a) historical aspects: oral vs. written vs. mechanical
- significance / historical role of TR - contribution to & impact on:
b) TYPES: literary vs. non-literary
c) METHODS of ORAL TR: simultaneous vs. consecutive
d) FORM: oral (always non-literary) vs. written
e) medium in which TR is performed:
mechanical & computer-aided vs. human
- TLR as a linguistic person (knowledge, spatio-temporal restrictions)
- Sender, TLR, Receiver as linguistic persons in the communicative act
- TLR as a linguistic person in the communicative act:
- change as much as necessary - BUT –
- as little as possible
- always written and non-literary
- 50's & 60's – cold war (US/Russia)
- ASSUMPTION:
- computer - programmed to decode (SL) & encode (TL) !!!?
- equivalence between SL and TL (one-to-one correspondence)
- 1980-ies: initial success and promises (large investments - projects)
- human TLR - more efficient
- a procedure whereby a computer program analyses a source text and produces a target text without further human intervention.
- however, machine translation typically does involve human intervention, in the form of pre-editing and post-editing
- an exception to that rule:
- e.g., the translation of technical specifications (strings of technical terms and adjectives), using a dictionary-based machine-translation system.
- In regard to texts (e.g., weather reports) with limited ranges of vocabulary and simple sentence structure, machine translation can deliver results that do not require much human intervention to be useful.
- Also, the use of a controlled language, combined with a machine-translation tool, will typically generate largely comprehensible translations (AirSpeak)
- Relying on machine translation exclusively ignores the fact that
- communication in human language is context-embedded and that
- it takes a person to comprehend the context of the original text with a reasonable degree of probability.
- even purely human-generated translations are prone to error.
- such translations must be reviewed and edited by a human
- To date, machine translation — a major goal of natural-language processing — has met with limited success. [16]
- Machine translation has been brought to a large public by tools available on the Internet, such as AltaVista's Babel Fish, Babylon, and StarDict, Systran, Trados. These tools produce a "gisting translation" — a rough translation that "gives the gist" of the source text.
- With proper terminology work, with preparation of the source text for machine translation (pre-editing), and with re-working of the machine translation by a professional human translator (post-editing), commercial machine-translation tools can produce useful results, especially if the machine-translation system is integrated with a translation-memory or globalization-management system. [17]
- a sub-field of computational linguistics that investigates the use of computer software to translate text or speech from one natural language to another.
- At its basic level, MT performs simple substitution of words in one natural language for words in another.
- Using corpus techniques, more complex translations may be attempted, allowing for better handling of differences in linguistic typology, phrase recognition, and translation of idioms, as well as the isolation of anomalies.
- Current machine translation software often allows for customisation by domain (filters: field, subject matter)
- Current machine translation software often allows for customisation by profession (such as weather reports) — improving output by limiting the scope of allowable substitutions.
- particularly effective in domains where formal or formulaic language is used
- i.e. machine translation of government and legal documents more readily produces usable output than conversation or less standardised text
- Improved output quality can also be achieved by human intervention:
- E.g. some systems are able to translate more accurately if the user has unambiguously identified which words in the text are names.
- With the assistance of these techniques, MT has proven useful as a tool to assist human translators, and in some cases can even produce output that can be used "as is".
- However, current systems are unable to produce output of the same quality as a human translator, particularly where the text to be translated uses casual language
- computers are not human beings - THE NATURE OF TR. (AND HUMAN LANGUAGE) IS NOT AN ALGORITHMIC PROCESS:, esp.:
- 1. polysemy - on the lexical level
- 2. connotations, pragmatics etc. (siječanj - januar)
- 3. unable to account for changes in word order (syntax)
- 90's - in spite of taggers and parsers & semantic programs/ MT (translators) (whole blocks of language - now algorithmically available for TR
- UNABLE translate literary texts (esp. poetry)
- pre-translation procedure (computer-aided TR)
- raw material for human refinement
- even: voice recognition - automated transcripts of human speech
- restricted texts: institutional, legal, specific technical (operational / maintenance) instructions; scientific abstracts, etc.
- TR tools (dictionaries, glossaries, lexical & textual databases, wordnet, www)
- corpus linguistics etc.: COBUILD, BNC, Brown, LOB, etc.
- though practically still unusable (except in restricted languages) -
- MT important for the theory of TR: investigation of basic relationships in the process of TR
- algorithmic rigour of MT - clear linguistic descriptions
- investigation of cognitive processes and the process of human TR (brain)
- computers useful in helping humans (speed) in the translation activity rather than in translation itself
- very common and ever-present human activity
- interest in the nature of the process of TR
- what happens in the translator's brain (Think-aloud protocols, Translog)
- assessment of the product of TR, criticism
- human brain - inaccessible for investigation (psycholinguistics) - only results are accessible and available for research - indirect conclusions
- for teaching purposes
- an exciting new field in translation - a growing professional demand
- dubbing and voice-over
- surtitling and subtitling
- http://ics.leeds.ac.uk/papers/llp/exhibits/16/IntroAVTranslation_Adriana_Serban.ppt#257,2,Talk map
- Audiovisual translation (AVT) - subtitling and dubbing:
- one of the commonest forms of translation encountered in everyday life in contemporary societies
- of the 8,108 hours of programming broadcast by the Finnish broadcasting company YLE in 1996, 48% consisted of foreign-language programmes (including re-runs) (Kontula, Larma and Petäinen 1997:52-53).
- The visibility of AVT is probably one reason why AVT also lends itself to easy and occasionally sharp criticism among viewers
- "subtitles offer the pretext for a linguistic game of 'spot the error'" for those viewers who have a command of both (Shochat and Stam 1985:46)
- Internet sites devoted to listing subtitling gaffes, e,g, Turun Sanomat 5.7.1998
- It is interesting that in a sense AVT has been a channel for venting ideas on linguistic purism for quite a long while
- E.g.: an angry viewer had written to the editor complaining about the quality of a subtitling in a film. (Paunonen 1996:549):
- he demanded that distributors should take action to improve the quality of translations, or else censorship should intervene.
- http://ethesis.helsinki.fi/julkaisut/hum/engla/pg/jaskanen/ch2.html
- the intellectual activity of facilitating oral and sign-language communication, either simultaneously or consecutively, between two, or among three or more, speakers who neither speak nor sign the same source language.
- Functionally, interpreting and interpretation are the descriptive words for the activity;
- Functionally, an interpreter orally translates a source language to a target language; likewise in sign language
- The interpreter's function is conveying every semantic element (tone and register) and every intention and feeling of the message that the source-language speaker is directing to the target-language listeners
- Computer-assisted translation (CAT), also called computer-aided translation or machine-aided human translation (MAHT), is a form of translation wherein a human translator creates a target text with the assistance of a computer program. The machine supports a human translator.
- Computer-assisted translation can include standard dictionary and grammar software. The term, however, normally refers to a range of specialized programs available to the translator, including translation-memory, terminology-management, concordance, and alignment programs.
- Computers are used in many aspects of modern translation (particularly of technical texts).
- Note: a segment is a coherent piece of text larger than a term, usually a sentence.
- General translation & interpretation
- Specialized translation & interpretation
- the translation or interpretation of non-specific language that does not require any specialized vocabulary or knowledge
- However, the best translators and interpreters read extensively in order to be up-to-date with current events and trends so that they are able to do their work to the best of their ability, having knowledge of what they might be asked to convert
- good translators and interpreters make an effort to read about whatever topic they are currently working on
- refers to domains which require at the very least that the person be extremely well read in the domain.
- training in the field (such as a college degree in the subject, or a specialized course in that type of translation or interpretation)
- common types of specialized translation:
- financial translation and interpretation
- legal translation and interpretation
- literary translation
- medical translation and interpretation
- scientific translation and interpretation
- technical translation and interpretation
- For legal and official purposes, evidentiary documents and other official documentation are usually required in the official language(s) of that jurisdiction. In some countries, it is a requirement for translations of such documents that a translator swear an oath to attest that it is the legal equivalent of the source text. Often, only translators of a special class are authorized to swear such oaths. In some cases, the translation is only accepted as a legal equivalent if it is accompanied by the original or a sworn or certified copy of it
- The procedure for translating to legal equivalence differs from country to country
- South Africa the translator must be authorized by the High Court, and (s)he must use an original (or a sworn copy of an original) in his physical presence as his source text; the translator may only swear by his own translation; there is no requirement for an additional witness (such as a notary) to attest to the authenticity of the translation.
- Croatia: registered by the court; formal qualifications and exam
- In the case of Mexico, some local instances, such as the High Superior Court of Justice, establish that a written and oral examination should be taken for a translator to be recognized as an expert or "sworn" / “certified” translator (this kind of translator does not swear before the court to be authorized). http://www.tsjdf.gob.mx/iej/peritos.html)
- Even if a translator specializes in legal translation or if (s)he is a lawyer in his country, this does not necessarily make him a sworn translator
- The infrastructure for a translation environment is not necessarily translation-specific, but the importance of infrastructure becomes even more important in multilingual situations.
- Elements of the infrastucture need to be as integrated as possible, both among themselves and with the actual translation process.
- The elements of the infrastructure are:
- Document creation/management system
- Terminology database
- Telecommunications (intranet/Internet, e-mail, ftp, web browsing, etc.)
- Term candidate extraction and terminology research. Term candidate extraction and terminology research are used to determine what words might be candidates for inclusion in a term base.
- After a source language term is identified, by candidate extraction or some other process, terminology research is needed to find an appropriate term in the target language to designate the concept.
- Terminology research can draw on many resources, including the
- Internet and multilingual text databases.
- The term candidate extraction goes beyond what a spell checker can do by identifying candidates for new multi-word terms.
- if we assume that the sentences in the bitext on the next page were part of a large text, and that thermal layer were not already in the termbase an extraction tool should propose it as a candidate term,
- even if both thermal and layer were already in the termbase as individual words.
- Automatic terminology lookup:
- could be thought of as the term level equivalent of machine translation. For example, in the bitext on the next page the
- words thermocline and thermal layer might be considered terms that should always be translated consistently.
- Automatic terminology lookup would display the preferred target language term (gradiente térmico and capa térmica in these cases)
- Without the translator having to look the terms up manually.
- As each segment of source receives the focus,
- preferred target language terms are displayed and the human translator can quickly incorporate them into the target text without risk of misspelling.
- Automatic terminology lookup supports terminological consistency for all text types.
- Terminology consistency check and non-allowed terminology check.
- Terminology consistency checkers verify consistent use of terminology after a translation has been completed;
- i.e., they make sure that each term is translated consistently, wherever it occurs.
- For example, if the preferred term for thermocline is gradiente térmico and a human translator, for whatever reason, returns termoclino, a terminology consistency checker would detect this inconsistent use and flag the term for human attention.
- Non-allowed terminology checkers flag terms which are not allowed (as in the case of deprecated terms) and bring them to the attention of a human.
- New text segmentation, previous source-target text alignment, and indexing.
- The preparation of an aligned, indexed source-target bitext is vital for the correct functioning of translation memory tools if previously translated text is to be leveraged (re-used).