Autism Spectrum Disorder (ASD) is a neurological and developmental disability that affects how an individual learns, communicates, interacts with others. Applied Behavior Analysis (ABA) is a gold standard therapy for children and adults suffering from ASD to improve their learning, social, and communication skills. Today, 1 in 36 children are diagnosed with ASD with expectations that this rate will only continue to rise. The supply of certified ABA providers is alarmingly insufficient to meet the needs of children with ASD. In fact, waitlists to receive ABA therapy in the United States exceed 10 months in most states. Clinicians or Board Certified Behavior Analysts (BCBAs) are now experiencing intense bottlenecks around diagnostic evaluations and developing treatment plans quickly enough to support timely access to care. Over the past few years, Artificial Intelligence has changed the way industries operate by offering powerful ways to process, analyze, generate, and predict data. In this paper, we have addressed the problem of both time and supply restrictions faced by ABA providers by proposing a novel method for personalized treatment plan generation and program prediction by leveraging the capabilities of Deep Learning and Large Language Models (LLM). Additionally, we have introduced two separate models for behavior program prediction (F1-Score: 0.671) and skill acquisition program predictions (Rouge-1 Score: 0.476) which will help ABA providers in treatment plan implementation. Results are promising: an AI-generated treatment plan demonstrates a high similarity (Average Similarity Score: 0.915) to the original treatment plan written by a BCBA. Finally, as we partnered with a multi-state ABA provider in building this product, we ran a single-blind study that concluded that BCBAs prefer an AI-generated treatment plan 65 percent of the time compared to a BCBA-generated one.

Natural Language Generation (NLG) for non-English languages is hampered by the scarcity of datasets in these languages. We present the IndicNLG Benchmark, a collection of datasets for benchmarking NLG for 11 Indic languages. We focus on five diverse tasks, namely, biography generation using Wikipedia infoboxes, news headline generation, sentence summarization, paraphrase generation and, question generation. We describe the created datasets and use them to benchmark the performance of several monolingual and multilingual baselines that leverage pre-trained sequence-to-sequence models. Our results exhibit the strong performance of multilingual language-specific pre-trained models, and the utility of models trained on our dataset for other related NLG tasks. Our dataset creation methods can be easily applied to modest-resource languages as they involve simple steps such as scraping news articles and Wikipedia infoboxes, light cleaning, and pivoting through machine translation data. To the best of our knowledge, the IndicNLG Benchmark is the first NLG benchmark for Indic languages and the most diverse multilingual NLG dataset, with approximately 8M examples across 5 tasks and 11 languages. The datasets and models will be publicly available.

As the demands for large-scale information processing have grown, knowledge graph-based approaches have gained prominence for representing general and domain knowledge. The development of such general representations is essential, particularly in domains such as manufacturing which intelligent processes and adaptive education can enhance. Despite the continuous accumulation of text in these domains, the lack of structured data has created information extraction and knowledge transfer barriers. In this paper, we report on work towards developing robust knowledge graphs based upon entity and relation data for both commercial and educational uses. To create the FabKG (Manufacturing knowledge graph), we have utilized textbook index words, research paper keywords, FabNER (manufacturing NER), to extract a sub knowledge base contained within Wikidata. Moreover, we propose a novel crowdsourcing method for KG creation by leveraging student notes, which contain invaluable information but are not captured as meaningful information, excluding their use in personal preparation for learning and written exams. We have created a knowledge graph containing 65000+ triples using all data sources. We have also shown the use case of domain-specific question answering and expression/formula-based question answering for educational purposes.

In this study we develop a system that tags and extracts financial concepts called financial named entities (FNE) along with corresponding numeric values – monetary and temporal. We employ machine learning and natural language processing methods to identify financial concepts and dates, and link them to numerical entities.