"Strategic Planning in the Construction Sector: A Proposed Model for the Strategic Plan to Adopt BIM in Syria." Research Summary: Comprehensive strategic planning for the Syrian state is an inevitable necessity to address the catastrophic effects of
the war that the country has suffered from, and its effects are still ongoing in light of government incapacity and gross failure that has affected all economic, industrial, and scientific aspects. This is reflected in the global development and knowledge indicators. Therefore, the Syrian state must adopt comprehensive planning concepts and strive to make knowledge its primary destination to create a strong economy and a strong industry ready for the upcoming reconstruction phase. It should adopt modern administrative concepts, cognitive and engineering sciences, and seek to incorporate them into its plans. Building Information Modeling (BIM) modeling may occupy the forefront of these sciences due to its great importance in transferring Syrian engineering work in the construction sector to advanced countries. Therefore, the study presented in its chapters the concepts of comprehensive planning and strategic planning on the one hand, and BIM modeling on the other hand, through analyzing and comparing current strategies to adopt BIM technology and the most important global trends and experiences, and identifying the most important obstacles and challenges that faced it. Then, the current situation of BIM technology was studied and analyzed, exploring the extent of its spread in the Syrian construction industry, with the aim of formulating a framework for integrating BIM modeling technology effectively within the life cycle of engineering projects in Syria, and proposing a strategic plan to adopt BIM in Syria. As a result, the research produced a set of findings throughout its chapters as follows: In chapter two, a model was constructed to integrate government plans that contribute to achieving the BIM plan. This was done through a comprehensive study of planning and strategic planning, as well as an examination of the reality of planning and the various government plans in Syria, which revealed weaknesses in both the planning mechanism and plan implementation mechanisms. In chapter three, a comprehensive study was conducted on the current situation of BIM adoption in Syria, and the problems and difficulties that hinder its implementation. The BIM maturity matrix was applied to companies in both the public and private sectors, revealing weaknesses in both sectors in terms of BIM adoption, despite the private sector's superiority in most areas. Based on this, a SWOT analysis was conducted on the current situation in Syria regarding BIM adoption, which indicated strengths, weaknesses, opportunities, and threats. In chapter four, a proposed framework was developed for implementing the strategic plan for BIM adoption in Syria. This resulted in a roadmap for BIM adoption in Syria from the beginning of 2023 until the end of 2030. In chapter five, the plan was practically applied to a performance management program called BSC DESIGNER, resulting in a strong and robust performance management system for implementing the strategic plan according to a timeline from the beginning of 2023 until the end of 2030. This research is a bold attempt by the researcher to complement various sciences within a comprehensive strategic planning framework. This research aims to reach decision-makers and help put Syria on the global BIM map by translating the plan's vocabulary and goals into practical reality that contributes to shaping the future of the construction industry in Syria. This study recommends coordination and cooperation between decision-makers and stakeholders in the construction sector to implement the proposed BIM adoption strategy through its four axes (policies, technologies, processes, knowledge, and skills) and secure financial support. Keywords: strategic planning, comprehensive planning, building information modeling, performance management, engineering projects, BIM adoption plan, Syria.
Large-scale conversation models are turning to leveraging external knowledge to improve the factual accuracy in response generation. Considering the infeasibility to annotate the external knowledge for large-scale dialogue corpora, it is desirable to
learn the knowledge selection and response generation in an unsupervised manner. In this paper, we propose PLATO-KAG (Knowledge-Augmented Generation), an unsupervised learning approach for end-to-end knowledge-grounded conversation modeling. For each dialogue context, the top-k relevant knowledge elements are selected and then employed in knowledge-grounded response generation. The two components of knowledge selection and response generation are optimized jointly and effectively under a balanced objective. Experimental results on two publicly available datasets validate the superiority of PLATO-KAG.
This paper presents a pilot study to automatic linguistic preprocessing of Ancient and Byzantine Greek, and morphological analysis more specifically. To this end, a novel subword-based BERT language model was trained on the basis of a varied corpus o
f Modern, Ancient and Post-classical Greek texts. Consequently, the obtained BERT embeddings were incorporated to train a fine-grained Part-of-Speech tagger for Ancient and Byzantine Greek. In addition, a corpus of Greek Epigrams was manually annotated and the resulting gold standard was used to evaluate the performance of the morphological analyser on Byzantine Greek. The experimental results show very good perplexity scores (4.9) for the BERT language model and state-of-the-art performance for the fine-grained Part-of-Speech tagger for in-domain data (treebanks containing a mixture of Classical and Medieval Greek), as well as for the newly created Byzantine Greek gold standard data set. The language models and associated code are made available for use at https://github.com/pranaydeeps/Ancient-Greek-BERT
With the emergence of the COVID-19 pandemic, the political and the medical aspects of disinformation merged as the problem got elevated to a whole new level to become the first global infodemic. Fighting this infodemic has been declared one of the mo
st important focus areas of the World Health Organization, with dangers ranging from promoting fake cures, rumors, and conspiracy theories to spreading xenophobia and panic. Addressing the issue requires solving a number of challenging problems such as identifying messages containing claims, determining their check-worthiness and factuality, and their potential to do harm as well as the nature of that harm, to mention just a few. To address this gap, we release a large dataset of 16K manually annotated tweets for fine-grained disinformation analysis that (i) focuses on COVID-19, (ii) combines the perspectives and the interests of journalists, fact-checkers, social media platforms, policy makers, and society, and (iii) covers Arabic, Bulgarian, Dutch, and English. Finally, we show strong evaluation results using pretrained Transformers, thus confirming the practical utility of the dataset in monolingual vs. multilingual, and single task vs. multitask settings.
Dialogue state tracking models play an important role in a task-oriented dialogue system. However, most of them model the slot types conditionally independently given the input. We discover that it may cause the model to be confused by slot types tha
t share the same data type. To mitigate this issue, we propose TripPy-MRF and TripPy-LSTM that models the slots jointly. Our results show that they are able to alleviate the confusion mentioned above, and they push the state-of-the-art on dataset MultiWoz 2.1 from 58.7 to 61.3.
An ideal integration of autonomous agents in a human world implies that they are able to collaborate on human terms. In particular, theory of mind plays an important role in maintaining common ground during human collaboration and communication. To e
nable theory of mind modeling in situated interactions, we introduce a fine-grained dataset of collaborative tasks performed by pairs of human subjects in the 3D virtual blocks world of Minecraft. It provides information that captures partners' beliefs of the world and of each other as an interaction unfolds, bringing abundant opportunities to study human collaborative behaviors in situated language communication. As a first step towards our goal of developing embodied AI agents able to infer belief states of collaborative partners in situ, we build and present results on computational models for several theory of mind tasks.
Human conversations naturally evolve around different topics and fluently move between them. In research on dialog systems, the ability to actively and smoothly transition to new topics is often ignored. In this paper we introduce TIAGE, a new topic-
shift aware dialog benchmark constructed utilizing human annotations on topic shifts. Based on TIAGE, we introduce three tasks to investigate different scenarios of topic-shift modeling in dialog settings: topic-shift detection, topic-shift triggered response generation and topic-aware dialog generation. Experiments on these tasks show that the topic-shift signals in TIAGE are useful for topic-shift response generation. On the other hand, dialog systems still struggle to decide when to change topic. This indicates further research is needed in topic-shift aware dialog modeling.
One of the central aspects of contextualised language models is that they should be able to distinguish the meaning of lexically ambiguous words by their contexts. In this paper we investigate the extent to which the contextualised embeddings of word
forms that display multiplicity of sense reflect traditional distinctions of polysemy and homonymy. To this end, we introduce an extended, human-annotated dataset of graded word sense similarity and co-predication acceptability, and evaluate how well the similarity of embeddings predicts similarity in meaning. Both types of human judgements indicate that the similarity of polysemic interpretations falls in a continuum between identity of meaning and homonymy. However, we also observe significant differences within the similarity ratings of polysemes, forming consistent patterns for different types of polysemic sense alternation. Our dataset thus appears to capture a substantial part of the complexity of lexical ambiguity, and can provide a realistic test bed for contextualised embeddings. Among the tested models, BERT Large shows the strongest correlation with the collected word sense similarity ratings, but struggles to consistently replicate the observed similarity patterns. When clustering ambiguous word forms based on their embeddings, the model displays high confidence in discerning homonyms and some types of polysemic alternations, but consistently fails for others.
We introduce a new pretraining approach geared for multi-document language modeling, incorporating two key ideas into the masked language modeling self-supervised objective. First, instead of considering documents in isolation, we pretrain over sets
of multiple related documents, encouraging the model to learn cross-document relationships. Second, we improve over recent long-range transformers by introducing dynamic global attention that has access to the entire input to predict masked tokens. We release CDLM (Cross-Document Language Model), a new general language model for multi-document setting that can be easily applied to downstream tasks. Our extensive analysis shows that both ideas are essential for the success of CDLM, and work in synergy to set new state-of-the-art results for several multi-text tasks.
With the recent breakthrough of deep learning technologies, research on machine reading comprehension (MRC) has attracted much attention and found its versatile applications in many use cases. MRC is an important natural language processing (NLP) tas
k aiming to assess the ability of a machine to understand natural language expressions, which is typically operationalized by first asking questions based on a given text paragraph and then receiving machine-generated answers in accordance with the given context paragraph and questions. In this paper, we leverage two novel pretrained language models built on top of Bidirectional Encoder Representations from Transformers (BERT), namely BERT-wwm and MacBERT, to develop effective MRC methods. In addition, we also seek to investigate whether additional incorporation of the categorical information about a context paragraph can benefit MRC or not, which is achieved based on performing context paragraph clustering on the training dataset. On the other hand, an ensemble learning approach is proposed to harness the synergistic power of the aforementioned two BERT-based models so as to further promote MRC performance.
