United States

We present a machine-learning-guided process that can efficiently extract factor tables from unstructured rate filing documents. Our approach combines multiple deep-learning-based models that work in tandem to create structured representations of tabular data present in unstructured documents such as pdf files. This process combines CNN&#39;s to detect tables, language-based models to extract table metadata and conventional computer vision techniques to improve the accuracy of tabular data on the machine-learning side. The extracted tabular data is validated through an intuitive user interface. This process, which we call Harvest, significantly reduces the time needed to extract tabular information from PDF files, enabling analysis of such data at a speed and scale that was previously unattainable.

AAAI 2022

Harvest – a System for Creating Structured Rate Filing Data from Filing PDFs

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EMNLP 2022

Welcome!
EMNLP 2022 will take place in Abu Dhabi from December 7th to December 11th, 2022. And it will be held in hybrid mode, both online and offline.

We present a machine-learning-guided process that can efficiently extract factor tables from unstructured rate filing documents. Our approach combines multiple deep-learning-based models that work in tandem to create structured representations of tabular data present in unstructured documents such as pdf files. This process combines CNN's to detect tables, language-based models to extract table metadata and conventional computer vision techniques to improve the accuracy of tabular data on the machine-learning side. The extracted tabular data is validated through an intuitive user interface. This process, which we call Harvest, significantly reduces the time needed to extract tabular information from PDF files, enabling analysis of such data at a speed and scale that was previously unattainable.

technical paper

The purpose of the AAAI conference is to promote research in artificial intelligence (AI) and scientific exchange among AI researchers, practitioners, scientists, and engineers in affiliated disciplines. 

Recent years have seen significant advances in multi-turn Spoken Language Understanding (SLU), where dialogue contexts are used to guide intent classification and slot filling. However, how to selectively incorporate dialogue contexts, such as previous utterances and dialogue acts, into multi-turn SLU still remains a substantial challenge. In this work, we propose a novel contextual SLU model for multi-turn intent classification and slot filling tasks. We introduce an adaptive global-local context fusion mechanism to selectively integrate dialogue contexts into our model. The local context fusion aligns each dialogue context using multi-head attention, while the global context fusion measures overall context contribution to intent classification and slot filling tasks. Experiments show that on two benchmark datasets, our model achieves absolute F1 score improvements of 2.73% and 2.57% for the slot filling task on Sim-R and Sim M datasets, respectively. Additional experiments on a large-scale, de-identified, in-house dataset further verify the measurable accuracy gains of our proposed model.

Adaptive Global-Local Context Fusion for Multi-Turn Spoken Language Understanding

We approach the task of network congestion control in datacenters using Reinforcement Learning (RL). Successful congestion control algorithms can dramatically improve latency and overall network throughput. Until today, no such learning-based algorithms have shown practical potential in this domain. Evidently, the most popular recent deployments rely on rule-based heuristics that are tested on a predetermined set of benchmarks. Consequently, these heuristics do not generalize well to newly-seen scenarios. Contrarily, we devise an RL-based algorithm with the aim of generalizing to different configurations of real-world datacenter networks. We overcome challenges such as partial-observability, non-stationarity, and multi-objectiveness. We further propose a policy gradient algorithm that leverages the analytical structure of the reward function to approximate its derivative and improve stability. We show that these challenges prevent standard RL algorithms from operating within this domain. Our experiments, conducted on a realistic simulator that emulates communication networks' behavior, show that our method exhibits improved performance concurrently on the multiple considered metrics compared to the popular algorithms deployed today in real datacenters. Our algorithm is being productized to replace heuristics in some of the largest datacenters in the world.

Reinforcement Learning for Datacenter Congestion Control

The cost of developing new drugs is estimated at billions of dollars per year. Identification of new molecules for drugs involves scanning existing bio-medical literature for relevant information. As the potential drug molecule is novel, retrieval of relevant information using a simple direct search is less likely to be productive. Identifying relevant papers is therefore a more complex and challenging task, which requires searching for information on molecules with similar characteristics to the novel drug. In this paper, we present the novel task of ranking documents based on novel molecule queries. Given a chemical molecular structure, we wish to rank medical papers that will contribute to a researcher's understanding of the novel molecule drug potential.
We present a set of ranking algorithms and molecular embeddings to address the task. An extensive evaluation of the algorithms is performed over the molecular embeddings, studying their performance on a benchmark retrieval corpus, which we share with the community.
Additionally, we introduce a heterogeneous edge-labeled graph embedding approach to address the molecule ranking task. Our evaluation shows that the proposed embedding model can significantly improve molecule ranking methods. The system is currently deployed in a targeted drug delivery and personalized medicine research laboratory.

Learning to Rank Articles for Molecular Queries

Product allocation in retail is the process of placing products throughout a store to connect consumers with relevant products. Discovering a good allocation strategy is challenging due to the scarcity of data and the high cost of experimentation in the physical world. Some work explores Reinforcement learning (RL) as a solution, but these approaches are often limited because of the sim2real problem. Learning policies from logged trajectories of a system is a key step forward for RL in physical systems. Recent work has shown that model-based offline RL can improve the effectiveness of offline policy estimation through uncertainty-penalized exploration. However, existing work assumes a continuous state space and access to a covariance matrix of the environment dynamics, which is not possible in the discrete case. To solve this problem, we propose a Bayesian model-based technique that naturally produces probabilistic estimates of the environment dynamics via the posterior predictive distribution, which we use for uncertainty-penalized exploration. We call our approach Posterior Penalized Offline Policy Optimization (PPOPO). We show that our world model better fits historical data due to informative priors, and that PPOPO outperforms other offline techniques in simulation and against real-world data.

Bayesian Model-Based Offline Reinforcement Learning for Product Allocation

Fuzzing is a widely-used testing technique to assure software robustness. However, automatic generation of high-quality test suites is challenging, especially for software that takes in highly-structured inputs, such as the compilers. Compiler fuzzing remains difficult as generating tons of syntactically and semantically valid programs is not trivial. Most previous methods either depend on human-crafted grammars or heuristics to learn partial language patterns. They both suffer from the completeness issue that is a classic puzzle in software testing. To mitigate the problem, we propose a knowledge-guided reinforcement learning-based approach to generating valid programs for compiler fuzzing. We first design a naive learning model which evolves with the sequential mutation rewards provided by a target compiler we test. By iterating the training cycle, the model learns to generate valid programs that can improve the testing efficacy as well. We implement the proposed method into a tool called ALPHAPROG. We analyze the framework with four different reward functions and our study reveal the effectiveness of  ALPHAPROG for compiler testing. We also reported two important bugs for a compiler production that were confirmed and addressed by the project owner, which further demonstrates ALPHAPROG's applied value in practice.

ALPHAPROG: Reinforcement Generation of Valid Programs for Compiler Fuzzing

African smallholder farmers have struggled with low agricultural productivity for decades, partly due to their inability to proactively assess irrigation needs in their farms in the face of long-term climate change. In this paper, we tackle this challenge by employing data-driven techniques to develop forecasting tools for three widely used crop-productivity related variables (i.e., actual evapotranspiration, reference evapotranspiration, and net primary production), which can then be used by farmers to take corrective actions on their farms. Prior work in this domain, despite using data-driven methods, suffers from two major limitations: (i) they mainly focus on estimating variable values (as opposed to forecasting the future); and (ii) they mostly use classical Machine Learning (ML) prediction models, despite the abundance of data sufficient to train sophisticated deep learning models. To fill this research gap, we collaborate with PlantVillage, the world's leading non-profit agricultural knowledge delivery platform for African farmers, to identify âˆ¼2,200 smallholder farm locations, and gather remote-sensed data of these farms over a period of five years. Next, we propose CLIMATES, a meta-algorithm leveraging structural insights about temporal patterns of this time-series data to accurately forecast their future values. We conduct extensive experiments to evaluate its performance in this domain. Our experimental results show that CLIMATES outperforms several state-of-the-art time-series forecasting models. We also provide insights about the poor performance of some competing models. Our work is being evaluated by officials at PlantVillage for potential future deployment as an early warning system in East Africa. We release the code at https://github.com/maryam-tabar/CLIMATES.

Mitigating Low Agricultural Productivity of Smallholder Farms in Africa: Time-Series Forecasting for Environmental Stressors

Micronutrient deficiency (MND), which is a form of malnutrition that can have serious health consequences, is difficult to diagnose in early stages without blood draws, which are expensive and time-consuming to collect and process. It is even more difficult at a public health scale seeking to identify regions at higher risk of MND. To provide data more widely and frequently, we propose an accurate, scalable, low-cost, and interpretable regional-level MND prediction system. Specifically, our work is the first to use satellite data, such as forest cover, weather, and presence of water, to predict deficiency of micronutrients such as iron, Vitamin B12, and Vitamin A, directly from their biomarkers. We use real-world, ground truth biomarker data collected from four different regions across Madagascar for training, and demonstrate that satellite data are viable for predicting regional-level MND, surprisingly exceeding the performance of baseline predictions based only on survey responses. Our method could be broadly applied to other countries where satellite data are available, and potentially create high societal impact if these predictions are used by policy makers, public health officials, or healthcare providers.

Micronutrient Deficiency Prediction via Publicly Available Satellite Data

We propose PaintTeR, our Paintings TextRank algorithm for extracting art-related text spans from passages on paintings. PaintTeR combines a lexicon of painting words curated automatically through distant supervision with random walks on a large-scale word co-occurrence graph for ranking passage spans for artistic characteristics. The spans extracted with PaintTeR are used in state-of-the-art Question Generation and Reading Comprehension models for designing an interactive aid that enables gallery and museum visitors focus on the artistic elements of paintings. We provide experiments on two datasets of expert-written passages on paintings to showcase the effectiveness of PaintTeR. Evaluations by both gallery experts as well as crowdworkers indicate that our proposed algorithm can be used to
select relevant and interesting art-centered questions. To the best of our knowledge, ours is the first work to effectively fine-tune question generation models using minimal supervision for a low-resource, specialized context such as gallery visits.

PaintTeR: Automatic Extraction of Text Spans for Generating Art-Centered Questions

As climate change is increasing the frequency and intensity of climate and weather hazards, improving detection and monitoring of flood events is a priority. Being weather independent and high resolution, Sentinel 1 (S1) radar satellite imagery data has become the go to data source to detect flood events accurately. However, current methods are either based on fixed thresholds to differentiate water from land or train Artificial Intelligence (AI) models based on only S1 data, despite the availability of many other relevant data sources publicly. These models also lack comprehensive validations on out-of-sample data and deployment at scale.  In this study, we investigated whether adding extra input layers could increase the performance of AI models in detecting floods from S1 data. We also provide performance across a range of 11 historical events, with results ranging between 0.93 and 0.97 accuracy, 0.53 and 0.81 IoU, and 0.68 and 0.89 F1 scores. Finally, we show the infrastructure we developed to deploy our AI models at scale to satisfy a range of use cases and user requests.

Deploying an Artificial Intelligence Application to Detect Flood from Sentinel 1 Data

In recent years, companies in the Architecture, Engineering, and Construction (AEC) industry have started exploring how artificial intelligence (AI) can reduce time-consuming and repetitive tasks. One use case that can benefit from the adoption of AI is the determination of quantities in floor plans. This information is required for several planning and construction steps. Currently, the task requires companies to invest a significant amount of manual effort. Either digital floor plans are not available for existing buildings, or the formats cannot be processed due to lack of standardization. In this paper, we therefore propose a human-in-the-loop approach for the detection and classification of symbols in floor plans. The developed system calculates a measure of uncertainty for each detected symbol which is used to acquire the knowledge of human experts for those symbols that are difficult to classify. We evaluate our approach with a real-world dataset provided by an industry partner and find that the selective acquisition of human expert knowledge enhances the model's performance by up to 10.5%â€”resulting in an overall prediction accuracy of 92.1% on average. We further design a pipeline for the generation of synthetic training data that allows the systems to be adapted to new construction projects with minimal manual effort. Overall, our work supports professionals in the AEC industry on their journey to the data-driven generation of business value.

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Adaptive Global-Local Context Fusion for Multi-Turn Spoken Language Understanding

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