------------------------------------------------------------
 Cumulative N-gram scoring
 -gram   2-gram   3-gram   4-gram   5-gram   6-gram   7-gram   8-gram   9-gram
         ------   ------   ------   ------   ------   ------   ------   ------   ------
- NIST:  2.0950   2.2090   2.2109   2.2109   2.2109   2.2109   2.2109   2.2109   2.2109  "apertium"
+ NIST:  3.6697   4.1659   4.2382   4.2546   4.2546   4.2546   4.2546   4.2546   4.2546  "zzz"
- BLEU:  0.3333   0.1423   0.0706   0.0386   0.0220   0.0137   0.0090   0.0061   0.0043  "apertium"
+ BLEU:  0.5615   0.3587   0.2709   0.2206   0.1878   0.1608   0.1372   0.1143   0.0899  "zzz"
 WER:

@@ Line 75: / Line 75: @@
 *added rlx rule in apertium-kaz
 BLEU:
+Evaluation of kaz-to-eng translation using:
+    src set "apertium" (1 docs, 43 segs)
+    ref set "apertium" (1 refs)
+    tst set "apertium" (1 systems)
+NIST score = 2.2109  BLEU score = 0.0386 for system "apertium"
+Individual N-gram scoring
+-gram   2-gram   3-gram   4-gram   5-gram   6-gram   7-gram   8-gram   9-gram
+        ------   ------   ------   ------   ------   ------   ------   ------   ------
+ NIST:  2.0950   0.1140   0.0019   0.0000   0.0000   0.0000   0.0000   0.0000   0.0000  "apertium"
+ BLEU:  0.3333   0.0607   0.0174   0.0063   0.0023   0.0013   0.0007   0.0004   0.0002  "apertium"
+Cumulative N-gram scoring
+-gram   2-gram   3-gram   4-gram   5-gram   6-gram   7-gram   8-gram   9-gram
+        ------   ------   ------   ------   ------   ------   ------   ------   ------
+ NIST:  2.0950   2.2090   2.2109   2.2109   2.2109   2.2109   2.2109   2.2109   2.2109  "apertium"
+ BLEU:  0.3333   0.1423   0.0706   0.0386   0.0220   0.0137   0.0090   0.0061   0.0043  "apertium"
 WER:

@@ Line 66: / Line 66: @@
 *Number of position-independent correct words: 97
 *Position-independent word error rate (PER): 81.35 %
+==MT evaluation ended on 2016 Mar 24 at 01:05:00==
+==Coding challenge finished==
+Results:
+*removed all #
+*added unknown words
+*added t1x and tx2 rules
+*added rlx rule in apertium-kaz
+BLEU:
+WER:
+Statistics about input files
+*Number of words in reference: 480
+*Number of words in test: 388
+*Number of unknown words (marked with a star) in test: 0
+*Percentage of unknown words: 0.00 %
+Results when removing unknown-word marks (stars)
+-------------------------------------------------------
+*Edit distance: 366
+*Word error rate (WER): 76.25 %
+*Number of position-independent correct words: 176
+*Position-independent word error rate (PER): 63.33 %
 ==Work plan==

@@ Line 30: / Line 30: @@
 I have no non-GSoC plans for the summer and I can spend about 40-50 hours for a week
-==Initial data==
+==Initial data:Coding challenge==
 Evaluation of kaz-to-eng translation using:
     src set "apertium" (1 docs, 43 segs)

← Older revision		Revision as of 13:12, 21 March 2016
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	[[Category:GSoC ~~2014~~ Student proposals\|~~Aida~~]]		[[Category:GSoC 2016 Student proposals\|Aidana]]

@@ Line 8: / Line 8: @@
 These days the translating text automatically by using machine translation is very important, because it helps people from  whole world to understand and get information in foreign language very quickly and easy. Building machine translation systems is very interesting process, which needs to have and use your knowledge in programming, linguistics and languages, and in the result, by combining all these stuff, you could create machine translation system, which could be useful for many people.
-==Why is it that they are interested in the Apertium project?==
+==Why is it that you are interested in the Apertium project?==
 I have worked on Apertium platform since 2014, when I started to build Kazakh-English machine translation system. I chose this platform, because it is free and open-source, so I was able to use previously developed resources, such like Kazakh transducer and English-Kazakh bilingual dictionary.