United States

Automatic Text Scoring (ATS) is a widely-investigated task in education. Existing approaches often stressed the structure design of an ATS model and neglected the training process of the model. Considering the difficult nature of this task, we argued that the performance of an ATS model could be potentially boosted by carefully selecting data of varying complexities in the training process. Therefore, we aimed to investigate the effectiveness of curriculum learning (CL) in scoring educational text. Specifically, we designed two types of difficulty measurers: (i) pre-defined, calculated by measuring a sample&#39;s readability, length, the number of grammatical errors or unique words it contains; and (ii) automatic, calculated based on whether a model in a training epoch can accurately score the samples. These measurers were tested in both the easy-to-hard to hard-to-easy training paradigms. Through extensive evaluations on two widely-used datasets (one for short answer scoring and the other for long essay scoring), we demonstrated that (a) CL indeed could boost the performance of state-of-the-art ATS models, and the maximum improvement could be up to 4.5%, but most improvements were achieved when assessing short and easy answers; (b) the pre-defined measurer calculated based on the number of grammatical errors contained in a text sample tended to outperform the other difficulty measurers across different training paradigms.

AAAI 2023

On the Effectiveness of Curriculum Learning in Educational Text Scoring

automatic text scoring

curriculum learning

bert

stages 

COLING, the International Conference on Computational Linguistics, is one of the premier conferences for natural language processing and computational linguistics. Often grouped within the field of artificial intelligence, but actually pre-dating the development of artificial intelligence, advances in computational linguistics and natural language processing are now some of the major drivers behind the use of artificial intelligence for commercial and social applications – for example, on-line search, machine translation and with voice-assisted conversational devices.

First established in 1965, the biennial COLING conference is held in diverse parts of the globe and attracts participants from both top-ranked research centers and emerging countries. Today, the most important developments in our field are taking place not only in universities and academic research institutes, but also in industrial research departments and in technological startups. COLING conferences provide opportunities for all these communities to showcase their exciting developments.

COLING 2020

COLING, the International Conference on Computational Linguistics, is one of the premier conferences for natural language processing and computational linguistics. Often grouped within the field of artificial intelligence, but actually pre-dating the development of artificial intelligence, advances in computational linguistics and natural language processing are now some of the major drivers behind the use of artificial intelligence for commercial and social applications – for example, on-line search, machine translation and with voice-assisted conversational devices.

Automatic Text Scoring (ATS) is a widely-investigated task in education. Existing approaches often stressed the structure design of an ATS model and neglected the training process of the model. Considering the difficult nature of this task, we argued that the performance of an ATS model could be potentially boosted by carefully selecting data of varying complexities in the training process. Therefore, we aimed to investigate the effectiveness of curriculum learning (CL) in scoring educational text. Specifically, we designed two types of difficulty measurers: (i) pre-defined, calculated by measuring a sample's readability, length, the number of grammatical errors or unique words it contains; and (ii) automatic, calculated based on whether a model in a training epoch can accurately score the samples. These measurers were tested in both the easy-to-hard to hard-to-easy training paradigms. Through extensive evaluations on two widely-used datasets (one for short answer scoring and the other for long essay scoring), we demonstrated that (a) CL indeed could boost the performance of state-of-the-art ATS models, and the maximum improvement could be up to 4.5%, but most improvements were achieved when assessing short and easy answers; (b) the pre-defined measurer calculated based on the number of grammatical errors contained in a text sample tended to outperform the other difficulty measurers across different training paradigms.

poster

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-23 is the Thirty-Seventh AAAI Conference on Artificial Intelligence. The theme of this conference is to create collaborative bridges within and beyond AI. Like previous AAAI conferences, AAAI-23 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and two new activities: a Bridge Program and a Lab Program. Many of these activities are tailored to the theme of bridges and all are selected according to the highest standards, with additional programs for students and young researchers. 
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The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines.

There has been increasing concern within the machine learning community and beyond that Artificial Intelligence (AI) faces a bias and discrimination crisis which needs AI fairness with urgency. As many have begun to work on this problem, most existing work depends on the availability of class label for the given fairness definition and algorithm which may not align with real-world usage. In this work, we study an AI fairness problem that stems from the gap between the design of a ``fair'' model in the lab and its deployment in the real-world. Specifically, we consider defining and mitigating individual unfairness as a survival analysis problem, where the availability of class label is not always guaranteed due to censorship, which is broadly applicable in a diversity of real-world, socially-sensitive applications. We show that our method is able to quantify and mitigate individual unfairness in the presence of censorship across four benchmark tasks, which provides the first known results on individual fairness guarantee in analysis of censored data.

Censored Fairness through Awareness

A significant cause of air pollution in urban areas worldwide is the high volume of road traffic. Long-term exposure to severe pollution can cause serious health issues. One approach towards tackling this problem is to design a pollution-aware traffic routing policy that balances multiple objectives of i) avoiding extreme pollution in any area ii) enabling short transit times, and iii) making effective use of the road capacities. We propose a novel sampling-based approach for this problem. We give the first construction of a Markov Chain that can sample integer max flow solutions of a planar graph, with theoretical guarantees that the probabilities depend on the aggregate transit length. We designed a traffic policy using diverse samples and simulated traffic on real-world road maps using the SUMO traffic simulator. We observe a considerable decrease in areas with severe pollution when experimented with maps of large cities across the world compared to other approaches. 

City-scale Pollution Aware Traffic Routing by Sampling Max Flows using MCMC

Recently, short video platforms have become an important channel for news sharing, but also a new breeding ground for fake news. To mitigate this problem, the research of fake news video detection has recently received a lot of attention. Existing works face two roadblocks:  the scarcity of comprehensive and large-scale datasets and insufficient utilization of multimodal information. Therefore, in this paper, we construct the largest Chinese short video dataset about fake news named FakeSV, which includes the news content, user comments, and user profiles simultaneously. To understand the characteristics of fake news videos, we conduct exploratory analyses of FakeSV from different perspectives. Moreover, we provide a new multimodal detection model named SV-FEND, which exploits the cross-modal correlations to select the most informative features and utilizes the social context information for detection. Extensive experiments evaluate the superiority of the proposed method and also provide detailed comparisons of different methods and modalities for future works.

FakeSV: A Multimodal Benchmark with Rich Social Context for Fake News Detection on Short Video Platforms

Epidemic models are powerful tools in understanding infectious disease. However, as they increase in size and complexity, they can quickly become computationally intractable. Recent progress in modelling methodology has shown that surrogate models can be used to emulate complex epidemic models with a high-dimensional parameter space. We show that deep sequence-to-sequence (seq2seq) models can serve as accurate surrogates for complex epidemic models with sequence based model parameters, effectively replicating seasonal and long-term transmission dynamics. Once trained, our surrogate can predict scenarios a several thousand times faster than the original model, making them ideal for policy exploration. We demonstrate that replacing a traditional epidemic model with a learned simulator facilitates robust Bayesian inference.

Seq2Seq Surrogates of Epidemic Models to Facilitate Bayesian Inference

Automatic diagnosis systems aim to probe for symptoms (i.e., symptom checking) and diagnose disease through multi-turn conversations with patients. Most previous works formulate it as a sequential decision process and use reinforcement learning (RL) to decide whether to inquire about symptoms or make a diagnosis. However, these RL-based methods heavily rely on the elaborate reward function and usually suffer from an unstable training process and low data efficiency. In this work, we propose an effective multi-task framework for automatic diagnosis called MTDiag. We first reformulate symptom checking as a multi-label classification task by direct supervision. Each medical dialogue is equivalently converted into multiple samples for classification, which can also help alleviate the data scarcity problem. Furthermore, we design a multi-task learning strategy to guide the symptom checking procedure with disease information and further utilize contrastive learning to better distinguish symptoms between diseases. Extensive experimental results show that our method achieves state-of-the-art performance on four public datasets with 1.7%~3.1% improvement in disease diagnosis, demonstrating the superiority of the proposed method. Additionally, our model is now deployed in an online medical consultant system as an assistant tool for real-life doctors. 

MTDiag: An Effective Multi-Task Framework for Automatic Diagnosis

Class-incremental continual learning is a core step towards developing artificial intelligence systems that can continuously adapt to changes in the environment by learning new concepts without forgetting those previously learned. This is especially needed in the medical domain where continually learning from new incoming data is required to classify an expanded set of diseases. In this work, we focus on how old knowledge can be leveraged to learn new classes without catastrophic forgetting. We propose a framework that comprises of two main components: (1) a dynamic architecture with expanding representations to preserve previously learned features and accommodate new features; and (2) a training procedure alternating between two objectives to balance the learning of new features while maintaining the model’s performance on old classes. Experiment results on multiple medical datasets show that our solution is able to achieve superior performance over state-of-the-art baselines in terms of class accuracy and forgetting.

Leveraging Old Knowledge to Continually Learn New Classes in Medical Images

Making fair decisions is crucial to ethically implementing machine learning algorithms in social settings. In this work, we consider the celebrated definition of counterfactual fairness [Kusner et al., NeurIPS, 2017]. We begin by showing that an algorithm which satisfies counterfactual fairness also satisfies demographic parity, a far simpler fairness constraint. Similarly, we show that all algorithms satisfying demographic parity can be trivially modified to satisfy counterfactual fairness. Together, our results indicate that counterfactual fairness is basically equivalent to demographic parity, which has important implications for the growing body of work on counterfactual fairness. We then validate our theoretical findings empirically, analyzing three existing algorithms for counterfactual fairness against three simple benchmarks. We find that two simple benchmark algorithms outperform all three existing algorithms---in terms of fairness, accuracy, and efficiency---on several data sets. Our analysis leads us to formalize a concrete fairness goal: to preserve the order of individuals within protected groups. We believe transparency around the ordering of individuals within protected groups makes fair algorithms more trustworthy. By design, the two simple benchmark algorithms satisfy this goal while the existing algorithms do not.

Counterfactual Fairness Is Basically Demographic Parity

In this paper, we study a daycare matching problem in Japan and report the design and implementation of a new centralized algorithm, which is going to be deployed in one municipality in the Tokyo metropolis. There are two features that make this market different from the classical hospital-doctor matching problem: i) some children are initially enrolled and prefer to be transferred to other daycare centers; ii) one family may be associated with two or more children and is allowed to submit preferences over combinations of daycare centers. We revisit some well-studied properties including individual rationality, non-wastefulness, as well as stability, and generalize them to this new setting. We design an algorithm based on integer programming (IP) that captures these properties and conduct experiments on five real-life data sets provided by three municipalities. Experimental results show that i) our algorithm performs at least as well as currently used methods in terms of numbers of matched children and blocking coalition; ii) we can find a stable outcome for all instances, although the existence of such an outcome is not guaranteed in theory.

Daycare Matching in Japan: Transfers and Siblings

Many policies in the US are determined locally, e.g., at the county-level. Local policy regimes provide flexibility between regions, but may become less effective in the presence of geographic spillovers, where populations circumvent local restrictions by traveling to less restricted regions nearby. Due to the endogenous nature of policymaking, there have been few opportunities to reliably estimate causal spillover effects or evaluate their impact on local policies. In this work, we identify a novel setting and develop a suitable methodology that allow us to make unconfounded estimates of spillover effects of local policies. Focusing on California’s Blueprint for a Safer Economy, we leverage how county-level mobility restrictions were deterministically set by public COVID-19 severity statistics, enabling a regression discontinuity design framework to estimate spillovers between counties. We estimate these effects using a mobility network with billions of timestamped edges and find significant spillover movement, with larger effects in retail, eating places, and gyms. Contrasting local and global policy regimes, our spillover estimates suggest that county-level restrictions are only 54% as effective as statewide restrictions at reducing mobility. However, an intermediate strategy of macro-county restrictions---where we optimize county partitions by solving a minimum k-cut problem on a graph weighted by our spillover estimates---can recover over 90% of statewide mobility reductions, while maintaining substantial flexibility between counties.

Estimating geographic spillover effects of COVID-19 policies from large-scale mobility networks

The soaring number of personal mobile devices and public cameras poses a threat to fundamental human rights and ethical principles. For example, the stolen of private information such as face image by malicious third parties will lead to catastrophic consequences. By manipulating appearance of face in the image, most of existing protection algorithms are effective but irreversible. Here, we propose a practical and systematic solution to invertiblely protect face information in the full-process pipeline from camera to final users. Specifically, We design a novel lightweight Flow-based Face Encryption Method (FFEM) on the local embedded system privately connected to the camera,  minimizing the risk of  eavesdropping during data transmission. FFEM uses a flow-based face encoder to encode each face to a Gaussian distribution and encrypts the encoded face feature by random rotating the Gaussian distribution with the rotation matrix is as the password. While encrypted latent-variable face  images  are sent to users through public but less reliable channels, password will be protected through more secure channels through technologies such as asymmetric encryption, blockchain, or other sophisticated security schemes. User could select to decode an image with fake faces from the encrypted image on the public channel. Only trusted users are able to recover the original face  using the encrypted matrix transmitted in secure channel. More interestingly, by  tuning Gaussian ball in latent space, we could control the fairness of the replaced face on attributes such as gender and race. Extensive experiments demonstrate that our solution could protect privacy and enhance fairness with minimal effect on high-level downstream task.

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Censored Fairness through Awareness

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