Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userIntroducing linguistic transformation to improve translation memory retrieval. Results of a professional translators' survey for Spanish, French and Arabic
إدخال التحول اللغوي لتحسين استرجاع ذاكرة الترجمة.نتائج مسح المترجمين المحترفين للإسبانية والفرنسية والعربية
570
0 0
0.0
(
0
)
Created by
Shamra Editor
Ask ChatGPT about the research
أنظمة ذاكرة الترجمة (TMS) هي المكون الرئيسي لأدوات الترجمة المساعدة بمساعدة الكمبيوتر. يقومون بتخزين الترجمات التي تسمح بتوفير الوقت عن طريق تقديم الترجمات على قاعدة البيانات من خلال مطابقة عدة أنواع مثل المباريات الغامضة، والتي تحسبها خوارزميات مثل مسافة التعديل. ومع ذلك، أظهرت الدراسات أوجه القصور اللغوي لهذه النظم والصعوبات في استرجاع البيانات أو الحصول على نسبة عالية من المطابقة، خاصة بعد تطبيق التحولات النحوية والدلوية هي التغيير الصوتي النشط / السلبي، تغيير ترتيب الكلمات، الاستبدال بواسطة مرادف أو ضمير شخصي، على سبيل المثال. تقدم هذه الورقة نتائج دراسة تجريبية حيث نحلل البيانات النوعية والكمية من الاستبيانات التي أجريت مع المترجمين المحترفين للإسبانية والفرنسية والعربية من أجل تحسين فعالية TMS واستكشاف جميع الاحتمالات لدمج مزيد من المعالجة اللغوية من عشرة أنواع التحول وبعد النتائج مشجعة، وسمحت لنا لمعرفة عملية الترجمة نفسها؛ التي نقترحنا أداة معالجة مسبقة التحرير لتحسين عمليات المطابقة واسترجح العمليات.
Translation memory systems (TMS) are the main component of computer-assisted translation (CAT) tools. They store translations allowing to save time by presenting translations on the database through matching of several types such as fuzzy matches, which are calculated by algorithms like the edit distance. However, studies have demonstrated the linguistic deficiencies of these systems and the difficulties in data retrieval or obtaining a high percentage of matching, especially after the application of syntactic and semantic transformations as the active/passive voice change, change of word order, substitution by a synonym or a personal pronoun, for instance. This paper presents the results of a pilot study where we analyze the qualitative and quantitative data of questionnaires conducted with professional translators of Spanish, French and Arabic in order to improve the effectiveness of TMS and explore all possibilities to integrate further linguistic processing from ten transformation types. The results are encouraging, and they allowed us to find out about the translation process itself; from which we propose a pre-editing processing tool to improve the matching and retrieving processes.
References used
https://aclanthology.org/
rate research
Read More
Translation Memory (TM) system, a major component of computer-assisted translation (CAT), is widely used to improve human translators' productivity by making effective use of previously translated resource. We propose a method to achieve high-speed r
etrieval from a large translation memory by means of similarity evaluation based on vector model, and present the experimental result. Through our experiment using Lucene, an open source information retrieval search engine, we conclude that it is possible to achieve real-time retrieval speed of about tens of microseconds even for a large translation memory with 5 million segment pairs.
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 translatio
n. 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.
In primary school, children's books, as well as in modern language learning apps, multi-modal learning strategies like illustrations of terms and phrases are used to support reading comprehension. Also, several studies in educational psychology sugge
st that integrating cross-modal information will improve reading comprehension. We claim that state-of- he-art multi-modal transformers, which could be used in a language learner context to improve human reading, will perform poorly because of the short and relatively simple textual data those models are trained with. To prove our hypotheses, we collected a new multi-modal image-retrieval dataset based on data from Wikipedia. In an in-depth data analysis, we highlight the differences between our dataset and other popular datasets. Additionally, we evaluate several state-of-the-art multi-modal transformers on text-image retrieval on our dataset and analyze their meager results, which verify our claims.
Introducing biomedical informatics (BMI) students to natural language processing (NLP) requires balancing technical depth with practical know-how to address application-focused needs. We developed a set of three activities introducing introductory BM
I students to information retrieval with NLP, covering document representation strategies and language models from TF-IDF to BERT. These activities provide students with hands-on experience targeted towards common use cases, and introduce fundamental components of NLP workflows for a wide variety of applications.
This research proposes a new way to improve the
search outcome of Arabic semantics by abstractly summarizing the
Arabic texts (Abstractive Summary) using natural language
processing algorithms(NLP),Word Sense Disambiguation (WSD)
and techniques o
f measuring Semantic Similarity in Arabic WordNet
Ontology.
معالجة اللغات الطبيعية
Semantic analysis
استرجاع المعلومات
التلخيص التجريدي
الأنتولوجيا العربية ووردنت
العلاقة الدلالية المفاهيمية
التشابهية الدلالية
التحليل الدلالي
حل غموض معاني الكلمات
(Natural Language Processing (NLP
(Information Retrieval (IR
Abstractive Summarization
(Arabic WordNet (AWN
Conceptual Semantic Relation
Semantic Similarity
(Word Sense Disambiguation (WSD
المزيد..
suggested questions
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
