Learning to Learn to Disambiguate: Meta-Learning for Few-Shot Word Sense Disambiguation

The success of deep learning methods hinges on the availability of large training datasets annotated for the task of interest. In contrast to human intelligence, these methods lack versatility and struggle to learn and adapt quickly to new tasks, where labeled data is scarce. Meta-learning aims to solve this problem by training a model on a large number of few-shot tasks, with an objective to learn new tasks quickly from a small number of examples. In this paper, we propose a meta-learning framework for few-shot word sense disambiguation (WSD), where the goal is to learn to disambiguate unseen words from only a few labeled instances. Meta-learning approaches have so far been typically tested in an $N$-way, $K$-shot classification setting where each task has $N$ classes with $K$ examples per class. Owing to its nature, WSD deviates from this controlled setup and requires the models to handle a large number of highly unbalanced classes. We extend several popular meta-learning approaches to this scenario, and analyze their strengths and weaknesses in this new challenging setting.Comment: Added additional experiment

Biases for Emergent Communication in Multi-agent Reinforcement Learning

We study the problem of emergent communication, in which language arises because speakers and listeners must communicate information in order to solve tasks. In temporally extended reinforcement learning domains, it has proved hard to learn such communication without centralized training of agents, due in part to a difficult joint exploration problem. We introduce inductive biases for positive signalling and positive listening, which ease this problem. In a simple one-step environment, we demonstrate how these biases ease the learning problem. We also apply our methods to a more extended environment, showing that agents with these inductive biases achieve better performance, and analyse the resulting communication protocols.Comment: Accepted at NeurIPS 201

Transition-based directed graph construction for emotion-cause pair extraction

Emotion-cause pair extraction aims to extract all potential pairs of emotions and corresponding causes from unannotated emotion text. Most existing methods are pipelined framework, which identifies emotions and extracts causes separately, leading to a drawback of error propagation. Towards this issue, we propose a transition-based model to transform the task into a procedure of parsing-like directed graph construction. The proposed model incrementally generates the directed graph with labeled edges based on a sequence of actions, from which we can recognize emotions with the corresponding causes simultaneously, thereby optimizing separate subtasks jointly and maximizing mutual benefits of tasks interdependently. Experimental results show that our approach achieves the best performance, outperforming the state-of-the-art methods by 6.71% (p<0.01) in F1 measure

RetouchingFFHQ: A Large-scale Dataset for Fine-grained Face Retouching Detection

The widespread use of face retouching filters on short-video platforms has raised concerns about the authenticity of digital appearances and the impact of deceptive advertising. To address these issues, there is a pressing need to develop advanced face retouching techniques. However, the lack of large-scale and fine-grained face retouching datasets has been a major obstacle to progress in this field. In this paper, we introduce RetouchingFFHQ, a large-scale and fine-grained face retouching dataset that contains over half a million conditionally-retouched images. RetouchingFFHQ stands out from previous datasets due to its large scale, high quality, fine-grainedness, and customization. By including four typical types of face retouching operations and different retouching levels, we extend the binary face retouching detection into a fine-grained, multi-retouching type, and multi-retouching level estimation problem. Additionally, we propose a Multi-granularity Attention Module (MAM) as a plugin for CNN backbones for enhanced cross-scale representation learning. Extensive experiments using different baselines as well as our proposed method on RetouchingFFHQ show decent performance on face retouching detection. With the proposed new dataset, we believe there is great potential for future work to tackle the challenging problem of real-world fine-grained face retouching detection.Comment: Under revie

Remember your roots: Biogeographic properties of plants\u27 native habitats can inform invasive plant risk assessments

Aim: Reducing the effects of invasive plants is best accomplished by predicting which species will invade and preventing their introduction. To do this, risk assessments rely on a variety of plant traits and biogeographic properties to predict potential invasiveness. However, the relative importance of these traits and properties is unknown. Determining which biogeographic properties contribute the most to predicting invasiveness could improve the accuracy and reduce the time needed to complete future risk assessments. Here, we provide a comprehensive analysis and ranking of the biogeographic properties that best differentiate invasive and noninvasive plant species. Location: Conterminous United States. Methods: We compiled county-level distributions of 10,721 vascular plant species native to the conterminous United States of which 884 were established elsewhere and 131 were invasive elsewhere. For each species, we used native distribution data to calculate biogeographic properties, including range size, human modification and abiotic niche breadth. We assessed the ability of biogeographic properties to predict whether each species was invasive outside of the United States using random forest classification models. Results: Variables that represent the breadth of a species\u27 native range, including the ranges of soil textures, ranges of soil fertility and total geographic area, are strong predictors of plant invasiveness. Models that included these variables correctly classified 86% of invasive species and 62% of noninvasive species. Variables representing resource availability and disturbance regime were not useful for distinguishing between established and invasive species. Main conclusions: Focusing on niche breadth properties could improve the accuracy of risk assessments and reduce the effort spent compiling information with lower predictive power. The importance of niche breadth in this analysis supports previous findings that broad physiological tolerance enables survival and reproduction in numerous environments, thereby increasing the likelihood of invasion

Towards Reinforcement Learning-based Aggregate Computing

Recent trends in pervasive computing promote the vision of Collective Adaptive Systems (CASs): large-scale collections of relatively simple agents that act and coordinate with no central orchestrator to support distributed applications. Engineering global behaviour out of local activity and interaction, however, is a difficult task, typically addressed by try-and-error approaches in simulation environments. In the context of Aggregate Computing (AC), a prominent functional programming approach for CASs based on field-based coordination, this difficulty is reflected in the design of versatile algorithms preserving efficiency in a variety of environments. To deal with this complexity, in this work we propose to apply Machine Learning techniques to automatically devise local actions to improve over manually-defined AC algorithms specifications. Most specifically, we adopt a Reinforcement Learning-based approach to let a collective learn local policies to improve over the standard gradient algorithm—a cornerstone brick of several higher-level self-organisation algorithms. Our evaluation shows that the learned policies can speed up the self-stabilisation of the gradient to external perturbations

ProSpect: Expanded Conditioning for the Personalization of Attribute-aware Image Generation

Personalizing generative models offers a way to guide image generation with user-provided references. Current personalization methods can invert an object or concept into the textual conditioning space and compose new natural sentences for text-to-image diffusion models. However, representing and editing specific visual attributes like material, style, layout, etc. remains a challenge, leading to a lack of disentanglement and editability. To address this, we propose a novel approach that leverages the step-by-step generation process of diffusion models, which generate images from low- to high-frequency information, providing a new perspective on representing, generating, and editing images. We develop Prompt Spectrum Space P*, an expanded textual conditioning space, and a new image representation method called ProSpect. ProSpect represents an image as a collection of inverted textual token embeddings encoded from per-stage prompts, where each prompt corresponds to a specific generation stage (i.e., a group of consecutive steps) of the diffusion model. Experimental results demonstrate that P* and ProSpect offer stronger disentanglement and controllability compared to existing methods. We apply ProSpect in various personalized attribute-aware image generation applications, such as image/text-guided material/style/layout transfer/editing, achieving previously unattainable results with a single image input without fine-tuning the diffusion models