A Comparison of the Word Similarity Measurement in E nglish- A rabic Translation Memory Segment Retrieval Including an Inflectional Affix Intervention (2021)

Abstract: The aim of this paper is to investigate the similarity measurement approach of translation memory (TM) in five representative computer-aided translation (CAT) tools when retrieving inflectional verb-variation sentences in Arabic to English translation. In English, inflectional affixes in verbs include suffixes only; unlike English, verbs in Arabic derive voice, mood, tense, number and person through various inflectional affixes e.g. pre or post a verb root. The research question focuses on establishing whether the TM similarity algorithm measures a combination of the inflectional affixes as a word or as a character intervention when retrieving a segment. If it is dealt with as a character intervention, are the types of intervention penalized equally or differently? This paper experimentally examines, through a black box testing methodology and a test suite instrument, the penalties that TM systems’ current algorithms impose when input segments and retrieved TM sources are exactly the same, except for a difference in an inflectional affix. It would be expected that, if TM systems had some linguistic knowledge, the penalty would be very light, which would be useful to translators, since a high-scoring match would be presented near the top of the list of proposals. However, analysis of TM systems’ output shows that inflectional affixes are penalized more heavily than expected, and in different ways. They may be treated as an intervention on the whole word, or as a single character change.

## introduction a translation memory is a database that contains translation units which comprise source language segments aligned with their target language translations. when a new (input) text is uploaded for translating, the tm matching and retrieval mechanism computes the string similarity of the input in comparison with the source segments contained in the tm. then, tm technology leverages translation candidates with the highest similarity to the input segment @xcite . however, there is little detailed information on how the matching algorithms assign a score to the matching strings. most previous studies repeat the belief that the tm similarity measurement is based on the levenshtein @xcite distance algorithm (e.g. simard and fujita @xcite ). this similarity measurement uses three basic edit operations (insertion, deletion and substitution) to determine a distance between two strings, then the distance is normalized into a matching score. hence, the question is how do tm systems measure the matching between two strings? is the matching measurement based on a comparison word by word? or, is the measurement computed character by character? for example, if two source segments are identical except for a difference in an inflectional affix, does the algorithm measure a combination of the inflectional affixes as a word intervention or a character intervention? the researcher's hypothesis is that the tm metrics may compute inflectional verb variations is either as a word intervention, which means that the algorithm regards the inflected form as a totally different word, where the penalty would be expected to be very heavy, or as a character intervention, in which the penalty would be based on the edit type. hence, we argue that tm similarity metrics could have difficulties detecting inflectional affixes, which would not result in seeing high-scoring tm proposals. on the other hand, if the tm system were able to undertake a morphological analysis, it would treat the inflectional affix in a different way. however, macklovitch and russell @xcite pointed out that one of the limitations of tm systems is the failure to recognize inflectional variants. they argue that despite any necessary minor adjustments, a segment that includes an inflected word is still potentially informative. somers @xcite highlighted that a high-matching technique is needed to use linguistic information such as inflection paradigms, synonyms and grammatical alternations in order to improve tm fuzziness. a fuzzy match means a percentage assigned by a tm metric occurs when the input is partially similar to tm source; if the difference is minor, the value is high. if, on the other hand, the difference is significant, the score is low. in this paper, we aim to investigate the performance of tm similarity algorithms when retrieving inflectional verb-variation sentences in arabic-to-english translations. to achieve the aims of the study, a special corpus of arabic source segments and english target segments is provided, in which we apply a number of inflectional verb-variation transformation rules to the arabic source segments. test segments were extracted from the corpus and the edit distance metric was used as an analysis tool. the paper is organised as follows: section 2 reviews related works related to semantic matching in tms. in section 3, we present a review of the verb inflectional affixes in arabic. we describe the experimental methodology in section 4. we summarise the findings in section 5, and discussion of the results in section 6. lost usability opportunity of highly similar tm proposals is analysed in section 7. finally, the conclusions drawn from the research are in section 8. ## review of arabic verb inflections the arabic language is a highly inflected language, and verb inflection (which is known in arabic as ‫,األوزان‬ al-awzaan) is a conjugation process of creating new stems from the root using specific verbal templates. the verb conjugation involves the creation of new stems from the verb's root (the base of the verb form) using specific verbal templates. neme @xcite explains that the combination of a root with a pattern produces an inflected form in which the root signifies a morphemic abstraction for a verb, while the pattern is a template of characters (indices) surrounding the root consonants. the verb's tenseand other aspects such as gender and numberare generally represented using the rules of inflectional verb morphemes. tenses are used in either the perfect or imperfect form; the former indicates the past tense while the latter indicates the present or future tense. the language uses a unique inflection system: for example, verbs in the past tense are often designated by suffixes, whereas verbs in the present or future tense are often identified by a prefix. numbers are classified as plural, dual or singular, with two gender categories, feminine and masculine. the number and gender features can be integrated with the verb's tense and expressed in singleword forms @xcite . another important characteristic of arabic is that the overwhelming majority of verbs have roots consisting of three characters, in which the position of an inflectional affix (i.e. a character) that shapes the template is positioned either as a prefix or a suffix only, while the affix string may encompass one character or more. habash @xcite states in his book 'introduction to arabic natural language processing' that verb inflections have a limited number of patterns: ten basic templates for a three-character root and two templates for a four-character root. this means that the triliteral (threecharacter-root) verb could be transformed from one template into another template just by attaching a prefix (an initial attachment) or a suffix (a final attachment), while the string of basic form stays as one chunk (no mid-form intervention). in transformation sub-section (4.3) below, we describe a prefix and suffix combination with a three-character root in order to make different verbal templates. ## findings this section displays the results obtained from the tm systems' attempts to retrieve matches for the test segments. we assumed that scores at the higher end are better, for example 95% is better than 80%. ## discussion the experiment's findings show that the tm systems treated a combination of inflectional affixes in different ways: the tm matching algorithms dealt with the morphological combination as an intervention on the whole word, as a single character change, or according to the position of the intervention. in all the systems, however, it appears that segment length had a bearing on the results. ## conclusion the overall conclusion drawn from the results of testing the retrieval of tm sources for a text that is rich in morphological combinations is that all the selected