| { | |
| "paper_id": "1996", | |
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| "generated_with": "S2ORC 1.0.0", | |
| "date_generated": "2023-01-19T02:05:56.550571Z" | |
| }, | |
| "title": "AN EXAMPLE-BASED APPROACH TO MACHINE TRANSLATION", | |
| "authors": [ | |
| { | |
| "first": "Brona", | |
| "middle": [], | |
| "last": "Collins", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "bcollins@cs.tcd.ie" | |
| }, | |
| { | |
| "first": "Padraig", | |
| "middle": [], | |
| "last": "Cunningham", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Tony", | |
| "middle": [], | |
| "last": "Veale", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
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| "abstract": "In this paper we describe a methodological analysis of EBMT (Example-Based Machine Translation) based on a CBR (Case-Based Reasoning) perspective. This analysis focuses on adaptation. We argue that, just as in CBR, the overall power of an EBMT system is its ability to adapt examples retrieved to suit the new problem translation. Here we describe a technique whereby reusability is a function of the abstract 'adaptability' information stored in the cases. This information is exploited during both the adaptation and retrieval stages.", | |
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| "paper_id": "1996", | |
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| "text": "In this paper we describe a methodological analysis of EBMT (Example-Based Machine Translation) based on a CBR (Case-Based Reasoning) perspective. This analysis focuses on adaptation. We argue that, just as in CBR, the overall power of an EBMT system is its ability to adapt examples retrieved to suit the new problem translation. Here we describe a technique whereby reusability is a function of the abstract 'adaptability' information stored in the cases. This information is exploited during both the adaptation and retrieval stages.", | |
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| "section": "Abstract", | |
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| "text": "Example Based Machine Translation (EBMT) is the marriage of MT and Case Based Reasoning (CBR) techniques (see Nagao 1984; Sumita 1995) . It involves translating the source language (SL) into the target language (TL) via remindings from previous translation cases. Because of the sparse data problem in EBMT (Gale and Church, 1993; Moonjoo 1995) an EBMT system must have a sophisticated adaptation mechanism so that the previous translation may be suitably adapted to suit the new sentence at hand. However, adaptation is complicated by translation divergences (Dorr 1993) which cause the translation example to be brittle in reuse. This means that a translation example involving a target language segment that is structurally different to the source language segment can only be adapted with great care. What is required is a retrieval policy whereby the extent to which a previous translation may be adapted is made explicit and used as a deciding factor for choosing between several possible example translations. In CBR research, such a policy is called adaptation guided retrieval (AGR). An AGR policy in EBMT is useful because it helps retrieve cases that can be adapted safely. In this paper we present an approach to EBMT that quantifies this concept of 'safety' and makes it a key criterion in case selection. We describe ReVerb, an EBMT system that implements this idea. We illustrate that ReVerb retrieves adaptable cases in the translation of software manuals from English to German.", | |
| "cite_spans": [ | |
| { | |
| "start": 110, | |
| "end": 121, | |
| "text": "Nagao 1984;", | |
| "ref_id": "BIBREF11" | |
| }, | |
| { | |
| "start": 122, | |
| "end": 134, | |
| "text": "Sumita 1995)", | |
| "ref_id": null | |
| }, | |
| { | |
| "start": 307, | |
| "end": 330, | |
| "text": "(Gale and Church, 1993;", | |
| "ref_id": "BIBREF6" | |
| }, | |
| { | |
| "start": 331, | |
| "end": 344, | |
| "text": "Moonjoo 1995)", | |
| "ref_id": "BIBREF10" | |
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| "start": 560, | |
| "end": 571, | |
| "text": "(Dorr 1993)", | |
| "ref_id": "BIBREF5" | |
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| "section": "Introduction", | |
| "sec_num": "1." | |
| }, | |
| { | |
| "text": "In section 2 we provide an overview of EBMT and present our model of EBMT that emphasises the transformations in EBMT that dictate the adaptability of a translation case in a particular context. In section 3 we describe the overall architecture of ReVerb and in section 4 we describe how adaptation-guided retrieval works in ReVerb. In section 5 we illustrate how ReVerb adapts cases, followed by an evaluation in section 6. ", | |
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| "section": "Introduction", | |
| "sec_num": "1." | |
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| "text": "Natural language translation can be seen as a heavily memory-dependent problem, especially when the text at hand is a sublanguage (a subset of language which is specific to one domain, e.g. the language of software documentation). Professional translators often get the feeling that much of their skill is wasted on translating very repetitive passages of text. The motivation for EBMT is thus clear: when faced with a novel sentence, chances are that the expert translator will have translated something very similar before, will recognise this (matching and retrieval), and will make the changes necessary (adaptation) to produce a good translation. Approaches differ with respect to the amount of linguistic processing performed on the example texts. At one end of the spectrum are string-based approaches (Nirenburg 1995; MacLean 1993) in which examples are retrieved on the basis of string similarity only and the input is covered by combining substrings retrieved from different cases, giving preference to longer substrings. Combining substrings in this linguistically unprincipled manner inevitably leads to the problem of 'boundary friction'-a well recognised problem in EBMT literature, but the technique has the advantage that a huge data base of examples can be built easily. Other approaches incorporate shallow syntactic indexing (Juola 1994; Cranias 1995) and then use clustering techniques or simulated annealing during retrieval. At the linguistic end of the scale, language is usually represented in tree forms which typically represent the 'dependency relations' between syntactic constituents and then tree operations (e.g. deleting and replacing subtrees) are performed in order to cover the input string (Sato 1990 ). The surrounding context of the subtree is taken into account, along with the number of tree operations which the template must undergo, when choosing candidate translation examples.", | |
| "cite_spans": [ | |
| { | |
| "start": 809, | |
| "end": 825, | |
| "text": "(Nirenburg 1995;", | |
| "ref_id": "BIBREF12" | |
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| "start": 826, | |
| "end": 839, | |
| "text": "MacLean 1993)", | |
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| { | |
| "start": 1344, | |
| "end": 1356, | |
| "text": "(Juola 1994;", | |
| "ref_id": "BIBREF8" | |
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| "start": 1357, | |
| "end": 1370, | |
| "text": "Cranias 1995)", | |
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| "start": 1726, | |
| "end": 1736, | |
| "text": "(Sato 1990", | |
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| "section": "EBMT", | |
| "sec_num": "2." | |
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| "text": "In ReVerb, translation examples consist of aligned sentences/ clauses which are extracted from a bilingual corpus, abstracted to a dependency level of syntactic representation, and then cross-linked during the indexing stage. Linking the respective SL and TL sentence sub-parts, or 'chunks' (where a 'chunk' is a syntactic constituent consisting of one or more words, e.g., \"on the screen\" is an adverbial chunk) in this way results in a translation process which accounts for structural and lexical divergences that may have occurred during initial human translation of the corpus. Thus, the ReVerb system uses shallower linguistic processing to that of Sato and Nagao, and, instead of calculating the exact tree operations necessary to adapt the chosen case for both the source and target language at run-time, ReVerb compares flat lists of features and values and chooses the case which will unify best with the input. ReVerb's templates contain variables which indicate the chunks that can be replaced, thus increasing the likelihood of the input and examples unifying because in these positions, only the features (syntactic functions), and not the values (actual words), are required to match. In Sato's approach, the context of the original case is taken into account when inserting it into a new tree. This means that subtrees are replaced by subtrees coming from a similar syntactic and semantic environment, which is desirable. However, it does not guarantee that the resulting tree is syntactically correct in the case of the target language. Hacking a tree on the basis of subtree similarity in the source language can never be guaranteed to result in a correspondingly hacked up target language tree with its syntactic structure still intact. This is the problem which our approach seeks to address in particular.", | |
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| "section": "EBMT", | |
| "sec_num": "2." | |
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| "text": "One way of addressing this problem is by quantifying just how 'dependent' chunks within a sentence are on other chunks, not only with respect to other chunks in the sentence (within-language dependency) but also with respect to its mapping to the target text (betweenlanguages dependency). A high dependency of either kind is penalised and receives a low adaptability score.", | |
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| "section": "EBMT", | |
| "sec_num": "2." | |
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| { | |
| "text": "In general, an EBMT system architecture will contain the following data in the example base: SL Source Language in original string form. TL Target Language in original string form. SLA Source Language Abstraction. TLA Target language Abstraction.", | |
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| "section": "Mappings in EBMT", | |
| "sec_num": "2.1" | |
| }, | |
| { | |
| "text": "The SL is abstracted into the SLA by means of some standard linguistic processing, likewise the TL (see Figure. 1 ). The SLA and TLA representations are then linked at the sub-sentential level. The level of linguistic abstraction will determine how substrings can be linked. We link the SL and TL at the syntactic-functional level.", | |
| "cite_spans": [], | |
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| { | |
| "start": 104, | |
| "end": 113, | |
| "text": "Figure. 1", | |
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| "section": "Mappings in EBMT", | |
| "sec_num": "2.1" | |
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| "text": "In order to define a subsentential mapping between two natural languages it is not sufficient to link two nodes which have the same syntactic feature. Translators often find it necessary to express SL words as different syntactic functions in the TL in order to create a grammatically or even stylistically correct TL. For example, the verb, say, in the SLA, may be realised as the subject in the TLA. Such divergences are the main obstacles to MT and there have been several attempts to formalise the problem (see Dorr 1993; Streiter 1995) but they inevitably suffer from the knowledge-acquisition bottleneck problem. In CBR, the idea is to compare enough previous translations so that useful generalisations fall out of the data. Therefore, one needs firstly to link two constituents on the basis of their lexical meaning and then to determine how the corresponding TLA constituent was realised syntactically compared to the SLA. Each case therefore is a description of a TLA derivation. A scoring mechanism can be used to penalise SLA:TLA mappings where the syntactic function was changed. A case base of such derivations constitutes a subset of contrastive linguistic data, which, if suitably generalised, can be regarded as an abstract model of translation for the SLA and TLA in that particular domain. More importantly for our purposes, it represents how adaptable each translation case is, for only cases with good SLA : TLA mappings can be adapted to compensate for any differences between the SL and the SL'.", | |
| "cite_spans": [ | |
| { | |
| "start": 515, | |
| "end": 525, | |
| "text": "Dorr 1993;", | |
| "ref_id": "BIBREF5" | |
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| { | |
| "start": 526, | |
| "end": 540, | |
| "text": "Streiter 1995)", | |
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| "section": "Fig.l. Multi-level correspondences between an input sentence (SL') and a given example (SL).", | |
| "sec_num": null | |
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| "text": "EBMT Systems which base their retrieval mechanisms on both similarity and adaptability would then differentiate between the following scenarios given a case base of examples and an input sentence representation, SL'. Here, we ignore the surface similarity (of two sentences, SL':SL) but see Section 4 for a discussion of the Base Filtering Stage.", | |
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| "section": "Fig.l. Multi-level correspondences between an input sentence (SL') and a given example (SL).", | |
| "sec_num": null | |
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| "text": "\u2022 Good SLA' : SLA, Good SLA : TLA -Retrievable and adaptable \u2022 Poor SLA' : SLA, Good SLA : TLA -Difficult to retrieve, easy to adapt \u2022 Good SLA' : SLA, Poor SLA : TLA.", | |
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| "section": "Fig.l. Multi-level correspondences between an input sentence (SL') and a given example (SL).", | |
| "sec_num": null | |
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| { | |
| "text": "-Easy to retrieve but adaptation is 'unsafe' \u2022 Poor SLA' : SLA Poor SLA : TLA.", | |
| "cite_spans": [], | |
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| "section": "Fig.l. Multi-level correspondences between an input sentence (SL') and a given example (SL).", | |
| "sec_num": null | |
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| { | |
| "text": "-The case will not be retrieved", | |
| "cite_spans": [], | |
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| "section": "Fig.l. Multi-level correspondences between an input sentence (SL') and a given example (SL).", | |
| "sec_num": null | |
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| "text": "Imitation of the retrieved example may involve nothing more than simple word substitution or it could involve integration of standard MT techniques (Kaji 1990; Sumita,1993) . A good way to proceed is to determine where simple chunk substitution is 'safe' with respect to the above named dependencies and to perform it as much as boundary friction will allow.", | |
| "cite_spans": [ | |
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| "start": 148, | |
| "end": 159, | |
| "text": "(Kaji 1990;", | |
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| "text": "Sumita,1993)", | |
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| "section": "Fig.l. Multi-level correspondences between an input sentence (SL') and a given example (SL).", | |
| "sec_num": null | |
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| "text": "The overall CBR model adopted for this work is best understood by consideration of Figure 2 following (adapted from Bergmann 1995) . While providing a coherent organisation for all the necessary components of a full EBMT system, we presently concentrate on only three components of this model, namely: Case Organisation, CaseRetrieval, and Case Adaptation (described in sections 3, 4 and 5 respectively).", | |
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| "text": "Figure 2", | |
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| "section": "Overall Architecture", | |
| "sec_num": "3.1" | |
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| "text": "The current case-base is derived from a corpus of bilingual English/German CorelDRAW manuals. Each case is represented in a uniquely-labelled frame which in turn contains a number of 'chunk'-frame pointers. A frame-based language KRELL is used to represent the cases. KRELL is a generic frame-management system in which specific knowledge bases may be implemented. Since a case must be retrieved from memory on the basis of its string/word content, each such word is defined in ReVerb's frame-structured memory, providing pointers to the chunks and cases in which it is employed; this situation is illustrated in Figure 3 below. The links between case frames, chunk frames, and word frames are bidirectional, with associated demon procedures to ensure consistency after updates and deletions (see Cunningham and Veale 1991) . ", | |
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| "text": "Cunningham and Veale 1991)", | |
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| "start": 613, | |
| "end": 621, | |
| "text": "Figure 3", | |
| "ref_id": "FIGREF1" | |
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| "eq_spans": [], | |
| "section": "An Associative Model of Case-Memory", | |
| "sec_num": "3.2." | |
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| "text": "While we are against case-decomposition in general, due to the boundary friction problem it causes, in some situations it is perfectly safe (and advisable) to split a given SL sentence into its component clauses if there is a direct mapping into its TL counterpart, as in the following example taken from the CorelDRAW corpus: Such separate clauses can then be combined with the clauses arising from other sentences in the corpus without experiencing the ill effects of boundary friction, because it has already been established that the clauses in question are independent of the sentence in which they originally occurred. In our test corpus of 200 examples, about 50% of the sentences consist of two or more clauses, and of those about 10% could be split up in this manner.", | |
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| "section": "Aligning the Examples", | |
| "sec_num": "3.3." | |
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| "text": "The model of EBMT described in Section 3.1 supports different levels of abstraction. At present, a surface syntactic structure is obtained for the English component of each case by passing it through the constraint based grammar engcg of Voutilanien (1995). This grammar tags its input with morphological and categorial information, while also annotating words with the correct syntactic function. It does not completely parse the input, as syntactic functions are not used for building a hierarchical representation of the sentence. Attachment ambiguities may thus be ignored completely. However this under-specification leaves us with a problem, namely that some fertile words have two syntactic functions. Postprocessing of the grammar output is currently necessary to eliminate this ambiguity of syntactic function.", | |
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| "section": "Abstraction", | |
| "sec_num": "3.4." | |
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| "text": "ReVerb uses a mappability scale of 0...3 to classify the different levels of correspondence that may exist between chunks (see Fig. 4 ). A mappability of 3 is granted only if the correspondence is perfect: i.e., all the words in an SLA chunk are mapped in 1:1 fashion to the TLA. A mappability of 2 indicates there is a morphological difference in the words surrounding the head (H) of the chunk, but that the syntactic functions are the same. Morphological variation would include many-to-one mappings in either direction, inflection, case-marking, additional particles, etc.). A mappability of 1 indicates that the SLA : TLA chunks differ in syntactic function, but have a lexical correspondence which would be awarded a 3 or 2 mapping had the syntactic function been the same, while zero is reserved for chunks which exhibit no SLA:TLA commonality. Because it cannot be discerned what these 'zero' chunks are linked to in the TL, it is dangerous to make any changes to them. In general, their function in the TL is incorporated into one of the chunks of mappability 1 or 2 so as to make a dictionary-based connection impossible. Fig. 4(b) . Linking the abstracted SL and TL.", | |
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| "end": 133, | |
| "text": "Fig. 4", | |
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| "end": 1141, | |
| "text": "Fig. 4(b)", | |
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| "section": "Linking", | |
| "sec_num": "3.5." | |
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| "text": "Generalisation of cases (see Figure 2 ) in ReVerb is achieved by a process of case templatisation, whereby a case template is generated by combining either the source text of each chunk, or a representative variable encoding the syntactic function of that chunk. Variablisation is performed by substituting SL elements of a given level of mappability/adaptability (or higher) with their part-of-speech (POS) tags; the intuition at work here is that the higher a chunk's mappability, the safer it will be to adapt at a later stage. As an example, consider that the SL string \"Move allows you to move the active window with the direction keys on the keyboard\" which is generalisable at four levels of variablisation, each corresponding to a different mappability score (0...3):", | |
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| "end": 37, | |
| "text": "Figure 2", | |
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| "section": "Generalisation of Cases for Indexed Retrieval", | |
| "sec_num": "3.5" | |
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| "text": "Level 0: SUBJ FMAINV OBJ NFMAINV OBJ ADVL ADVL Level 1: SUBJ FMAINV OBJ NFMAINV OBJ ADVL ADVL Level 2: SUBJ allows OBJ NFMAINV OBJ ADVL on the keyboard Level 3: Move allows you to move the active window with the direction keys on the keyboard", | |
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| "section": "Generalisation of Cases for Indexed Retrieval", | |
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| "text": "The success of this indexing scheme in syntax-based retrieval was tested on a case base of 214 cases at different levels of chunk variablisation. The results, shown in Figure 5 following, give an indication as to the trade-off between precision and recall. One can observe that as the mappability threshold for generalisation increases, the recall potential, or average number of retrievable cases, decreases. At the very safe mappability level of 3, therefore, a case-base of 20,000 cases would by our calculations contain 20 very adaptable templates. Fig. 5 . This graph illustrates the probability of any two cases (from a 214 case memory) matching at different levels of variablisation. As we increase the threshold for variablisation, the percentage of matches drops considerably.", | |
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| "text": "Figure 5", | |
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| "text": "Fig. 5", | |
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| "section": "Generalisation of Cases for Indexed Retrieval", | |
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| "text": "ReVerb currently utilises two different levels of case retrieval, using both surface and structural criteria; our aim is to compare the nature of the cases retrieved at each level independently, given the same input, to determine if string-based retrieval can act as a useful filter for subsequent structural retrieval. This arrangement is illustrated in Figure 6 . We shall now consider each of these levels in turn:", | |
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| "text": "Figure 6", | |
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| "section": "Retrieval in ReVerb", | |
| "sec_num": "4." | |
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| "text": "At run time, the user inputs a sentence, or clause, for which examples must be selected from the corpus. No linguistic judgements are made by the system regarding this input: only exact words are matched, and near morphological neighbours (e.g. \"object\" and \"objects\") are not considered. Frame demons will activate all cases containing exact word matches, allocating the highest scores to those cases which have been activated the greatest number of times. This brute-force technique omits all consideration of part of speech criteria, syntactic dependency, or word count of the input, but any information stored in each case (e.g., POS, linear order) can be exploited if the user so wishes. The first pass is not reliable in and of itself, but is subsequently used as a loose-grain filter to select candidates for the structural matching stage, Phase 2. ", | |
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| "section": "String Matching Retrieval: Phase 1", | |
| "sec_num": "4.1" | |
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| "text": "In the loose-grain stage, retrieval can be regarded as a process that satisfies the constraints imposed by a memory probe (i.e., the input). Marker passing is a suitable technique for retrieval at this level, which is well documented in AI literature (see Charniak 1983; Hendlerl989) , and is used here to give any retrieved case a similarity score relative to the input. Activation passing is appropriate because it propagates simple numeric values throughout memory.", | |
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| "text": "Charniak 1983;", | |
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| "section": "Activation Passing for Retrieval", | |
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| "text": "For structural retrieval, the input sentence is first pre-chunked, such that each chunk has an explicit head-word. The algorithm initiates activation from each word in the chunk, giving the head word an increased weighting to reflect its pivotal role in the chunk. The summation of activation proceeds as for the purely string based retrieval method, save that adaptability/mappability scores are integrated into the calculation of activation levels.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Activation Passing for Syntactic Retrieval: Phase 2", | |
| "sec_num": "4.3" | |
| }, | |
| { | |
| "text": "By adaptation, we mean here on-line, or run-time adaptation of the TLA frame of a retrieved example to accommodate any differences between the input SL and the example SL'. We restrict our present discussion to the dependency-syntactic level of case representation. No changes are made to the syntactic structure (the linear ordering of syntactic functions) during adaptation.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Adaptation", | |
| "sec_num": "5." | |
| }, | |
| { | |
| "text": "If an SL' and an SL sentence are extremely similar on a syntactic level and yet semantically totally different, then they will almost always have different patterns of syntactic links to the target language. ReVerb will always choose an example which can be adapted easily at a syntactic level. So, if the input sentence (unlike the straightforward example) actually requires a divergence, caused by a certain verb, say, then the resultant translation will not be correct and the user will have to correct it and it will be stored as a new case. This stored example will have a poor mappability but it will be specialised for that particular verb. Now, if a new input sentence also has this divergency-causing verb in it and the surrounding context is sufficiently similar to render the example retrievable, then the poor mappability of the example is less likely to result in a poor translation this time around.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Adaptation", | |
| "sec_num": "5." | |
| }, | |
| { | |
| "text": "If a chunk has been seen before by the system it will be stored in the system's bilingual lexicon, which is simply a table of word-frames which are linked, in turn, to their host cases and thus to their TL-equivalents within those cases. In other words, no effort goes into building the lexicon as it is a by-product of ReVerb's case-memory organisation.Chunks which have a high mappability score (3 being best) are preferred by the system, as are those which have the same syntactic function as the problem word (2+), and we are currently experimenting with incorporating a chunk's context as an added contributor to the score. The new target word (i.e., in German) is then inserted into the new TLA structure (a copy of the old TLA example), and assumes the same syntactic function, linear order and syntactic category as the original. Depending on whether the mappability of the substitution is 3,2 or l, the system awards a local translation quality of 100%, 67% and 33% respectively. The translation quality of a chunk which does not need to be substituted by lexical lookup is similarly determined on the basis of its SL': TL' mappability. In this way a global translation quality can be compositionally determined for the whole sentence.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Using the Corpus as a Bilingual Dictionary", | |
| "sec_num": "5.1" | |
| }, | |
| { | |
| "text": "If the system has not previously 'seen' a particular SLA chunk to be adapted, then a bilingual dictionary outside the system has to be consulted. In this case, the word will be in its root form and so the system must consult the morphological markings on the original example's TL words to modify the dictionary entry correspondingly.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Using the Corpus as a Bilingual Dictionary", | |
| "sec_num": "5.1" | |
| }, | |
| { | |
| "text": "Our experiments to adapt input structures have produced encouraging results in that the output translations are quite accurate and helpful to the user. The input sentences we have used are taken from the case base itself-that is the SLA components of the cases are used to index the case base. At this stage, we are primarily interested in the correct adaptation of each structure with respect to its nearest structural matches in the case base, provided there are any. For some 74% of cases at present, however, there are no exact structural matches even when all of the chunks are variablised (level 0). This indicates how varied English language structures can be, even in the subdomain of software documentation where sentences and clauses tend to be short. Of the 24% SLAs which do retrieve structurally similar examples, the results of the matching and adaptation modules are outlined in sections 6.2 and 6.3 respectively. The basefiltering (phase 1) is discussed in section 6.1 but it is clear that it will only prove its usefulness when the case base has been scaled up.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Evaluation", | |
| "sec_num": "6." | |
| }, | |
| { | |
| "text": "The first retrieval phase acts as a base-filter which extracts a group of candidate translations on the basis of string level matching. These are then input into phase two. At present, the case base is too small for the retrieved examples to be significantly similar but when the data is scaled up to, say, 20,000 examples, then this stage will become more useful.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Phase 1 Retrieval : The Base Filter", | |
| "sec_num": "6.1" | |
| }, | |
| { | |
| "text": "At this level of retrieval structurally similar templates are retrieved. The threshold adaptability level with respect to the SLA' can be set to 3,2,1 or 0 depending on whether the user wishes to include templates with only high-level chunk mappings (3) or to include all templates (0). When the adaptability level is set to zero it is equivalent to similarity-guided retrieval only. At present, the system's performance at each level of adaptability is evaluated on the basis of the quality of output sentences, that is, the number of mistakes found in the output translations, as judged by a human evaluator (see Figure 8 ). The 'translation score' is ReVerb's prediction regarding translation quality and is based on its knowledge about chunk similarity and mappability ", | |
| "cite_spans": [], | |
| "ref_spans": [ | |
| { | |
| "start": 615, | |
| "end": 623, | |
| "text": "Figure 8", | |
| "ref_id": null | |
| } | |
| ], | |
| "eq_spans": [], | |
| "section": "Phase 2 Retrieval: Structural Comparison and Adaptability", | |
| "sec_num": "6.2" | |
| }, | |
| { | |
| "text": "At this highly restrictive level, only those templates are retrieved whose SLA differences are all reconcilable by adapting a chunk of mappability 3, usually by simple NP substitution ( Figure 9 ).", | |
| "cite_spans": [], | |
| "ref_spans": [ | |
| { | |
| "start": 186, | |
| "end": 195, | |
| "text": "Figure 9", | |
| "ref_id": null | |
| } | |
| ], | |
| "eq_spans": [], | |
| "section": "Retrieval at adaptability threshold: 3", | |
| "sec_num": "6.2.1" | |
| }, | |
| { | |
| "text": "Result: 6 matches in 214 cases Quality: perfect translations Fig. 9 Adaptation at Level 3 of a highly similar example.", | |
| "cite_spans": [], | |
| "ref_spans": [ | |
| { | |
| "start": 61, | |
| "end": 67, | |
| "text": "Fig. 9", | |
| "ref_id": null | |
| } | |
| ], | |
| "eq_spans": [], | |
| "section": "Retrieval at adaptability threshold: 3", | |
| "sec_num": "6.2.1" | |
| }, | |
| { | |
| "text": "At this level, one or more of the SLA chunks which need adaptation is different in word-form to the TLA, but syntactic functionality is isomorphic. ", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Retrieval at adaptability threshold: 2", | |
| "sec_num": "6.2.2" | |
| }, | |
| { | |
| "text": "In testing our experimental system, ReVerb, we found that it can accurately adapt exemplar TLA templates when the corresponding SLA exemplar has sufficient structural similarity to the input sentence. Indeed, the CorelDRAW corpus contains several examples of such nearmappings, which are typical of computer software manuals. We have implemented the system in such a way as to be extensible in the following important respects. Firstly, we wish to bring such near-misses within the reach of the system. For instance, the input sentence may differ from a memory exemplar only in the addition (or omission) of a trailing temporal adjunct such as \"yesterday\".Redundancy rules governing the organisation of the case base can easily generate new well-formed cases from old by deleting or adding such constituents (if they have a 3 mapping between SLA and TLA), thus increasing the coverage of the case-base while avoiding an over-complication of the system's matching algorithms.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Conclusions & Future Work", | |
| "sec_num": "7." | |
| }, | |
| { | |
| "text": "Secondly, we wish to refine the on-line adaptation process so that mappings of 2 and 1 will cause specialised adaptation operators to come into effect which will refer to the POS knowledge stored in the cases, or will use a derivational analysis approach to reconciling SLA: TLA differences and transmitting them to the TL. Finally, the mechanisms are already in place for semi-automatic case creation from bilingual text, so our long-term aim is to increase the number of cases until such time that the process can proceed completely automatically. This will require that all possible structures will be present in the case base, perhaps in generalised form, and also most (contextualised) words which one may expect to find in the given application domain. Our results have lead us to believe that there is an upper limit to the number of generalised templates necessary for producing perfect translations with the help of specialised adaptation specialists. On reaching this limit, a system could perform fully automatic MT. In the mean time, however, we are confident AGR can produce translation tools which assist users-who are not necessarily bilingual-in producing high quality translations of software documentation texts.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Conclusions & Future Work", | |
| "sec_num": "7." | |
| } | |
| ], | |
| "back_matter": [ | |
| { | |
| "text": "Befehl loeschen loescht markierte Objekte. TL': Drehung dreht die Zwischenformen. Human evaluation: no mistakes ReVerb Evaluation: 0.889", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "TL:", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "Here, the SLA chunks that need adaptation have different corresponding syntactic functions in the TLA.Result: 50 matches in 214 cases Quality: Varies from perfect (no mistakes) in some 36% of the cases to mediocre (3 errors or less) in most others. What is very interesting here is that almost two-thirds of the cases (62%) had only one error or less, which would be easy for even an inexperienced translator to correct. Only 2% of the cases had more than 3 errors. The most common problem is the verb positioning and the incorrect form of the verb appearing in the adapted case. Another common problem is that of prepositions vanishing because an ADVL chunk containing a preposition is suddenly replaced by a SUBJ or OBJ chunk then the preposition will be overwritten by a single noun. ", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Retrieval at adaptability threshold: 1", | |
| "sec_num": "6.2.3" | |
| }, | |
| { | |
| "text": "One or more chunks in the SLA have no lexically determinable links to the TLA. Result: 104 matches in 214 cases Quality: Words which needed adaptation were often not recorded in the system dictionary. The translation quality suffered from extra TLA' words being present which were irrelevant to the SLA'. Also, linear order and word-forms of many chunks were incorrect, especially verbs. Note that if the SL and SL' are similar both syntactically and semantically, as in the example below, the corresponding TL and TL' structures will mirror each other. The system gives a poor evaluation score because many chunks with poor mappability have been adapted. However, due to the multilevel similarity between SL and SL', the adaptation was nonetheless 'safe'. This suggests that ReVerb's evaluation technique needs to be refined to take this into account. Example: SL': Use the Zoom Tool to bring all objects into view. SL: Use the Offset Command to specify the spacing between the shapes. TL: Mit der Option Abstand legen Sie den Abstand zwischen den Formen fest. TL': Mit der Option Zoom des Hilfmittels Zoom bringen Sie alle Objekte in den sichtbaren Bereich Human Evaluation: 0 mistakes ReVerb Evaluation: 0.583", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Retrieval at adaptability threshold: 0", | |
| "sec_num": "6.2.4" | |
| } | |
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| "FIGREF0": { | |
| "num": null, | |
| "text": "1", | |
| "uris": null, | |
| "type_str": "figure" | |
| }, | |
| "FIGREF1": { | |
| "num": null, | |
| "text": "Concepts linked in the ReVerb translation memory.", | |
| "uris": null, | |
| "type_str": "figure" | |
| }, | |
| "FIGREF2": { | |
| "num": null, | |
| "text": "Retrieval levels in ReVerb.", | |
| "uris": null, | |
| "type_str": "figure" | |
| }, | |
| "TABREF2": { | |
| "text": "Rotate rotates the intermediate blend objects.", | |
| "content": "<table><tr><td>Result: 8 matches in 214 cases</td></tr><tr><td>Quality: Same as for threshold 3,but is dependent on the chunk's translation being correct in the</td></tr><tr><td>lexicon for the context of the new case.</td></tr><tr><td>Example:</td></tr><tr><td>SL':</td></tr></table>", | |
| "num": null, | |
| "html": null, | |
| "type_str": "table" | |
| } | |
| } | |
| } | |
| } |