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| Content Provider | ACM Digital Library |
|---|---|
| Author | Arora, Piyush Jones, Gareth J.F. Hosseinzadeh Vahid, Ali Liu, Qun |
| Abstract | Technical advances and its increasing availability, mean that Machine Translation (MT) is now widely used for the translation of search queries in multilingual search tasks. A number of free-to-use high-quality online MT systems are now available and, although imperfect in their translation behavior, are found to produce good performance in Cross-Language Information Retrieval (CLIR) applications. Users of these MT systems in CLIR tasks generally assume that they all behave similarly in CLIR applications, and the choice of MT system is often made on the basis of convenience. We present a set of experiments which compare the impact of applying two of the best known online systems, Google and Bing translation, for query translation across multiple language pairs and for two very different CLIR tasks. Our experiments show that the MT systems perform differently on average for different tasks and language pairs, but more significantly for different individual queries. We examine the differing translation behavior of these tools and seek to draw conclusions in terms of their suitability for use in different settings. |
| Starting Page | 859 |
| Ending Page | 864 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781450334730 |
| DOI | 10.1145/2740908.2743008 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2015-05-18 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Cross language information retrieval Machine translation Variable retrieval effectiveness |
| Content Type | Text |
| Resource Type | Article |
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