More Like this

1. Seed-Guided Fine-Grained Entity Typing in Science and Engineering Domains

   https://doi.org/10.1609/AAAI.V38I17.29933  

   Zhang, Yu; Zhang, Yunyi; Shen, Yanzhen; Deng, Yu; Popa, Lucian; Shwartz, Larisa; Zhai, ChengXiang; Han, Jiawei (March 2024, Proceedings of the AAAI Conference on Artificial Intelligence)

   Wooldridge, Michael J; Dy, Jennifer G; Natarajan, Sriraam (Ed.)

   Accurately typing entity mentions from text segments is a fundamental task for various natural language processing applications. Many previous approaches rely on massive human-annotated data to perform entity typing. Nevertheless, collecting such data in highly specialized science and engineering domains (e.g., software engineering and security) can be time-consuming and costly, without mentioning the domain gaps between training and inference data if the model needs to be applied to confidential datasets. In this paper, we study the task of seed-guided fine-grained entity typing in science and engineering domains, which takes the name and a few seed entities for each entity type as the only supervision and aims to classify new entity mentions into both seen and unseen types (i.e., those without seed entities). To solve this problem, we propose SEType which first enriches the weak supervision by finding more entities for each seen type from an unlabeled corpus using the contextualized representations of pre-trained language models. It then matches the enriched entities to unlabeled text to get pseudo-labeled samples and trains a textual entailment model that can make inferences for both seen and unseen types. Extensive experiments on two datasets covering four domains demonstrate the effectiveness of SEType in comparison with various baselines. Code and data are available at: https://github.com/yuzhimanhua/SEType. 

   more » « less
2. Clustering-based Inference for Zero-Shot Biomedical Entity Linking

   Angell, Rico; Monath, Nicholas; Mohan, Sunil; Yadav, Nishant; McCallum, Andrew (January 2021, Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies)

   null (Ed.)

   Due to large number of entities in biomedical knowledge bases, only a small fraction of entities have corresponding labelled training data. This necessitates entity linking models which are able to link mentions of unseen entities using learned representations of entities. Previous approaches link each mention independently, ignoring the relationships within and across documents between the entity mentions. These relations can be very useful for linking mentions in biomedical text where linking decisions are often difficult due mentions having a generic or a highly specialized form. In this paper, we introduce a model in which linking decisions can be made not merely by linking to a knowledge base entity but also by grouping multiple mentions together via clustering and jointly making linking predictions. In experiments on the largest publicly available biomedical dataset, we improve the best independent prediction for entity linking by 3.0 points of accuracy, and our clustering-based inference model further improves entity linking by 2.3 points. 

   more » « less
3. Cross-Modal Prediction of Superclasses Using Cortex-Inspired Neural Architecture

   https://doi.org/10.1109/BioSMART58455.2023.10162118  

   Kursun, Olcay; Nguyen, Hoa T.; Favorov, Oleg V. (June 2023, 2023 5th International Conference on Bio-engineering for Smart Technologies (BioSMART))

   The concept of stimulus feature tuning isfundamental to neuroscience. Cortical neurons acquire their feature-tuning properties by learning from experience and using proxy signs of tentative features’ potential usefulness that come from the spatial and/or temporal context in which these features occur. According to this idea, local but ultimately behaviorally useful features should be the ones that are predictably related to other such features either preceding them in time or taking place side-by-side with them. Inspired by this idea, in this paper, deep neural networks are combined with Canonical Correlation Analysis (CCA) for feature extraction and the power of the features is demonstrated using unsupervised cross-modal prediction tasks. CCA is a multi-view feature extraction method that finds correlated features across multiple datasets (usually referred to as views or modalities). CCA finds linear transformations of each view such that the extracted principal components, or features, have a maximal mutual correlation. CCA is a linear method, and the features are computed by a weighted sum of each view's variables. Once the weights are learned, CCA can be applied to new examples and used for cross-modal prediction by inferring the target-view features of an example from its given variables in a source (query) view. To test the proposed method, it was applied to the unstructured CIFAR-100 dataset of 60,000 images categorized into 100 classes, which are further grouped into 20 superclasses and used to demonstrate the mining of image-tag correlations. CCA was performed on the outputs of three pre-trained CNNs: AlexNet, ResNet, and VGG. Taking advantage of the mutually correlated features extracted with CCA, a search for nearest neighbors was performed in the canonical subspace common to both the query and the target views to retrieve the most matching examples in the target view, which successfully predicted the superclass membership of the tested views without any supervised training. 

   more » « less
4. Event and Entity Coreference using Trees to Encode Uncertainty in Joint Decisions

   https://doi.org/10.18653/v1/2021.crac-1.11  

   Yadav, Nishant; Monath, Nicholas; Angell, Rico; McCallum, Andrew (November 2021, Proceedings of the 4th Workshop on Computational Models of Reference, Anaphora and Coreference (CRAC 2021))

   Coreference decisions among event mentions and among co-occurring entity mentions are highly interdependent, thus motivating joint inference. Capturing the uncertainty over each variable can be crucial for inference among multiple dependent variables. Previous work on joint coreference employs heuristic approaches, lacking well-defined objectives, and lacking modeling of uncertainty on each side of the joint problem. We present a new approach of joint coreference, including (1) a formal cost function inspired by Dasgupta’s cost for hierarchical clustering, and (2) a representation for uncertainty of clustering of event and entity mentions, again based on a hierarchical structure. We describe an alternating optimization method for inference that when clustering event mentions, considers the uncertainty of the clustering of entity mentions and vice-versa. We show that our proposed joint model provides empirical advantages over state-of-the-art independent and joint models. 

   more » « less
5. Residual Policy Learning for Shared Autonomy

   Schaff, Charles; Walter, Matthew R. (July 2020, Proceedings of Robotics: Science and Systems (RSS))

   Shared autonomy provides an effective framework for human-robot collaboration that takes advantage of the complementary strengths of humans and robots to achieve common goals. Many existing approaches to shared autonomy make restrictive assumptions that the goal space, environment dynamics, or human policy are known a priori, or are limited to discrete action spaces, preventing those methods from scaling to complicated real world environments. We propose a model-free, residual policy learning algorithm for shared autonomy that alleviates the need for these assumptions. Our agents are trained to minimally adjust the human’s actions such that a set of goal-agnostic constraints are satisfied. We test our method in two continuous control environments: Lunar Lander, a 2D flight control domain, and a 6-DOF quadrotor reaching task. In experiments with human and surrogate pilots, our method significantly improves task performance without any knowledge of the human’s goal beyond the constraints. These results highlight the ability of model-free deep reinforcement learning to realize assistive agents suited to continuous control settings with little knowledge of user intent. 

   more » « less