Hybrid Machine Translation Approaches for Low-Resource Languages
| Thesis title in Czech: | Hybrid Machine Translation Approaches for Low-Resource Languages |
|---|---|
| Thesis title in English: | Hybrid Machine Translation Approaches for Low-Resource Languages |
| Key words: | Hybrid Machine Translation, Low-resource languages, English-to-Urdu |
| English key words: | Hybrid Machine Translation, Low-resource languages, English-to-Urdu |
| Academic year of topic announcement: | 2010/2011 |
| Thesis type: | diploma thesis |
| Thesis language: | angličtina |
| Department: | Institute of Formal and Applied Linguistics (32-UFAL) |
| Supervisor: | Mgr. Martin Popel, Ph.D. |
| Author: | hidden - assigned and confirmed by the Study Dept. |
| Date of registration: | 07.12.2010 |
| Date of assignment: | 07.12.2010 |
| Date and time of defence: | 06.09.2011 00:00 |
| Date of electronic submission: | 05.08.2011 |
| Date of submission of printed version: | 05.08.2011 |
| Date of proceeded defence: | 06.09.2011 |
| Opponents: | doc. RNDr. Vladislav Kuboň, Ph.D. |
| Guidelines |
| In recent years, corpus based machine translation systems produce significant results for a number of language pairs. However, for low-resource languages like Urdu the purely statistical or purely example based methods are not performing well. On the other hand, the rule-based approaches require a huge amount of time and resources for the development of rules, which makes it difficult in most scenarios. Hybrid machine translation systems might be one of the solutions to overcome these problems, where we can combine the best of different approaches to achieve quality translation.
The goal of the thesis is to explore different combinations of approaches and to evaluate their performance over the standard corpus based methods currently in use. This includes: 1. Use of syntax-based and dependency-based reordering rules with Statistical Machine Translation. 2. Automatic extraction of lexical and syntactic rules using statistical methods to facilitate the Transfer-Based Machine Translation. The novel element in the proposed work is to develop an algorithm to learn automatic reordering rules for English-to-Urdu statistical machine translation. Moreover, this approach can be extended to learn lexical and syntactic rules to build a rule-based machine translation system. |
| References |
| 1. Visweswariah et al. Syntax Based Reordering with Automatically Derived Rules for Improved Statistical Machine Translation. (2010)
2. Xu et al. Using a Dependency Parser to Improve SMT for Subject-Object-Verb Languages. (2009) 3. Eisele et al. Using Moses to Integrate Multiple Rule-Based Machine Translation Engines into a Hybrid System. (2008) 4. Probst. Learning Transfer Rules for Machine Translation with Limited Data. (2005) pp. 1-297 5. Lavie et al. Experiments with a Hindi-to-English Transfer-based MT System under a Miserly Data Scenario. (2003) pp. 1-21 6. Dolan et al. MSR-MT: The Microsoft Research Machine Translation System. (2002) |