Why Use Translation Models In Thai Translation For Sentence Retrieval?
The main purpose of using translation models is with intent that the model itself will learn and lead to a higher probability of any word translating to itself. This is the major difference between passage retrieval for QA and machine translation. In case of machine translation it assumes that there is very little overlap in the terminologies of two languages. On the other hand in passage retrieval model the entire translation process depends heavily on such overlapping in terms and words of two different languages.
Estimation Of Probability
All modern and Certified Thai Translation Services By Professionals use this specific and useful formulation.
By this the probability of any word and phrase translating to itself is easily estimated. This is a fraction of translating to all other terms and words.
As it is essential that all the probabilities must add up to one, the use of this model will ensure that given any scope of other translations for a specific word the self-translation probability of that word or phrase will be less than 1.
This simple translation models will perform well for any sentence retrieval task.
Improvement On The Query
When the translation model is used it enables the translators to figure out the most basic of the alignments.
This helps in making significant improvement over the query and increases the likelihood of the baseline result.
The retrieval of sentences is therefore further improved as it smoothen the document probabilities.
It focuses more than just the collection of probabilities and helps in the future work.
All these features help in planning to investigate the use of relevant models along with cross-lingual relevance models.
Helps In Investigation
With better investigation ensured the model helps the translators to determine the effect of the size of the passage on such retrieval. It also helps in the investigation of different translation alignment algorithms in the future. All in all it will help in improving the estimation of the term probabilities. The translation model assigns probability to different pair of terms and words that does not appear together in a question and answer pair. Consequently, it produces more homogeneous results for a given question.
