The rise of pre-trained language models has yielded substantial progress in the vast majority of Natural Language Processing (NLP) tasks. However, a generic approach towards the pre-training procedure can naturally be sub-optimal in some cases. Parti
cularly, fine-tuning a pre-trained language model on a source domain and then applying it to a different target domain, results in a sharp performance decline of the eventual classifier for many source-target domain pairs. Moreover, in some NLP tasks, the output categories substantially differ between domains, making adaptation even more challenging. This, for example, happens in the task of aspect extraction, where the aspects of interest of reviews of, e.g., restaurants or electronic devices may be very different. This paper presents a new fine-tuning scheme for BERT, which aims to address the above challenges. We name this scheme DILBERT: Domain Invariant Learning with BERT, and customize it for aspect extraction in the unsupervised domain adaptation setting. DILBERT harnesses the categorical information of both the source and the target domains to guide the pre-training process towards a more domain and category invariant representation, thus closing the gap between the domains. We show that DILBERT yields substantial improvements over state-of-the-art baselines while using a fraction of the unlabeled data, particularly in more challenging domain adaptation setups.
Starting from an existing account of semantic classification and learning from interaction formulated in a Probabilistic Type Theory with Records, encompassing Bayesian inference and learning with a frequentist flavour, we observe some problems with
this account and provide an alternative account of classification learning that addresses the observed problems. The proposed account is also broadly Bayesian in nature but instead uses a linear transformation model for classification and learning.
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, wh
ich 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.
The combination of multilingual pre-trained representations and cross-lingual transfer learning is one of the most effective methods for building functional NLP systems for low-resource languages. However, for extremely low-resource languages without
large-scale monolingual corpora for pre-training or sufficient annotated data for fine-tuning, transfer learning remains an understudied and challenging task. Moreover, recent work shows that multilingual representations are surprisingly disjoint across languages, bringing additional challenges for transfer onto extremely low-resource languages. In this paper, we propose MetaXL, a meta-learning based framework that learns to transform representations judiciously from auxiliary languages to a target one and brings their representation spaces closer for effective transfer. Extensive experiments on real-world low-resource languages -- without access to large-scale monolingual corpora or large amounts of labeled data -- for tasks like cross-lingual sentiment analysis and named entity recognition show the effectiveness of our approach. Code for MetaXL is publicly available at github.com/microsoft/MetaXL.
Epithelial to mesenchymal transition (EMT) process represents an
essential stage for tissue healing، it is also considered as a preface
for tumour proliferation and invasion. The current study aimed to
investigate the effects of rhBMP-2 protein on
two essential
biomarkers for this process : β-catenin and E-cadherin.
The study aimed to identify the point of view of the owners of family
companies in Syria to the process of transformation into a joint stock
company, and the benefits that can be achieved by the transformation
process from the standpoint of the ow
ners of family businesses. The
study sample included 117 family company in Latakia province
responded to 65 companies, including, where the study was based on a
survey method using a questionnaire to test hypotheses.
This study aims to examine factors influencing the customer switching behavior in the Syrian banking sector, as this is the first study of its kind in the Syrian Arab Republic. This study will help the departments of banks to understand the factors t
hat drive customers to switch their banks to another and develop strategies that help reducing the proportion of the switching they have.
Oral epithelial dysplasia is a precancerous change develops in the oral mucosa, and
leukoplakia is the most common precancerous lesions in oral mucosa.
The epithelial-mesenchymal transition (EMT) is considered the mechanism through
which the oral
leukoplakia becomes malignant, and it's also responsible for the invasion
and metastasis of cancers. This study investigates the expression of β-catenin in epithelialmesenchymal
transition detected by immunohistochemical staining in the different grades
of oral epithelial dysplasia.
The sample consisted of 45 formalin-fixed, paraffin-embedded specimens of oral
leukoplakia with different grades of oral epithelial dysplasia; specimenswere
immunohistochemically stained with β-catenin antibody.
The control group was consisted of biopsies from the normal oral epithelia.
The EMT rates are associated with the severity of the oral epithelial dysplasia.That
means, the epithelial-mesenchymal transition marker β-catenin could be used to evaluate
the potentiality of malignant transformation in the precancerous lesions.
This study aims at discovering the mediating role of switching costs in the relationship
between adapting the three relationship marketing programs (financial programs, social programs,
structural programs) and customers loyalty of Syrian private b
anks in Syrian costal area. The
sample of this study withdrew randomly from the population of the study. The data collected
through pre-designed questionnaire which was directed to those customers. This study finds that
there is a positive impact of the three relationship marketing programs (financial programs, social
programs, structural programs) on both customers loyalty and switching costs, which in turn have a
positive impact on customers loyalty. The perceived switching costs play a mediating role in the
relationship between relationship marketing programs and customers loyalty. This study
recommends Syrian private banks to build a strong relationships with their customers in a long
term by adapting the three relationship marketing programs.
مساهمة في التحول الحيوي للمنتجات الثانوية لمعاصر الزيتون والحصول على البروتين: تعد منطقة حوض البحر الابيض المتوسط رائدة بإنتاج زيت الزيتون
