Predicting future translations

Patent metadata

Field	Value
Publication number	US-9747283-B2
Application number	US-201514981769-A
Country	US
Kind code	B2
Filing date	Dec 28, 2015
Priority date	Dec 28, 2015
Publication date	Aug 29, 2017
Grant date	Aug 29, 2017

Abstract

Technology is disclosed for snippet pre-translation and dynamic selection of translation systems. Pre-translation uses snippet attributes such as characteristics of a snippet author, snippet topics, snippet context, expected snippet viewers, etc., to predict how many translation requests for the snippet are likely to be received. An appropriate translator can be dynamically selected to produce a translation of a snippet either as a result of the snippet being selected for pre-translation or from another trigger, such as a user requesting a translation of the snippet. Different translators can generate high quality translations after a period of time or other translators can generate lower quality translations earlier. Dynamic selection of translators involves dynamically selecting machine or human translation, e.g., based on a quality of translation that is desired. Translations can be improved over time by employing better machine or human translators, such as when a snippet is identified as being more popular.

First claim

We claim:

1. A method for selecting a snippet for pre-translation comprising:
receiving a potential snippet;
computing a pre-translation score for the potential snippet by:
selecting translation likelihood factors for the received potential snippet, wherein the selected translation likelihood factors are associated with corresponding values, the corresponding values computed as an estimation of an effect the corresponding translation likelihood factor will have on an amount of translations or time of translation of the potential snippet; and
computing, as the pre-translation score, a combination of the values corresponding to the selected translation likelihood factors;

determining that the pre-translation score is above a threshold; and
in response to determining that the pre-translation score is above the threshold, identifying the potential snippet as a snippet that will be pre-translated.

2. The method of claim 1 , wherein the translation likelihood factors comprise one or more of:
characteristics of an author of the potential snippet;
characteristics of content of the potential snippet;
characteristics of an expected audience of the potential snippet;
a measure of user engagement with the potential snippet thus far;
an identification of when the potential snippet was created;
an amount of time before a translation of the potential snippet is expected to be needed;
a virtual location where the potential snippet is posted; or
any combination thereof.

3. The method of claim 2 , wherein the translation likelihood factors comprise the characteristics of the author of the potential snippet including one or more of:
languages the author of the potential snippet is identified as being facile with;
an age identified for the author of the potential snippet;
a gender identified for the author of the potential snippet;
one or more locations associated with the author of the potential snippet;
an occupation identified for the author of the potential snippet;
an education level identified for the author of the potential snippet;
a number of friends of the author of the potential snippet;
a number of friends of the author of the potential snippet who speak a language other than a language spoken by the author of the potential snippet; or
any combination thereof.

4. The method of claim 2 , wherein the translation likelihood factors comprise the characteristics of content of the potential snippet including one or more of:
an identified topic or area of interest of the potential snippet;
whether an identified topic or area of interest of the potential snippet is trending;
a length of the potential snippet;
a language identified as a source language of the potential snippet; or
any combination thereof.

5. The method of claim 2 , wherein the translation likelihood factors comprise the characteristics of the expected audience of the potential snippet, wherein the expected audience of the potential snippet is identified by determining historical viewership for one or more of:
snippets with similar topics or areas of interest as the potential snippet;
snippets with similar authors as the potential snippet;
snippets posted to similar virtual locations as the potential snippet; or
any combination thereof.

6. The method of claim 2 , wherein the translation likelihood factors comprise the characteristics of the expected audience of the potential snippet including one or more of:
gender makeup of the expected audience;
age makeup of the expected audience;
one or more locations associated with the expected audience;
languages associated with the expected audience;
a size of the expected audience;
one or more jobs associated with the expected audience;
friends of members of the expected audience;
education level makeup of members of the expected audience; or
any combination thereof.

7. The method of claim 2 , wherein the translation likelihood factors comprise a measure of user engagement with the potential snippet thus far, wherein the measure of user engagement is based on one or more of:
a total number of viewers of the potential snippet;
a total number of requests to translate the potential snippet;
a number of users who have actuated a control displayed in association with the potential snippet; or
any combination thereof.

8. The method of claim 2 , wherein the translation likelihood factors comprise the amount of time before the translation of the potential snippet is expected to be needed, wherein the amount of time before the translation of the potential snippet is expected to be needed is determined based on a computation of when users who are determined likely to request a translation of the snippet are also determined likely to interact with a system that provides translations of the snippet.

9. The method of claim 1 ,
wherein the receiving the potential snippet is a second time the potential snippet is received;
wherein the pre-translation score is a second pre-translation score; and
wherein the method further comprises, prior to receiving the potential snippet the second time:
receiving the potential snippet a first time;
computing a first pre-translation score for the potential snippet;
determining that the first pre-translation score is not above the threshold; and
in response to determining that the first pre-translation score is not above the threshold, identifying the potential snippet as a snippet that will not be pre-translated.

10. The method of claim 9 , wherein the first pre-translation score is computed as a lower score than the second pre-translation score based on computing the first pre-translation score for a first output language and computing the second a pre-translation score for a second output language, wherein the first output language and the second output language are different.

11. The method of claim 9 , wherein the first pre-translation score is computed as a lower score than the second pre-translation score based on:
computing the first pre-translation score for a first output language and computing the second a pre-translation score for a second output language, wherein the first output language and the second output language are the same; and
(A) the values corresponding to the selected translation likelihood factors selected for the first pre-translation score being different from (B) the values corresponding to the selected translation likelihood factors selected for the second pre-translation score.

12. A computer-readable storage medium storing instructions that, when executed by a computing system, cause the computing system to perform operations for pre-translation of snippets, the operations comprising:
receiving a potential snippet;
computing a pre-translation score for the potential snippet based on a selection of translation likelihood factors for the potential snippet, wherein the selected translation likelihood factors are associated with corresponding values, the corresponding values computed as an estimation of an effect the corresponding translation likelihood factor will have on an amount of translations or time of translation of the potential snippet; and
determining that the pre-translation score is above a threshold;
in response to determining that the pre-translation score is above the threshold, performing a translation of the potential snippet.

13. The computer-readable storage medium of claim 12 , wherein the translation likelihood factors comprise characteristics of an author of the potential snippet and/or characteristics of an expected audience of the potential snippet.

14. The computer-readable stora

Assignees

• Facebook Inc

Inventors

• Rottmann Kay
• Huang Fei
• Zhang Ying

Classifications

• G06F40/47

  Machine-assisted translation, e.g. using translation memory · CPC title

• G06F40/42

  Data-driven translation · CPC title

• G06F40/263 Primary

  Language identification · CPC title

• G06F40/58

  Use of machine translation, e.g. for multi-lingual retrieval, for server-side translation for client devices or for real-time translation · CPC title

• G06F40/51 Primary

  Translation evaluation · CPC title