Change Details

When translating, users have to correct some terms that are not properly translated by the Machine Translation (MT) service. For example, when translating John Carpenter article, the director's surname can be translated into whichever term is used in the local language for the "carpenter" profession. Since an article is about a specific topic there are chances that those mistakes need to be fixed by our users again and again. From our user testing sessions we have observed that while fixing it the first time is reasonable, users were negatively surprised that the system didn't learnt the lesson for the next time. While improving MT services is probably out of the scope for the project, it may be worth it to think in ways CX can save the user time in that process of correction. Some of these mechanisms can be also useful when there is no MT at all acting like a very basic (maybe at word level) MT-like system based on what you have already translated. # Proposed solution - Keep track of user corrections on MT that happens repeatedly. We need to decide how many times, how many words and how long they should be to consider them a correction. - Replace previous corrections when a paragraph is added if the corrected word is found. - Provide a way for users to switch among the alternatives (which include the MT proposed term and the one used in previous corrections). - Learn from the use of the alternatives to decide whether to apply corrections automatically or just suggest them. We'll illustrate the idea with the example of translating the Los Angeles article from Spanish to English. Since "angeles" means "angels" in Spanish, we'll assume that the MT service is going to translate the name of the city too literally, and the user corrects those in the first paragraph: |Initial translation with errors|User corrects the first paragraph| |---|---| |{F8615692}|{F8615694}| When adding the second paragraph (where the name of the city appears again), the system will replace it automatically from the proposed text by the MT based on the fact that the user has corrected it in previous paragraphs. In addition, the replaced text will be shown as a tag for the user to click and access the alternatives. this allows the user to undo the automatic change (or exposing other alternatives). |{F8615709}|{F8615711}| Whether to apply the replacement word or let the user do it will depend on previous decisions of the user for that word. If the user undoes an alternative that was applied on a paragraph, we can consider not using the replacement word in following paragraphs, and just highlight the MT version to let the user know they can pick an alternative manually. ___ A first step in this direction is providing alternative for link labels based on the target article title (T197662).

When translating, users have to correct some terms that are not properly translated by the Machine Translation (MT) service. For example, when translating John Carpenter article, the director's surname can be translated into whichever term is used in the local language for the "carpenter" profession. Since an article is about a specific topic there are chances that those mistakes need to be fixed by our users again and again. From our user testing sessions we have observed that while fixing it the first time is reasonable, users were negatively surprised that the system didn't learnt the lesson for the next time. While improving MT services is probably out of the scope for the project, it may be worth it to think in ways CX can save the user time in that process of correction. Some of these mechanisms can be also useful when there is no MT at all acting like a very basic (maybe at word level) MT-like system based on what you have already translated. # Proposed solution - Keep track of user corrections on MT that happens repeatedly. We need to decide how many times, how many words and how long they should be to consider them a correction. - Replace previous corrections when a paragraph is added if the corrected word is found. - Provide a way for users to switch among the alternatives (which include the MT proposed term and the one used in previous corrections). - Learn from the use of the alternatives to decide whether to apply corrections automatically or just suggest them. We'll illustrate the idea with the example of translating the Los Angeles article from Spanish to English. Since "angeles" means "angels" in Spanish, we'll assume that the MT service is going to translate the name of the city too literally, and the user corrects those in the first paragraph: |Initial translation with errors|User corrects the first paragraph| |---|---| |{F23794461}|{F23794464}| When adding the second paragraph (where the name of the city appears again), the system will replace it automatically from the proposed text by the MT based on the fact that the user has corrected it in previous paragraphs. In addition, the replaced text will be highlighted to communicate the user that a correction was applied automatically. This allows the user to undo the automatic change. |{F23795261}|{F23795266}| Whether to apply the correction automatically or let the user do it will depend on previous decisions of the user for that word. If the user undoes an alternative that was applied on a paragraph, we can consider not using the replacement word in following paragraphs, and just highlight the MT version to let the user know they can pick an alternative manually. In the example below, the correction is not applied automatically, only suggested for the user to apply: |{F23795356}|{F23795387}| ___ A first step in this direction is providing alternative for link labels based on the target article title (T197662).

When translating, users have to correct some terms that are not properly translated by the Machine Translation (MT) service. For example, when translating John Carpenter article, the director's surname can be translated into whichever term is used in the local language for the "carpenter" profession. Since an article is about a specific topic there are chances that those mistakes need to be fixed by our users again and again. From our user testing sessions we have observed that while fixing it the first time is reasonable, users were negatively surprised that the system didn't learnt the lesson for the next time. While improving MT services is probably out of the scope for the project, it may be worth it to think in ways CX can save the user time in that process of correction. Some of these mechanisms can be also useful when there is no MT at all acting like a very basic (maybe at word level) MT-like system based on what you have already translated. # Proposed solution - Keep track of user corrections on MT that happens repeatedly. We need to decide how many times, how many words and how long they should be to consider them a correction. - Replace previous corrections when a paragraph is added if the corrected word is found. - Provide a way for users to switch among the alternatives (which include the MT proposed term and the one used in previous corrections). - Learn from the use of the alternatives to decide whether to apply corrections automatically or just suggest them. We'll illustrate the idea with the example of translating the Los Angeles article from Spanish to English. Since "angeles" means "angels" in Spanish, we'll assume that the MT service is going to translate the name of the city too literally, and the user corrects those in the first paragraph: |Initial translation with errors|User corrects the first paragraph| |---|---| |{F8615692|{F23794461}|{F8615694}|23794464}| When adding the second paragraph (where the name of the city appears again), the system will replace it automatically from the proposed text by the MT based on the fact that the user has corrected it in previous paragraphs. In addition, the replaced text will be shown as a tag forhighlighted to communicate the user to click and access the alternativesthat a correction was applied automatically. thisThis allows the user to undo the automatic change (or exposing other alternatives). |{F8615709|{F23795261}|{F8615711}|23795266}| Whether to apply the replacement wordcorrection automatically or let the user do it will depend on previous decisions of the user for that word. If the user undoes an alternative that was applied on a paragraph, we can consider not using the replacement word in following paragraphs, and just highlight the MT version to let the user know they can pick an alternative manually. In the example below, the correction is not applied automatically, only suggested for the user to apply: |{F23795356}|{F23795387}| ___ A first step in this direction is providing alternative for link labels based on the target article title (T197662).