"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.
We present GerDaLIR, a German Dataset for Legal Information Retrieval based on case documents from the open legal information platform Open Legal Data. The dataset consists of 123K queries, each labelled with at least one relevant document in a colle
ction of 131K case documents. We conduct several baseline experiments including BM25 and a state-of-the-art neural re-ranker. With our dataset, we aim to provide a standardized benchmark for German LIR and promote open research in this area. Beyond that, our dataset comprises sufficient training data to be used as a downstream task for German or multilingual language models.
A possible explanation for the impressive performance of masked language model (MLM) pre-training is that such models have learned to represent the syntactic structures prevalent in classical NLP pipelines. In this paper, we propose a different expla
nation: MLMs succeed on downstream tasks almost entirely due to their ability to model higher-order word co-occurrence statistics. To demonstrate this, we pre-train MLMs on sentences with randomly shuffled word order, and show that these models still achieve high accuracy after fine-tuning on many downstream tasks---including tasks specifically designed to be challenging for models that ignore word order. Our models perform surprisingly well according to some parametric syntactic probes, indicating possible deficiencies in how we test representations for syntactic information. Overall, our results show that purely distributional information largely explains the success of pre-training, and underscore the importance of curating challenging evaluation datasets that require deeper linguistic knowledge.
Zero-shot cross-lingual information extraction (IE) describes the construction of an IE model for some target language, given existing annotations exclusively in some other language, typically English. While the advance of pretrained multilingual enc
oders suggests an easy optimism of train on English, run on any language'', we find through a thorough exploration and extension of techniques that a combination of approaches, both new and old, leads to better performance than any one cross-lingual strategy in particular. We explore techniques including data projection and self-training, and how different pretrained encoders impact them. We use English-to-Arabic IE as our initial example, demonstrating strong performance in this setting for event extraction, named entity recognition, part-of-speech tagging, and dependency parsing. We then apply data projection and self-training to three tasks across eight target languages. Because no single set of techniques performs the best across all tasks, we encourage practitioners to explore various configurations of the techniques described in this work when seeking to improve on zero-shot training.
Although olfactory references play a crucial role in our cultural memory, only few works in NLP have tried to capture them from a computational perspective. Currently, the main challenge is not much the development of technological components for olf
actory information extraction, given recent advances in semantic processing and natural language understanding, but rather the lack of a theoretical framework to capture this information from a linguistic point of view, as a preliminary step towards the development of automated systems. Therefore, in this work we present the annotation guidelines, developed with the help of history scholars and domain experts, aimed at capturing all the relevant elements involved in olfactory situations or events described in texts. These guidelines have been inspired by FrameNet annotation, but underwent some adaptations, which are detailed in this paper. Furthermore, we present a case study concerning the annotation of olfactory situations in English historical travel writings describing trips to Italy. An analysis of the most frequent role fillers show that olfactory descriptions pertain to some typical domains such as religion, food, nature, ancient past, poor sanitation, all supporting the creation of a stereotypical imagery related to Italy. On the other hand, positive feelings triggered by smells are prevalent, and contribute to framing travels to Italy as an exciting experience involving all senses.
Current abstractive summarization systems outperform their extractive counterparts, but their widespread adoption is inhibited by the inherent lack of interpretability. Extractive summarization systems, though interpretable, suffer from redundancy an
d possible lack of coherence. To achieve the best of both worlds, we propose EASE, an extractive-abstractive framework that generates concise abstractive summaries that can be traced back to an extractive summary. Our framework can be applied to any evidence-based text generation problem and can accommodate various pretrained models in its simple architecture. We use the Information Bottleneck principle to jointly train the extraction and abstraction in an end-to-end fashion. Inspired by previous research that humans use a two-stage framework to summarize long documents (Jing and McKeown, 2000), our framework first extracts a pre-defined amount of evidence spans and then generates a summary using only the evidence. Using automatic and human evaluations, we show that the generated summaries are better than strong extractive and extractive-abstractive baselines.
Finding informative COVID-19 posts in a stream of tweets is very useful to monitor health-related updates. Prior work focused on a balanced data setup and on English, but informative tweets are rare, and English is only one of the many languages spok
en in the world. In this work, we introduce a new dataset of 5,000 tweets for finding informative COVID-19 tweets for Danish. In contrast to prior work, which balances the label distribution, we model the problem by keeping its natural distribution. We examine how well a simple probabilistic model and a convolutional neural network (CNN) perform on this task. We find a weighted CNN to work well but it is sensitive to embedding and hyperparameter choices. We hope the contributed dataset is a starting point for further work in this direction.
Pimentel et al. (2020) recently analysed probing from an information-theoretic perspective. They argue that probing should be seen as approximating a mutual information. This led to the rather unintuitive conclusion that representations encode exactl
y the same information about a target task as the original sentences. The mutual information, however, assumes the true probability distribution of a pair of random variables is known, leading to unintuitive results in settings where it is not. This paper proposes a new framework to measure what we term Bayesian mutual information, which analyses information from the perspective of Bayesian agents---allowing for more intuitive findings in scenarios with finite data. For instance, under Bayesian MI we have that data can add information, processing can help, and information can hurt, which makes it more intuitive for machine learning applications. Finally, we apply our framework to probing where we believe Bayesian mutual information naturally operationalises ease of extraction by explicitly limiting the available background knowledge to solve a task.
Many recent works use consistency regularisation' to improve the generalisation of fine-tuned pre-trained models, both multilingual and English-only. These works encourage model outputs to be similar between a perturbed and normal version of the inpu
t, usually via penalising the Kullback--Leibler (KL) divergence between the probability distribution of the perturbed and normal model. We believe that consistency losses may be implicitly regularizing the loss landscape. In particular, we build on work hypothesising that implicitly or explicitly regularizing trace of the Fisher Information Matrix (FIM), amplifies the implicit bias of SGD to avoid memorization. Our initial results show both empirically and theoretically that consistency losses are related to the FIM, and show that the flat minima implied by a small trace of the FIM improves performance when fine-tuning a multilingual model on additional languages. We aim to confirm these initial results on more datasets, and use our insights to develop better multilingual fine-tuning techniques.
Adapter layers are lightweight, learnable units inserted between transformer layers. Recent work explores using such layers for neural machine translation (NMT), to adapt pre-trained models to new domains or language pairs, training only a small set
of parameters for each new setting (language pair or domain). In this work we study the compositionality of language and domain adapters in the context of Machine Translation. We aim to study, 1) parameter-efficient adaptation to multiple domains and languages simultaneously (full-resource scenario) and 2) cross-lingual transfer in domains where parallel data is unavailable for certain language pairs (partial-resource scenario). We find that in the partial resource scenario a naive combination of domain-specific and language-specific adapters often results in catastrophic forgetting' of the missing languages. We study other ways to combine the adapters to alleviate this issue and maximize cross-lingual transfer. With our best adapter combinations, we obtain improvements of 3-4 BLEU on average for source languages that do not have in-domain data. For target languages without in-domain data, we achieve a similar improvement by combining adapters with back-translation. Supplementary material is available at https://tinyurl.com/r66stbxj.
