Agenda

1. Error Analysis of Rule-based Machine Translation OutputsA Case Study on English – Persian MT System تجزیه و تحلیل خطا از حکومت مبتنی بر خروجی دستگاه ترجمه؟ مطالعه موردی در زبان انگلیسی - فارسی سیستم های MT Zahra Pourniksefat Islamic Azad University – Science & Research Branch

3. Agenda • Introduction • Machine Translation Overview • Evaluation of MT systems • Methods & Materials • Error Categories & Description • Results & Discussion

4. Machine Translation Overview Definition: The term Machine Translation (MT) is used for translating text or speech from one natural language to another by using computers and software. • Systran: MT is much fasterthanhumantranslatorsbecause it is much cheaper and has a better memory than human translators. • Shahba2002 believed that “It’s better to spend our time on the actualactoftranslation rather than typing the English text or scanning it for the MT to translate. Efforts in MT are by themselves valuable as they at least satisfy one of the needs of human beings: need for innovation and discovery” • MT is more economic on time and money, but it is lessaccurate than human translators (Frederking, 2004).

5. Why MT matters? According to Hatim and Mundayit’s an important topic - socially, politically, commercially, scientifically, and intellectually or philosophically (2004) • The social or politicalimportance of MT arises from the socio- political importance of translation in communities where more than one language is generally spoken. So translation is necessary for communication- for ordinary human interaction, and for gathering the information one needs to play a full part in society. • The commercialimportance of MT is a result of related factors. First, translation itself is commercially important. Second, translation is expensive. • Scientifically, MT is interesting, because it is an obvious application and testing ground for many ideas in Computer Science, Artificial Intelligence, and Linguistics. • Philosophically, MT is interesting, because it represents an attempt to automate an activity that can require the full range of human knowledge.

6. Some Misconceptions about MT MT is a waste of time because you will never make a machine that can translate Shakespeare. This criticism that MT systems cannot translate Shakespeare is a bit like the criticism of industrial robots for not being able to dance.(Hatim and Munday, 2004) • First, translating literature requires special literary skills – it is not the kind of thing that the average professional translators normally attempt • Second, literary translation is a small proportion of the translation that has to be done. • Finally, one may wonder who would ever want to translate Shakespeare by machine – it’s a job that human translators find challenging and rewarding, and it’s not a job that MT systems have been designed for.

7. Approaches to MT • Direct Machine Translation Approach The first developed MT systems where a word–for–word translation from the source language to the target language is performed. • Transfer Machine Translation Approach • The analysis stage that is the direct strategy which takes benefits of a dictionary in source language to demonstrate the source language from linguistic point of view. • The transfer stage varies the outputs of the analysis stage to produce structural and linguistic equivalents between the two languages. • The generation stage is the third stage in which a target language dictionary is applied to result the target language document on the basis of linguistic information. (Steiner, 1988) • Interlingua Machine Translation Approach • First the source text meaning is decoded • Second the resulted meaning is re-encoded in the target language

8. Approaches to MT cont’d. • Rule-based Machine Translation Approach It operates on the linguisticdata on source and target languages fundamentally taken from bilingual dictionaries and the basic semantic, morphological, and syntacticgrammarof the individual language (Gelbukh, 2011). Minimally, to get a Persian translation of English sentence one needs: • A dictionary that will map each English word to an appropriate Persian word. • Rules representing regular English sentence structure • Rules representing regular Persian sentence structure • And finally, we need rules according to which one can relate these two structures together.

9. Approaches to MT cont’d. • Statistical Machine Translation Approach This system uses a corpus or database as a translated example for analyzing and decoding source language. In comparison with the machine translation of about three decades ago, GoogleTranslate as an example of more contemporary automated engine for the task of translation has taken a giant leap. However, it is still too imperfect. (Nierenberg, 1998) • Hybrid Machine Translation Approach • Rules post-processed by statistics in which translation are practiced on the pivot of rule-based engine. Next statistics are applied to correct the output. • Statistics guided by rules in which rules have an important role to pre-process date to quite the statistical representation to normalize. This approach is powerful, flexible and under more control when it’s translating.

10. Evaluation of MT Systems • Human translation assessment (Secară 2005; Williams 2001) has been moving from microtextual, word- or sentence-level error analysis methods toward more macrotextualmethods focused on the function, purpose and effect of the text. At the same time, machine translation assessment has mainly been microtextual and focused on the aspects of accuracy and fluency. • Hovy (2002) discussed the complexity of MT evaluation, and stressed the importance of adjusting evaluation to the purpose and context of the translation.

11. Evaluation of MT Systems cont’d. Mary A. Flangan Believed that Machine translation quality can be difficult to quantify for a number of reasons: • A text can have several different translations, all of which are correct. • Defining the boundaries of errors in MT output is often difficult. Errors sometimes involve only single words, but more often involve phrases, discontinuous expressions, word order or relationships across sentence boundaries. Therefore, simply counting the number of wrong words in the translation is not meaningful. • One error can lead to another. For example, if the part of speech of a word is identified incorrectly by the MT software, the entire analysis of the sentence may be affected, creating a chain of errors. • The cause of errors in MT output is not always apparent. The evaluator usually does not have access to a trace of the software's tests and actions. Thus it can be difficult to identify what went wrong in the translation of a sentence.

12. Evaluation of MT Systems cont’d. Types of Evaluation • Automatic Evaluation the Word Error Rate (WER), the Position independent word Error Rate (PER), the BLEU (Papineni et al., 2002) and the NIST (Doddington, 2002) where the MT output is compared to one or more human reference translations. • Human Evaluation Due to the complexity of natural language, manual evaluation seems more reliable • Three passages were selected and translated by Rule-based MT Systems and compared with one Statistical MT System and Human translator • Error categories were derived after the analysis of each text

13. Methods & Materials • Three passages were translated by two different MT systems and also a human translator. • From each text type a passage of approximately 400 words was taken from story, user guide and magazine. • The rule-based MT – Arya TM– system was designed based on thousands of lexical and grammatical rules. • The statistical system, Google Translate by Google Inc., is based on the use of large monolingual and parallel corpora for translation. • The unit of analysis was set to a sentence level because it’s the largest unit which can be easily recognized in MT systems and ST sentence can be clearly corresponded to its TT pairs. Table of Source Text Passages for Analysis

14. Error Categories & Descriptions • For English-to-Persian Rule- based MT systems the following categories were derived

15. Error Categories & Descriptions cont’d. Description of Error Categories: • Syntactic Errors: Those errors that are related to the grammar of the language such as parts of speech or conjugation Word order that means sentence elements ordered incorrectly Example: Commands generally take the form of buttons and lists. (User Guide) Missing words: incorrect elision of some words Example: This requires better data collection and analysis tools for studying outcomes and consistent use of these tools across individual studies. (Magazine)

16. Error Categories & Descriptions cont’d. Unknown words: word not in a dictionary Example: The women, wearing faded house dresses and sweaters, came shortly after their menfolk.( Story) Punctuation: incorrect punctuation Example: The children assembled first, of course. (Story)

17. Error Categories & Descriptions cont’d. Parts of speech: errors in identifying pars of speech such as noun or verb Example: If you decrease the width of the ribbon, small button labels disappear. (User Guide) Conjugation: incorrectly formed verb or wrong tense Example: Soon the women, standing by their husbands, began to call to their children, and the children came reluctantly, having to be called four or five times.

18. Error Categories & Descriptions cont’d. • Semantic Errors: Those errors that are related to the meaning such as incorrect meaning of words or expressions which caused the incorrect meaning of the whole sentence. Incorrect word: completely incorrect meaning Polysemy: incorrect selection of the meaning of the words with more than one meaning Example: The people of the village began to gather in the square, between the post office and the bank, around ten o'clock. Style and idiomatic expression : incorrect translation of multi-word expression Example: They greeted one another and exchanged bits of gossip as they went to join their husbands.

19. Results & Discussions Table of Syntactic Errors

20. Results & Discussions contd. Table of Semantic Errors

21. Results & Discussions cont’d. • Both systems made the least errors with the simplersentencesand the most ones with the compound-complexsentences, as well as lexically or structurally ambiguous texts. This is because ambiguous source texts with different contents can correspond with more than one representation. • For the rule-based system, the most typical errors are in conjugation, wordorderand also in rendering polysemous wordsand idiomatic expressions. For the statistical system the most common error is in conjugating and determiningthetense. However, it has also some problems in translating words with multiple meaning and idiomatic expression. • To see whether machine translation accuracy is affected by text-type three different genres were analyzed thoroughly. And for the different text types, the rule- based system had similar amounts of syntactic and semantic errors in each text.

22. Future! • Evaluating MT quality is necessarily a subjective process because it involves human judgments. • Determining the best category for an error in MT output is not easy because we have to place them on how they are realized rather than the cause of errors and many machine translated sentences contained multiple, linked errors. • Future work will therefore be focused on the cause of errors and ranking error categories. The error categories presented here is flexible, allowing for the deletion or addition of morecategories.