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courses:rg:2012:encouraging-consistent-translation-bushra [2012/10/23 15:25]
jawaid |
courses:rg:2012:encouraging-consistent-translation-bushra [2012/10/23 15:28]
jawaid |
| ==Introduction:== | ====Introduction:==== |
| This paper emphasizes on using "one translation per discourse" heuristic in hierarchical phrase-based machine translation after getting motivated by "one sense per discourse" heuristic in Word Sense Disambiguation. A document (domain specific) is treated as a discourse unit in this paradigm. A novel approach of forced decoding is used to implement the heuristic in three different ways in machine translation system. Experiments are performed on Arabic-English and Chinese-English language pairs. | This paper emphasizes on using "one translation per discourse" heuristic in hierarchical phrase-based machine translation after getting motivated by "one sense per discourse" heuristic in Word Sense Disambiguation. A document (domain specific) is treated as a discourse unit in this paradigm. A novel approach of forced decoding is used to implement the heuristic in three different ways in machine translation system. Experiments are performed on Arabic-English and Chinese-English language pairs. |
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| Related Work: | ====Related Work:==== |
| | - Paper on the similar approach by Carput (2009) has found to be different in comparison to this work. They have used "one translation per discourse" approach as a post-processing step for MT whereas this works integrate the proposed scheme inside the decoding model of MT system. The later approach could also affect the selection of neighboring phrases whereas same doesn't hold for Carput (2009). |
| 1- Paper on similar approach by Carput (2009) has found to be different in comparison to this work. They have used "one translation per discourse" approach as a post-processing step for MT whereas this works integrate the proposed scheme inside the decoding model of MT system. The later approach could also affect the selection of neighboring phrases whereas same doesn't hold for Carput (2009). | |
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| 2- Without giving any proper evidence, authors have speculated that modeling "one-sense-per-discourse" is similar to the human translators' nature of making consistent word choices for specific domains. | |
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| Analysis: | - Without giving any proper evidence, authors have speculated that modeling "one-sense-per-discourse" is similar to the human translators' nature of making consistent word choices for specific domains. |
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| 1- Forced Decoding is the decoding method in which for a given pair of source and target sentences, decoder searches for the translation rules that fit the target sentence for a given source sentence. | ====Analysis:==== |
| | * Forced Decoding is the decoding method in which for a given pair of source and target sentences, decoder searches for the translation rules that fit the target sentence for a given source sentence. |
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| 2- Term "cases" represent situations in which multiple occurrences of a given source phrase (f) in a document (d) might be translated using more than one different translation rules resulting in different target translations. Such cases are shown in Table 1, numbers shown in column "Translation counts" are the number of sentences in which source phrase occurred. E.g. in 4th row, Korea=2 shows that word "Korea" is produced as target translation in 2 sentences whereas in last column same source phrase is translated as "the" in one sentence and as "which" in other sentence. | 2- Term "cases" represent situations in which multiple occurrences of a given source phrase (f) in a document (d) might be translated using more than one different translation rules resulting in different target translations. Such cases are shown in Table 1, numbers shown in column "Translation counts" are the number of sentences in which source phrase occurred. E.g. in 4th row, Korea=2 shows that word "Korea" is produced as target translation in 2 sentences whereas in last column same source phrase is translated as "the" in one sentence and as "which" in other sentence. |
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| Paper is nicely written and all experiments are well documented. We believe that consistent translation choices system is better suited only for translating from direction of morphologically-rich to morphologically-low language pairs but in case of reverse direction this approach can make serious errors by putting different morhological forms of the words bearing different meanings under the conistent translations. | Paper is nicely written and all experiments are well documented. We believe that consistent translation choices system is better suited only for translating from direction of morphologically-rich to morphologically-low language pairs but in case of reverse direction this approach can make serious errors by putting different morhological forms of the words bearing different meanings under the conistent translations. |
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