Domain adaptive pretraining, i.e. the continued unsupervised pretraining of a language model on domain-specific text, improves the modelling of text for downstream tasks within the domain. Numerous real-world applications are based on domain-specific
text, e.g. working with financial or biomedical documents, and these applications often need to support multiple languages. However, large-scale domain-specific multilingual pretraining data for such scenarios can be difficult to obtain, due to regulations, legislation, or simply a lack of language- and domain-specific text. One solution is to train a single multilingual model, taking advantage of the data available in as many languages as possible. In this work, we explore the benefits of domain adaptive pretraining with a focus on adapting to multiple languages within a specific domain. We propose different techniques to compose pretraining corpora that enable a language model to both become domain-specific and multilingual. Evaluation on nine domain-specific datasets---for biomedical named entity recognition and financial sentence classification---covering seven different languages show that a single multilingual domain-specific model can outperform the general multilingual model, and performs close to its monolingual counterpart. This finding holds across two different pretraining methods, adapter-based pretraining and full model pretraining.
Recent task-oriented dialogue systems learn a model from annotated dialogues, and such dialogues are in turn collected and annotated so that they are consistent with certain domain knowledge. However, in real scenarios, domain knowledge is subject to
frequent changes, and initial training dialogues may soon become obsolete, resulting in a significant decrease in the model performance. In this paper, we investigate the relationship between training dialogues and domain knowledge, and propose Dialogue Domain Adaptation, a methodology aiming at adapting initial training dialogues to changes intervened in the domain knowledge. We focus on slot-value changes (e.g., when new slot values are available to describe domain entities) and define an experimental setting for dialogue domain adaptation. First, we show that current state-of-the-art models for dialogue state tracking are still poorly robust to slot-value changes of the domain knowledge. Then, we compare different domain adaptation strategies, showing that simple techniques are effective to reduce the gap between training dialogues and domain knowledge.
This research is a study of distribution of Patella caerulea in four locations of the Latakia shore different from each other by Substrate nature and their exposure to pollution sources. They are as follow : Afamia (A),Ibn Hani (B) ,harbor fishing of
Tower Islam (C), and the sandy beach of Tower Islam (D).
This study was achieved between January 2012 and January 2013 ,The samples were collected manually from the Supralittoral , Littoral and Sublittoral region(depth of two meters).The results of this study showed the following:
1 – The area A is the most suitable for the growth and reproduction of the Patella caerulea, which recorded the highest value of biomass reaching ( 3990.2 gr / m²), and the number of individuals (2128 ind /m²) .
2 - The area C recorded the lowest value of biomass (2872.47 gr/ m²) and the number of individuals (1632 ind / m²).
3 - Patella caerulea is consederied a Eurybiont as it was found in all the study areas .
4 - Patella caerulea is comercially important species .
