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1. Task Programming: Learning Data Efficient Behavior Representations

   https://doi.org/10.1109/CVPR4643.2021.00290  

   Sun, Jennifer J.; Kennedy, Ann; Zhan, Eric; Anderson, David J.; Yue, Yisong; Perona, Pietro (October 2021, 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR))

   Specialized domain knowledge is often necessary to ac- curately annotate training sets for in-depth analysis, but can be burdensome and time-consuming to acquire from do- main experts. This issue arises prominently in automated behavior analysis, in which agent movements or actions of interest are detected from video tracking data. To reduce annotation effort, we present TREBA: a method to learn annotation-sample efficient trajectory embedding for be- havior analysis, based on multi-task self-supervised learn- ing. The tasks in our method can be efficiently engineered by domain experts through a process we call “task program- ming”, which uses programs to explicitly encode structured knowledge from domain experts. Total domain expert effort can be reduced by exchanging data annotation time for the construction of a small number of programmed tasks. We evaluate this trade-off using data from behavioral neuro- science, in which specialized domain knowledge is used to identify behaviors. We present experimental results in three datasets across two domains: mice and fruit flies. Using embeddings from TREBA, we reduce annotation burden by up to a factor of 10 without compromising accuracy com- pared to state-of-the-art features. Our results thus suggest that task programming and self-supervision can be an ef- fective way to reduce annotation effort for domain experts. 

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2. Enhancing Biomedical Semantic Annotations through a Knowledge Graph-Based Approach

   https://doi.org/10.32473/flairs.36.133253  

   Abbas, Asim; Khalid, Mutahira; Chalarca, Sebastian; Keshtkar, Fazel; Bukhari, Syed Ahmad (May 2023, The International FLAIRS Conference Proceedings)

   An abundance of biomedical data is generated in the form of clinical notes, reports, and research articles available online. This data holds valuable information that requires extraction, retrieval, and transformation into actionable knowledge. However, this information has various access challenges due to the need for precise machine-interpretable semantic metadata required by search engines. Despite search engines' efforts to interpret the semantics information, they still struggle to index, search, and retrieve relevant information accurately. To address these challenges, we propose a novel graph-based semantic knowledge-sharing approach to enhance the quality of biomedical semantic annotation by engaging biomedical domain experts. In this approach, entities in the knowledge-sharing environment are interlinked and play critical roles. Authorial queries can be posted on the "Knowledge Cafe," and community experts can provide recommendations for semantic annotations. The community can further validate and evaluate the expert responses through a voting scheme resulting in a transformed "Knowledge Cafe" environment that functions as a knowledge graph with semantically linked entities. We evaluated the proposed approach through a series of scenarios, resulting in precision, recall, F1-score, and accuracy assessment matrices. Our results showed an acceptable level of accuracy at approximately 90%. The source code for "Semantically" is freely available at: https://github.com/bukharilab/Semantically 

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3. Enhancing Biomedical Semantic Annotations through a Knowledge Graph-Based Approach

   https://doi.org/10.32473/flairs.36.133253  

   Abbas, A.; Khalid, M; Chalarca, S; Keshtkar, F; Bukhari, S. A. (August 2023, VOL. 36 (2023): PROCEEDINGS OF FLAIRS-36)

   An abundance of biomedical data is generated in the form of clinical notes, reports, and research articles available online. This data holds valuable information that requires extraction, retrieval, and transformation into actionable knowledge. However, this information has various access challenges due to the need for precise machine-interpretable semantic metadata required by search engines. Despite search engines' efforts to interpret the semantics information, they still struggle to index, search, and retrieve relevant information accurately. To address these challenges, we propose a novel graph-based semantic knowledge-sharing approach to enhance the quality of biomedical semantic annotation by engaging biomedical domain experts. In this approach, entities in the knowledge-sharing environment are interlinked and play critical roles. Authorial queries can be posted on the "Knowledge Cafe," and community experts can provide recommendations for semantic annotations. The community can further validate and evaluate the expert responses through a voting scheme resulting in a transformed "Knowledge Cafe" environment that functions as a knowledge graph with semantically linked entities. We evaluated the proposed approach through a series of scenarios, resulting in precision, recall, F1-score, and accuracy assessment matrices. Our results showed an acceptable level of accuracy at approximately 90%. The source code for "Semantically" is freely available at: https://github.com/bukharilab/Semantically 

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4. Extracting Lexical Features from Dialects via Interpretable Dialect Classifiers

   Xie, Roy; Ahia, Orevaoghene; Tsvetkov, Yulia; Anastasopoulos, Antonios (June 2024, NAACL)

   Identifying linguistic differences between dialects of a language often requires expert knowledge and meticulous human analysis. This is largely due to the complexity and nuance involved in studying various dialects. We present a novel approach to extract distinguishing lexical features of dialects by utilizing interpretable dialect classifiers, even in the absence of human experts. We explore both posthoc and intrinsic approaches to interpretability, conduct experiments on Mandarin, Italian, and Low Saxon, and experimentally demonstrate that our method successfully identifies key language-specific lexical features that contribute to dialectal variations 

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5. Self-Supervised Knowledge Assimilation for Expert-Layman Text Style Transfer

   https://doi.org/10.1609/aaai.v36i10.21410  

   Xu, Wenda; Saxon, Michael; Sra, Misha; Wang, William Yang (June 2022, Proceedings of the AAAI Conference on Artificial Intelligence)

   Expert-layman text style transfer technologies have the potential to improve communication between members of scientific communities and the general public. High-quality information produced by experts is often filled with difficult jargon laypeople struggle to understand. This is a particularly notable issue in the medical domain, where layman are often confused by medical text online. At present, two bottlenecks interfere with the goal of building high-quality medical expert-layman style transfer systems: a dearth of pretrained medical-domain language models spanning both expert and layman terminologies and a lack of parallel corpora for training the transfer task itself. To mitigate the first issue, we propose a novel language model (LM) pretraining task, Knowledge Base Assimilation, to synthesize pretraining data from the edges of a graph of expert- and layman-style medical terminology terms into an LM during self-supervised learning. To mitigate the second issue, we build a large-scale parallel corpus in the medical expert-layman domain using a margin-based criterion. Our experiments show that transformer-based models pretrained on knowledge base assimilation and other well-established pretraining tasks fine-tuning on our new parallel corpus leads to considerable improvement against expert-layman transfer benchmarks, gaining an average relative improvement of our human evaluation, the Overall Success Rate (OSR), by 106%. 

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