Lingala and Kikuyu/Structural Transfer

This page details transfer rules for translation between Lingala and Kikuyu

Pre-Evaluation

Below are the evaluation statistics for both the mini test corpus and the full parallel corpus:

lin → kik evaluation

Evaluation of mini corpus:

• WER: 87.50%
• PER: 87.50%
• Percentage unknown words: 0.0%
• Percentage known words: 100%

Evaluation of full corpus:

• WER: 99.06%
• PER: 94.34% (unknown-word marks removed)
• Percentage unknown words: 66.67%
• Percentage known words: 33.33%

kik → lin evaluation

Evaluation of mini corpus:

• WER: 80.00%
• PER: 80.00%
• Percentage of unknown words: 0.0%
• Percents of known words: 100%

Evaluation of full corpus:

• WER: 98.13%
• PER: 90.65% (unknown-word marks remove)
• Percentage of unknown words: 70.75%
• Percentage of known words: 29.25%

Implementation

lin → kik

We implemented one rule in the Lingala to Kikuyu translation direction to deal with basic verb transfer. It works by mapping Kikuyu tenses to Lingala tenses and adding a default aspect and noun class. The mapping of the Lingala negative particle te to a negative tag on the verb in the same sentence is done by the Lingala transducer using constraint grammar to add a negative tag to the Lingala verb whenever te appears in the same sentence. The sentence used in our second contrastive grammar point ((lin) ezalí malámu te → (kik) ti wega) can be used to illustrate this rule:

• Tagger: ^kozala<v><pres><p3><sp><nn><neg>$ ^malámu<adj>$ ^te<neg>$^.<sent>$
• Biltrans: ^kozala<v><pres><p3><sp><nn><neg>/rĩ<cop><pres><p3><sp><nn><neg>$ ^malámu<adj>/ega<adj>$ ^te<neg>/$^.<sent>/.<sent>$
• Chunker: ^verb<verb>{^rĩ<cop><pres><neg><p3><cl15><sg>$}$ ^default<default>{^ega<adj>$}$ ^sent<SENT>{^.<sent>$}$
• Interchunk: ^verb<verb>{^rĩ<cop><pres><neg><p3><cl15><sg>$}$ ^default<default>{^ega<adj>$}$ ^sent<SENT>{^.<sent>$}$
• Postchunk: ^rĩ<cop><pres><neg><p3><cl15><sg>$ ^ega<adj>$ ^.<sent>$
• Complete translation: ti wega

kik → lin

We implemented two rules in the Kikuyu to Lingala translation direction, one of which fixes a number of contrasts between the two languages. The first and simplest dealt with our first contrastive grammar point, which states that a locative tag on a noun in Kikuyu is realized as a preposition preceding the noun in the Lingala. The sentence used in our contrastive grammar can be used to illustrate the rule: (kik) kĩambĩrĩriainĩ → (lin) na ebandeli. The output is as follows:

• Tagger: ^ambĩrĩria<n><cl7><sg><loc>$^.<sent>$
• Biltrans: ^ambĩrĩria<n><cl7><sg><loc>/ebandeli<n><cl7><sg><loc>$^.<sent>/.<sent>$
• Chunker: ^noun_locative<loc>{^na<pr>$ ^ebandeli<n><cl7><sg>$}$^sent<SENT>{^.<sent>$}$
• Interchunk: ^noun_locative<loc>{^na<pr>$ ^ebandeli<n><cl7><sg>$}$^sent<SENT>{^.<sent>$}$
• Postchunk: ^na<pr>$ ^ebandeli<n><cl7><sg>$^.<sent>$
• Complete translation: na ebandeli

The other rule we implemented was a basic verb transfer rule. It works by mapping Kikuyu tenses to Lingala tenses, removing extraneous Kikuyu tags not used in Lingala, and adding the negative particle "te" at the end of a sentence for Kikuyu verbs that are tagged as negative. It also adds a default animacy tag to the Lingala verb. The sentence used in our second contrastive grammar point can be used to illustrate the rule: (kik) ti wega → (lin) ezalí malámu te. The output is as follows:

• Tagger: ^rĩ<cop><pres><neg><p3><cl5><sg>$ ^ega<adj>$^.<sent>$
• Biltrans: ^rĩ<cop><pres><neg><p3><cl5><sg>/kozala<v><pres><neg><p3><cl5><sg>$ ^ega<adj>/malámu<adj>$^.<sent>/.<sent>$
• Chunker: ^verb<verb>{^kozala<v><pres><p3><sp><nn>$}$ ^default<default>{^malámu<adj>$}$^negative<neg>{ ^te<neg>$}$^sent<SENT>{^.<sent>$}$
• Interchunk: ^verb<verb>{^kozala<v><pres><p3><sp><nn>$}$ ^default<default>{^malámu<adj>$}$^negative<neg>{ ^te<neg>$}$^sent<SENT>{^.<sent>$}$
• Postchunk: ^kozala<v><pres><p3><sp><nn>$ ^malámu<adj>$ ^te<neg>$^.<sent>$
• Complete translation: ezalí malámu te

Post-Evalatuion

lin → kik evaluation

Evaluation of mini corpus:

• WER: 75.00%
• PER: 75.00%
• Percentage unknown words: 0.00%
• Percentage known words: 100.00%

Evaluation of full corpus:

• WER: 99.06%
• PER: 94.34% (unknown-word marks removed)
• Percentage unknown words: 66.67%
• Percentage known words: 33.33%

kik → lin evaluation

Evaluation of mini corpus:

• WER: 50.00%
• PER: 50.00%
• Percentage of unknown words: 0.00%
• Percents of known words: 100.00%

Evaluation of full corpus:

• WER: 97.20%
• PER: 87.85% (unknown-word marks remove)
• Percentage of unknown words: 70.09%
• Percentage of known words: 29.91%