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Introduction

Root: /* References */

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References

Root: /* Further reading */

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'''Machine Translation - A challenge or a chance for human translators?'''

[[Machine_translation|Overview Page of Machine Translation]]

30 Chapters（0/30)

[[Machine_Trans_EN_1]] [[Machine_Trans_EN_2]] [[Machine_Trans_EN_3]] [[Machine_Trans_EN_4]] [[Machine_Trans_EN_5]] [[Machine_Trans_EN_6]] [[Machine_Trans_EN_7]] [[Machine_Trans_EN_8]] [[Machine_Trans_EN_9]] [[Machine_Trans_EN_10]] [[Machine_Trans_EN_11]] [[Machine_Trans_EN_12]] [[Machine_Trans_EN_13]] [[Machine_Trans_EN_14]] [[Machine_Trans_EN_15]] [[Machine_Trans_EN_16]] [[Machine_Trans_EN_17]] [[Machine_Trans_EN_18]] [[Machine_Trans_EN_19]] [[Machine_Trans_EN_20]] [[Machine_Trans_EN_21]] [[Machine_Trans_EN_22]] [[Machine_Trans_EN_23]] [[Machine_Trans_EN_24]] [[Machine_Trans_EN_25]] [[Machine_Trans_EN_26]] [[Machine_Trans_EN_27]] [[Machine_Trans_EN_28]] [[Machine_Trans_EN_29]] [[Machine_Trans_EN_30]] ...

[[Book_projects|Back to translation project overview]]

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 <center>Muhammad Saqib Mehran, Hunan Normal University, China</center>
 ==Introduction==
 In this chapter we will look at some of the particular problems that translation task
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 there is no trivial problem in creating a description that is accurate enough to be used by an automated system.
-'''Ambiguity'''
+==Ambiguity==
 In the high-quality of all feasible worlds(as some distance as maximum Natural Language Processing is concerned, anyway) each phrase could have one and simplest one meaning. But, as all of us know, that is not the case. When a phrase has a couple of meaning, it's miles stated to be lexically ambiguous. When a word or sentence may have a couple of shape it's miles stated to be structurally ambiguous.
@@ Line 34: / Line 36: @@
 Ambiguity is a pervasive phenomenon in human languages. It could be very tough to locate words that aren't at the least methods ambiguous, and sentences which are (out of context) several methods ambiguous are the rule, now no longer the exception. This isn't simplest tricky because a number of the options are unintended (i.e. constitute incorrect interpretations), however because ambiguities ‘multiply’. In the worst case, a sentence containing words, every of which is methods ambiguous can be 4 methods.
-.	(a) Do not use abrasive cleaners on the printer cabinet.
+===1. (a) Do not use abrasive cleaners on the printer cabinet.===
 (b) The use of abrasive cleaners on the printer housing is not recommended.
 In the first sentence the usage is a verb and in the second a noun, that is, we have a case of lexical ambiguity. An English-French dictionary says that the verb can be translated by (ua) to operate by and employers, while the noun is translated with employment or use.
@@ Line 46: / Line 47: @@
 In order for the machine to choose the correct interpretation, we must give it information about the meaning dangerous, since applying a grammar to a sentence can produce a number of different parsees depending on how the rules are applied, and we may end up with one get large number of alternate parses on a single sentence Ambiguity can match true meaning ambiguity, but very often it does not, and it is when it does. not that we want to eliminate by applying information approximately meaning. We can illustrate this with a few examples. First, allow us to display how grammar rules, differ- ently applied, can produce multiple syntactic evaluation for a sentence. One manner this can arise is in which a phrase is assigned to multiple class with inside the grammar. For example, anticipate that the phrase cleansing is each an adjective and a verb in our grammar. This will permit us to assign one of a kind analyses to the subsequent sentence.
-(2) Cleaning fluids may be dangerous
+===(2) Cleaning fluids may be dangerous===
 One of those analyses may have cleansing as a verb, and one may have it as an adjective. In the former (much less plausible) case the experience is ‘to smooth a fluid can be dangerous’, i.e. it's miles approximately an pastime being dangerous. In the latter case the experience is that fluids used for cleansing may be dangerous. Choosing among those opportunity syntactic analyses requires information approximately meaning. It can be really well worth noting, in passing, that this ambiguity disappears while can is changed through a verb which suggests wide variety settlement through having one of a kind paperwork for 1/3 character singular and plural. For example, the subsequent aren't ambiguous on this manner: (3a) has most effective the experience that the movement is dangerous, (3b) has most effective the experience that the fluids are dangerous.
-(3) a. Cleaning fluids is dangerous.
+===(3) a. Cleaning fluids is dangerous. ===
 b. Cleaning fluids are dangerous.
@@ Line 62: / Line 63: @@
 (6) Put the paper with inside the printer. Then transfer it on. In order to training session that it's miles the printer this is to be switched on, instead of the paper, one desires to apply the expertise of the arena that printers (and now no longer paper) are the type of issue one is probable to replace on. There are different instances in which actual international expertise, aleven though necessary, does now no longer appear to be sufficient. The following, in which humans are re-assembling a printer, appears to be such an example:
-'''Lexical and Structural Mismatches'''
+==Lexical and Structural Mismatches==
 At the begin of the preceding phase we stated that, with inside the first-class of all feasible worlds for NLP, each phrase might have precisely one sense. While that is real for maximum NLP, it's miles an exaggeration as regards MT. It might be a higher world, however now no longer the first-class of all feasible worlds, due to the fact we'd nonetheless be confronted with tough translation troubles. Some of these troubles are to do with lexical variations among languages — variations with inside the ways wherein languages appear to categorize the world, what standards they select to explicit by unmarried phrases, and which they select now no longer to lexicalize. We will examine a number of these directly. Other troubles stand up due to the fact extraordinary languages use extraordinary systems for the identical purpose, and the identical shape for extraordinary purposes. In both case, the end result is that we need to complicate the interpretation process. In this phase we can examine some consultant examples. Examples just like the ones in (7) under are acquainted to translators, however the examples of colors (7c), and the Japanese examples in (7d) are in particular striking. The latter due to the fact they display how languages want vary now no longer best with admire to the fineness or ‘granularity’ of the differences they make, however additionally with admire to the idea for the distinction: English chooses extraordinary verbs for the action/occasion of placing on, and the action/nation of wearing. Japanese does now no longer make this distinction, however differentiates in line with the item that is worn. In the case of English to Japanese, a reasonably easy check at the semantics of the NPs that accompany a verb can be enough to determine at the proper translation. Some of the color examples are similar, however extra generally, research of color vocabulary shows that languages really carve up the spectrum in alternatively extraordinary ways, and that selecting the first-class translation might also additionally require know-how that is going nicely past what's in the text, and might also be undecidable. In this sense, the interpretation of color terminology starts offevolved to resemble the interpretation of phrases for cultural artifacts (e.g. phrases like English cottage, Russian dacha, French chateau ˆ , etc. for which no good enough translation exists, and for which the human translator need to determine among instantly borrowing, supplying an explanation). In this area, translation is a actually innovative act1 , which is properly past the ability of modern computers.
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 (8) a. Satoo-san wa shyushoo ni erabaremashita.
 Satoo-hon TOP Prime Minister in changed into-elected.
@@ Line 131: / Line 131: @@
 The trouble with idioms, in an MT context, is that it isn't commonly viable to translate them the usage of the everyday guidelines. There are exceptions, as an example take the bull through the horns (that means ‘face and address a problem with out shirking’) may be translated actually into French as prendre le taureau par les cornes, which has the identical that means. But, for the maximum part, using everyday guidelines with a view to translate idioms will bring about nonsense. Instead, one has to deal with idioms as unmarried gadgets in translation. In many cases, a herbal translation for an idiom can be a unmarried phrase — as an example, the French phrase mourir (‘die’) is a probable.
-−
 =Conclusion=
 This bankruptcy seems at a few troubles which face the builder of MT systems. We charac- terized them as troubles of ambiguity (lexical and syntactic) and troubles of lexical and structural mismatches. We noticed how one of a kind styles of linguistic and non-linguistic knowl- part are essential to remedy troubles of ambiguity, and withinside the subsequent bankruptcy we examine in greater element a way to constitute this knowledge. In this bankruptcy we mentioned times of lexical and structural mismatches and the trouble of non-compositionality (as exempli- fied via way of means of idioms and collocations) and checked out a few techniques for handling them in MT systems.

@@ Line 156: / Line 156: @@
 ==References==
 Kay (1969)
 Katz (1978)
 Keenan (1978)

← Older revision		Revision as of 21:25, 8 December 2021
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	The discussion in this chapter touches on two issues of general linguistic and philosophical interest: to what extent human languages really do carve the world up differently, and whether there are some sentences in some languages which cannot be translated into other languages. As regards the first question, it seems as though there are some limits. For example, though languages carve the colour spectrum up rather differently, so there can be rather large differences between colour words in terms of their extensions, there seems to be a high level of agreement about ‘best instances’. That is, though the extension of English red, and Japanese akai is different, nevertheless, the colour which is regarded as the best instance of red by English speakers is the colour which is regarded as the best instance of akai by Japanese speakers. The seminal work on this topic is Berlin and Kay (1969), and see the title essay of Pullum (1991). The second question is sometimes referred to as the question of effability, see Katz (1978); Keenan (1978) for relevant discussion.		The discussion in this chapter touches on two issues of general linguistic and philosophical interest: to what extent human languages really do carve the world up differently, and whether there are some sentences in some languages which cannot be translated into other languages. As regards the first question, it seems as though there are some limits. For example, though languages carve the colour spectrum up rather differently, so there can be rather large differences between colour words in terms of their extensions, there seems to be a high level of agreement about ‘best instances’. That is, though the extension of English red, and Japanese akai is different, nevertheless, the colour which is regarded as the best instance of red by English speakers is the colour which is regarded as the best instance of akai by Japanese speakers. The seminal work on this topic is Berlin and Kay (1969), and see the title essay of Pullum (1991). The second question is sometimes referred to as the question of effability, see Katz (1978); Keenan (1978) for relevant discussion.
		+
		+	==References==
		+	Kay (1969)
		+	Katz (1978)
		+	Keenan (1978)

Machine Trans EN 5 - Revision history

Root: /* Introduction */

Root: /* References */

Root: /* Further reading */

Root at 21:21, 8 December 2021

Root: Created page with "'''Machine Translation - A challenge or a chance for human translators?''' Overview Page of Machine Translation 30 Chapters（0/30) Machine_Trans_..."