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Sequence generation demonstrates promising performance in recent information extraction efforts, by incorporating large-scale pre-trained Seq2Seq models. This paper investigates the merits of employing sequence generation in relation extraction, finding that with relation names or synonyms as generation targets, their textual semantics and the correlation (in terms of word sequence pattern) among them affect model performance. We then propose Relation Extraction with Label Augmentation (RELA), a Seq2Seq model with automatic label augmentation for RE. By saying label augmentation, we mean prod semantically synonyms for each relation name as the generation target. Besides, we present an in-depth analysis of the Seq2Seq model&#39;s behavior when dealing with RE. Experimental results show that RELA achieves competitive results compared with previous methods on four RE datasets.

AAAI 2023

Sequence Generation with Label Augmentation for Relation Extraction

label augmentation

seq2seq

relation extraction

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.

Sequence generation demonstrates promising performance in recent information extraction efforts, by incorporating large-scale pre-trained Seq2Seq models. This paper investigates the merits of employing sequence generation in relation extraction, finding that with relation names or synonyms as generation targets, their textual semantics and the correlation (in terms of word sequence pattern) among them affect model performance. We then propose Relation Extraction with Label Augmentation (RELA), a Seq2Seq model with automatic label augmentation for RE. By saying label augmentation, we mean prod semantically synonyms for each relation name as the generation target. Besides, we present an in-depth analysis of the Seq2Seq model's behavior when dealing with RE. Experimental results show that RELA achieves competitive results compared with previous methods on four RE datasets.

poster

The performances of defect inspection have been severely hindered by insufficient defect images in industries, which can be alleviated by generating more samples as data augmentation. We propose the first defect image generation method in the challenging few-shot cases. Given just a handful of defect images and relatively more defect-free ones, our goal is to augment the dataset with new defect images. Our method consists of two training stages. First, we train a data-efficient StyleGAN2 on defect-free images as the backbone. Second, we attach defect-aware residual blocks to the backbone, which learn to produce reasonable defect masks and accordingly manipulate the features within the masked regions by training the added modules on limited defect images. Extensive experiments on MVTec AD dataset not only validate the effectiveness of our method in generating realistic and diverse defect images, but also manifest the benefits it brings to downstream defect inspection tasks.

Few-Shot Defect Image Generation via Defect-Aware Feature Manipulation

Solving reachability games is a fundamental problem for the analysis, verification, and synthesis of reactive systems.
We consider logical reachability games modulo theories (in short, GMTs), i.e.,
infinite-state games whose rules are defined by logical formulas over a multi-sorted first-order theory. 
Our games have an asymmetric constraint: the safety player has at most k possible moves from each game configuration, whereas the reachability player has no such limitation.
Even though determining the winner of such a GMT is undecidable, it can be reduced to the well-studied problem of checking the satisfiability of a system of  constrained Horn clauses (CHCs), for which many off-the-shelf solvers have been developed.
Winning strategies for GMTs can also be computed by resorting to suitable CHC queries. 
We demonstrate that GMTs can model various relevant real-world games, and that our approach can effectively solve several problems from different domains, using Z3 as the backend CHC solver.

Reachability Games Modulo Theories with a Bounded Safety Player

Deep learning frameworks optimize the computation graphs and intra-operator computations to boost the inference performance on GPUs,  while inter-operator parallelism is usually ignored.  In this paper, a unified framework, AutoGraph, is proposed to obtain highly optimized computation graphs in favor of parallel executions of GPU kernels.  A novel dynamic programming algorithm, combined with backtracking search, is adopted to explore the optimal graph optimization solution, with the fast performance estimation from the mixed critical path cost. Accurate runtime information based on GPU Multi-Stream launched with CUDA Graph is utilized to determine the convergence of the optimization. Experimental results demonstrate that our method achieves up to 3.47x speedup over existing graph optimization methods. Moreover, AutoGraph outperforms state-of-the-art parallel kernel launch frameworks by up to 1.26x. 

AutoGraph: Optimizing DNN Computation Graph for Parallel GPU Kernel Execution

MinMax Multiple Travelling Salesman Problem (mTSP) is an important class of combinatorial optimization problems with many practical applications, of which the goal is to minimize the longest tour of all vehicles. Due to its high computational complexity, existing methods for solving this problem cannot obtain a solution of satisfactory quality with fast speed, especially when the scale of the problem is large. In this paper, we propose a learning-based method named SplitNet to transform the single TSP solutions into the MinMax mTSP solutions of the same instances. Specifically, we generate single TSP solution sequences and split them into mTSP subsequences using an attention-based model trained by reinforcement learning. We also design a decision region for the splitting policy, which significantly reduces the policy action space on instances of various scales and thus improves the generalization ability of SplitNet. The experimental results show that SplitNet generalizes well and outperforms existing learning-based baselines and Google OR-Tools on widely-used random datasets of different scales and public datasets with fast solving speed.

SplitNet: A Reinforcement Learning based Sequence Splitting Method for the MinMax Multiple Travelling Salesman Problem

We present regret minimization algorithms for stochastic contextual MDPs under minimum reachability assumption, using an access to an offline least square regression oracle.
    We analyze three different settings: where the dynamics is known, where the dynamics is unknown but independent of the context and the most challenging setting where the dynamics is unknown and context-dependent. For the latter, our algorithm obtains regret bound of
    $\widetilde{O}( (H+{1}/{p_{min}})H|S|^{3/2}\sqrt{|A|T\log(\max\{|\mathcal{G}|,|\mathcal{P}|\}/\delta)})$ with probability $1-\delta$, where $\mathcal{P}$ and $\mathcal{G}$ are finite and realizable function classes used to approximate the dynamics and rewards respectively, $p_{min}$ is the minimum reachability parameter, $S$ is the set of states, $A$ the set of actions, $H$ the horizon, and $T$ the number of episodes.
    To our knowledge, our approach is the first optimistic approach applied to contextual MDPs with general function approximation (i.e., without additional knowledge regarding the function class, such as it being linear and etc.).
    We present a lower bound of $\Omega(\sqrt{T  H |S| |A| \ln(|\mathcal{G}|)/\ln(|A|)})$, on the expected regret which holds even in the case of known dynamics.
    Lastly, we discuss an extension of our results to CMDPs without minimum reachability, that obtains $\widetilde{O}(T^{3/4})$ regret.

Optimism in Face of a Context:Regret Guarantees for Stochastic Contextual MDP

Learning models that are robust to test-time distribution shifts is a key concern in domain generalization, and in the wider context of their real-life applicability. Invariant Risk Minimization (IRM) is one particular framework that aims to learn deep invariant features from multiple domains, and has subsequently led to further variants. A key assumption for the success of these methods requires that the underlying causal mechanisms/features remain invariant across domains and the true invariant features be sufficient to learn the optimal predictor. In practical problem settings, these assumptions are often not satisfied, which leads to IRM learning a sub-optimal predictor for that task. In this work, we propose the notion of partial invariance as a relaxation of the IRM framework. Under our problem setting, we first highlight the sub-optimality of the IRM solution. We then demonstrate how partitioning the training domains, assuming access to some meta-information about the domains, can help improve the performance of invariant models via partial invariance. Finally, we conduct several experiments, in linear settings as well as with classification tasks in language and images with deep models, which verify our conclusions.

Learning Optimal Features via Partial Invariance

Self-supervised anomaly detection and localization are critical to real-world scenarios in which collecting anomalous samples and pixel-wise labeling is tedious or even infeasible, even worse when a wide variety of unseen anomalies could surface at test time. Our approach involves a pretext task in the context of masked image modeling, where the goal is to impose agreement between cluster assignments obtained from the representation of an image view containing saliency-aware masked patches and the uncorrupted image view. We harness the self-attention map extracted from the transformer to mask non-salient image patches without destroying the crucial structure associated with the foreground object. Subsequently, the pre-trained model is fine-tuned to detect and localize simulated anomalies generated under the guidance of the transformer's self-attention map. We conducted extensive validation and ablations on the benchmark of industrial images and achieved superior performance against competing methods. We also show the adaptability of our method to the medical images of the chest X-rays benchmark. 

Attention-Conditioned Augmentations for Self-Supervised Anomaly Detection and Localization

In NLP annotation, it is common to have multiple annotators label the text and then obtain the ground truth labels based on major annotators’ agreement. However, annotators are individuals with different backgrounds and various voices. When annotation tasks become subjective, such as detecting politeness, offense, and social norms, annotators’ voices differ and vary. Their diverse voices may represent the true distribution of people’s opinions on subjective matters. Therefore, it is crucial to study the disagreement from annotation to understand which content is controversial from the annotators. In our research, we extract disagreement labels from five subjective datasets, then fine-tune language models to predict annotators’ disagreement. Our results show that knowing annotators’ demographic information (e.g., gender, ethnicity, education level), in addition to the task text, helps predict the disagreement. To investigate the effect of annotators’ demographics on their disagreement level, we simulate different combinations of their artificial demographics and explore the variance of the prediction to distinguish the disagreement from the inherent controversy from text content and the disagreement in the annotators’ perspective. Overall, we propose an innovative disagreement prediction mechanism for better design of the annotation process that will achieve more accurate and inclusive results for NLP systems. Our code and dataset are publicly available.

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Sequence Generation with Label Augmentation for Relation Extraction

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