Optimizing Audio Recommendations for the Long-Term: A Reinforcement Learning Perspective

We study the problem of optimizing a recommender system for outcomes that occur over several weeks or months. We begin by drawing on reinforcement learning to formulate a comprehensive model of users' recurring relationships with a recommender system. Measurement, attribution, and coordination challenges complicate algorithm design. We describe careful modeling -- including a new representation of user state and key conditional independence assumptions -- which overcomes these challenges and leads to simple, testable recommender system prototypes. We apply our approach to a podcast recommender system that makes personalized recommendations to hundreds of millions of listeners. A/B tests demonstrate that purposefully optimizing for long-term outcomes leads to large performance gains over conventional approaches that optimize for short-term proxies.

CellMix: A General Instance Relationship based Method for Data Augmentation Towards Pathology Image Analysis

Pathology image analysis crucially relies on the availability and quality of annotated pathological samples, which are very difficult to collect and need lots of human effort. To address this issue, beyond traditional preprocess data augmentation methods, mixing-based approaches are effective and practical. However, previous mixing-based data augmentation methods do not thoroughly explore the essential characteristics of pathology images, including the local specificity, global distribution, and inner/outer-sample instance relationship. To further understand the pathology characteristics and make up effective pseudo samples, we propose the CellMix framework with a novel distribution-based in-place shuffle strategy. We split the images into patches with respect to the granularity of pathology instances and do the shuffle process across the same batch. In this way, we generate new samples while keeping the absolute relationship of pathology instances intact. Furthermore, to deal with the perturbations and distribution-based noise, we devise a loss-drive strategy inspired by curriculum learning during the training process, making the model fit the augmented data adaptively. It is worth mentioning that we are the first to explore data augmentation techniques in the pathology image field. Experiments show SOTA results on 7 different datasets. We conclude that this novel instance relationship-based strategy can shed light on general data augmentation for pathology image analysis. The code is available at https://github.com/sagizty/CellMix.

Causal conditional hidden Markov model for multimodal traffic prediction

Multimodal traffic flow can reflect the health of the transportation system, and its prediction is crucial to urban traffic management. Recent works overemphasize spatio-temporal correlations of traffic flow, ignoring the physical concepts that lead to the generation of observations and their causal relationship. Spatio-temporal correlations are considered unstable under the influence of different conditions, and spurious correlations may exist in observations. In this paper, we analyze the physical concepts affecting the generation of multimode traffic flow from the perspective of the observation generation principle and propose a Causal Conditional Hidden Markov Model (CCHMM) to predict multimodal traffic flow. In the latent variables inference stage, a posterior network disentangles the causal representations of the concepts of interest from conditional information and observations, and a causal propagation module mines their causal relationship. In the data generation stage, a prior network samples the causal latent variables from the prior distribution and feeds them into the generator to generate multimodal traffic flow. We use a mutually supervised training method for the prior and posterior to enhance the identifiability of the model. Experiments on real-world datasets show that CCHMM can effectively disentangle causal representations of concepts of interest and identify causality, and accurately predict multimodal traffic flow.

Spatio-temporal neural structural causal models for bike flow prediction

As a representative of public transportation, the fundamental issue of managing bike-sharing systems is bike flow prediction. Recent methods overemphasize the spatio-temporal correlations in the data, ignoring the effects of contextual conditions on the transportation system and the inter-regional timevarying causality. In addition, due to the disturbance of incomplete observations in the data, random contextual conditions lead to spurious correlations between data and features, making the prediction of the model ineffective in special scenarios. To overcome this issue, we propose a Spatio-temporal Neural Structure Causal Model(STNSCM) from the perspective of causality. First, we build a causal graph to describe the traffic prediction, and further analyze the causal relationship between the input data, contextual conditions, spatiotemporal states, and prediction results. Second, we propose to apply the frontdoor criterion to eliminate confounding biases in the feature extraction process. Finally, we propose a counterfactual representation reasoning module to extrapolate the spatio-temporal state under the factual scenario to future counterfactual scenarios to improve the prediction performance. Experiments on real-world datasets demonstrate the superior performance of our model, especially its resistance to fluctuations caused by the external environment. The source code and data will be released.

Graph Learning: A Comprehensive Survey and Future Directions

Graph learning aims to learn complex relationships among nodes and the topological structure of graphs, such as social networks, academic networks and e-commerce networks, which are common in the real world. Those relationships make graphs special compared with traditional tabular data in which nodes are dependent on non-Euclidean space and contain rich information to explore. Graph learning developed from graph theory to graph data mining and now is empowered with representation learning, making it achieve great performances in various scenarios, even including text, image, chemistry, and biology. Due to the broad application prospects in the real world, graph learning has become a popular and promising area in machine learning. Thousands of works have been proposed to solve various kinds of problems in graph learning and is appealing more and more attention in academic community, which makes it pivotal to survey previous valuable works. Although some of the researchers have noticed this phenomenon and finished impressive surveys on graph learning. However, they failed to link related objectives, methods and applications in a more logical way and cover current ample scenarios as well as challenging problems due to the rapid expansion of the graph learning.

AWTE-BERT:Attending to Wordpiece Tokenization Explicitly on BERT for Joint Intent Classification and SlotFilling

Intent classification and slot filling are two core tasks in natural language understanding (NLU). The interaction nature of the two tasks makes the joint models often outperform the single designs. One of the promising solutions, called BERT (Bidirectional Encoder Representations from Transformers), achieves the joint optimization of the two tasks. BERT adopts the wordpiece to tokenize each input token into multiple sub-tokens, which causes a mismatch between the tokens and the labels lengths. Previous methods utilize the hidden states corresponding to the first sub-token as input to the classifier, which limits performance improvement since some hidden semantic informations is discarded in the fine-tune process. To address this issue, we propose a novel joint model based on BERT, which explicitly models the multiple sub-tokens features after wordpiece tokenization, thereby generating the context features that contribute to slot filling. Specifically, we encode the hidden states corresponding to multiple sub-tokens into a context vector via the attention mechanism. Then, we feed each context vector into the slot filling encoder, which preserves the integrity of the sentence. Experimental results demonstrate that our proposed model achieves significant improvement on intent classification accuracy, slot filling F1, and sentence-level semantic frame accuracy on two public benchmark datasets. The F1 score of the slot filling in particular has been improved from 96.1 to 98.2 (2.1% absolute) on the ATIS dataset.

A Comprehensive Survey on Enterprise Financial Risk Analysis: Problems, Methods, Spotlights and Applications

Enterprise financial risk analysis aims at predicting the enterprises' future financial risk.Due to the wide application, enterprise financial risk analysis has always been a core research issue in finance. Although there are already some valuable and impressive surveys on risk management, these surveys introduce approaches in a relatively isolated way and lack the recent advances in enterprise financial risk analysis. Due to the rapid expansion of the enterprise financial risk analysis, especially from the computer science and big data perspective, it is both necessary and challenging to comprehensively review the relevant studies. This survey attempts to connect and systematize the existing enterprise financial risk researches, as well as to summarize and interpret the mechanisms and the strategies of enterprise financial risk analysis in a comprehensive way, which may help readers have a better understanding of the current research status and ideas. This paper provides a systematic literature review of over 300 articles published on enterprise risk analysis modelling over a 50-year period, 1968 to 2022. We first introduce the formal definition of enterprise risk as well as the related concepts. Then, we categorized the representative works in terms of risk type and summarized the three aspects of risk analysis. Finally, we compared the analysis methods used to model the enterprise financial risk. Our goal is to clarify current cutting-edge research and its possible future directions to model enterprise risk, aiming to fully understand the mechanisms of enterprise risk communication and influence and its application on corporate governance, financial institution and government regulation.

TSMind: Alibaba and Soochow University's Submission to the WMT22 Translation Suggestion Task

This paper describes the joint submission of Alibaba and Soochow University, TSMind, to the WMT 2022 Shared Task on Translation Suggestion (TS). We participate in the English-German and English-Chinese tasks. Basically, we utilize the model paradigm fine-tuning on the downstream tasks based on large-scale pre-trained models, which has recently achieved great success. We choose FAIR's WMT19 English-German news translation system and MBART50 for English-Chinese as our pre-trained models. Considering the task's condition of limited use of training data, we follow the data augmentation strategies proposed by WeTS to boost our TS model performance. The difference is that we further involve the dual conditional cross-entropy model and GPT-2 language model to filter augmented data. The leader board finally shows that our submissions are ranked first in three of four language directions in the Naive TS task of the WMT22 Translation Suggestion task.

Learning with Noisy Labels over Imbalanced Subpopulations

Learning with Noisy Labels (LNL) has attracted significant attention from the research community. Many recent LNL methods rely on the assumption that clean samples tend to have "small loss". However, this assumption always fails to generalize to some real-world cases with imbalanced subpopulations, i.e., training subpopulations varying in sample size or recognition difficulty. Therefore, recent LNL methods face the risk of misclassifying those "informative" samples (e.g., hard samples or samples in the tail subpopulations) into noisy samples, leading to poor generalization performance. To address the above issue, we propose a novel LNL method to simultaneously deal with noisy labels and imbalanced subpopulations. It first leverages sample correlation to estimate samples' clean probabilities for label correction and then utilizes corrected labels for Distributionally Robust Optimization (DRO) to further improve the robustness. Specifically, in contrast to previous works using classification loss as the selection criterion, we introduce a feature-based metric that takes the sample correlation into account for estimating samples' clean probabilities. Then, we refurbish the noisy labels using the estimated clean probabilities and the pseudo-labels from the model's predictions. With refurbished labels, we use DRO to train the model to be robust to subpopulation imbalance. Extensive experiments on a wide range of benchmarks demonstrate that our technique can consistently improve current state-of-the-art robust learning paradigms against noisy labels, especially when encountering imbalanced subpopulations.

Easy Guided Decoding in Providing Suggestions for Interactive Machine Translation

Fully machine translation scarcely guarantees error-free results. Humans perform post-editing on machine generated translations to correct errors in the scenario of computer aided translation. In favor of expediting the post-editing process, recent works have investigated machine translation in an interactive mode, where machines can automatically refine the rest of translations constrained on human's edits. In this paper, we utilize the parameterized objective function of neural machine translation and propose an easy constrained decoding algorithm to improve the translation quality without additional training. We demonstrate its capability and time efficiency on a benchmark dataset, WeTS, where it conditions on humans' guidelines by selecting spans with potential errors. In the experimental results, our algorithm is significantly superior to state-of-the-art lexically constrained decoding method by an increase of 10.37 BLEU in translation quality and a decrease of 63.4% in time cost on average. It even outperforms the benchmark systems trained with a large amount of annotated data on WeTS in English-German and German-English.