United States

Across multiple domains from computer vision to speech recognition, machine learning models have been shown to match or outperform human experts at recognition tasks. We lack such a comparison point for Entity Linking. We construct a human benchmark on two standard datasets (TAC KBP 2010 and AIDA (YAGO)) to measure human accuracy. We find that current systems still fall short of human performance.
 
 We present DeepType 2, a novel entity linking system that closes the gap. Our proposed approach overcomes shortcomings of previous type-based entity linking systems, and does not use pre-trained language models to reach this level. Three key innovations are responsible for DeepType 2&#39;s performance: 1) an abstracted representation of entities that favors shared learning and greater sample efficiency, 2) autoregressive entity features indicating type interactions (e.g. list type homogeneity, shared employers, geographical co-occurrence) with previous predictions that enable globally coherent document-wide predictions, 3) the entire model is trained end to end using a single entity-level maximum likelihood objective function. This is made possible by associating a context-specific score to each of the entity&#39;s abstract representation&#39;s sub-components (types), and summing these scores to form a candidate entity logit. In this paper, we explain how this factorization focuses the learning on the salient types of the candidate entities. Furthermore, we show how the scores can serve as a rationale for predictions.
 
  The key contributions of this work are twofold: 1) we create the first human performance benchmark on standard benchmarks in entity linking (TAC KBP 2010 and AIDA (YAGO)) which will be made publicly available to support further analyses, 2) we obtain a new state of the art on these datasets and are the first to outperform humans on our benchmark. We perform model ablations to measure the contribution of the different facets of our system. We also include an analysis of human and algorithmic errors to provide insights into the causes, notably originating from journalistic style and historical context.

AAAI 2022

DeepType 2: Superhuman Entity Linking, All You Need Is Type Interactions

[![](https://assets.underline.io/uploads/markdown_image/1/image/81a3c0317d24f663b49b996875024d45.png)](https://aclanthology.org/volumes/2022.coling-1/)



### THE CONFERENCE WE KNOW AND WE WANT.
**COLING**, the International Conference on Computational Linguistics, is one of the premier conferences for the natural language processing and computational linguistics.

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 including tech-startups. COLING provides opportunities for all these communities to showcase their exciting discovery.

In fall of 2022, COLING will be held in Gyeongju in a hybrid format. All participants can either present at the venue site or join virtually. As more people get vaccinated, we are happy to provide safer environments for our colleagues. We believe that COLING 2022 will be one of the conferences, free from the pandemic. The hybrid format gives presenters and sponsors a valuable opportunity to promote their companies in both an online and in-person venue. For the first time in a long time, customers can interact with their sponsor's products first-hand. The online venue, too, gives sponsors the chance to network with those unable to attend the in-person session.

The hybrid format ultimately lends itself to greater exposure for our sponsors: COLING2022 will let you reach more potential partners and customers than ever before!<br><br><br>

To gain access to this event page you are required to register.<br> Find more information and pay the registration fee on event the organizer’s website **[https://coling2022.org/reg](https://coling2022.org/reg)**

COLING 2022

COLING, the International Conference on Computational Linguistics, is one of the premier conferences for the natural language processing and computational linguistics.

Across multiple domains from computer vision to speech recognition, machine learning models have been shown to match or outperform human experts at recognition tasks. We lack such a comparison point for Entity Linking. We construct a human benchmark on two standard datasets (TAC KBP 2010 and AIDA (YAGO)) to measure human accuracy. We find that current systems still fall short of human performance.
 
 We present DeepType 2, a novel entity linking system that closes the gap. Our proposed approach overcomes shortcomings of previous type-based entity linking systems, and does not use pre-trained language models to reach this level. Three key innovations are responsible for DeepType 2's performance: 1) an abstracted representation of entities that favors shared learning and greater sample efficiency, 2) autoregressive entity features indicating type interactions (e.g. list type homogeneity, shared employers, geographical co-occurrence) with previous predictions that enable globally coherent document-wide predictions, 3) the entire model is trained end to end using a single entity-level maximum likelihood objective function. This is made possible by associating a context-specific score to each of the entity's abstract representation's sub-components (types), and summing these scores to form a candidate entity logit. In this paper, we explain how this factorization focuses the learning on the salient types of the candidate entities. Furthermore, we show how the scores can serve as a rationale for predictions.
 
  The key contributions of this work are twofold: 1) we create the first human performance benchmark on standard benchmarks in entity linking (TAC KBP 2010 and AIDA (YAGO)) which will be made publicly available to support further analyses, 2) we obtain a new state of the art on these datasets and are the first to outperform humans on our benchmark. We perform model ablations to measure the contribution of the different facets of our system. We also include an analysis of human and algorithmic errors to provide insights into the causes, notably originating from journalistic style and historical context.

technical paper

The purpose of the AAAI conference is to promote research in artificial intelligence (AI) and scientific exchange among AI researchers, practitioners, scientists, and engineers in affiliated disciplines. 

We address the problem of facial motion retargeting that aims to transfer facial motion from a 2D face image to 3D characters. Existing methods often formulate this problem as a 3D face reconstruction problem, which estimates the face attributes such as face identity and expression from face images. However, due to the lack of ground-truth labels for both identity and expression, most 3D-face reconstruction-based methods fail to capture the facial identity and expression accurately. As a result, these methods may not achieve promising performance. To address this, we propose an identity-consistent constraint to learn accurate identities by encouraging consistent identity prediction across multiple frames. Based on a more accurate identity, we are able to obtain a more accurate facial expression. Moreover, we further propose an expression-exclusive constraint to improve performance by avoiding the co-occurrence of contradictory expression units (e.g., ``brow lower'' vs. ``brow raise''). Extensive experiments on facial motion retargeting and 3D face reconstruction tasks demonstrate the superiority of the proposed method over existing methods. Our code and  supplementary materials are available at https://github.com/deepmo24/CPEM.

Towards Accurate Facial Motion Retargeting with Identity-Consistent and Expression-Exclusive Constraints

In the context of social choice theory, we develop a tableau-based calculus for reasoning about voting rules. This calculus can be used to obtain structured explanations for why a given set of axioms justifies a given election outcome for a given profile of voter preferences. We then show how to operationalise this calculus, using a combination of SAT solving and answer set programming, to arrive at a flexible framework for presenting human-readable justifications to users.

A Calculus for Computing Structured Justifications for Election Outcomes

Unsupervised domain adaptation has recently emerged as an effective paradigm for generalizing deep neural networks to new target domains. However, there is still enormous potential to be tapped to reach the fully supervised performance. In this paper, we present a novel active learning strategy to assist knowledge transfer in the target domain, dubbed active domain adaptation. We start from an observation that energy-based models exhibit free energy biases when training (source) and test (target) data come from different distributions. Inspired by this inherent mechanism, we empirically reveal that a simple yet efficient energy-based sampling strategy sheds light on selecting the most valuable target samples than existing approaches requiring particular architectures or computation of the distances. Our algorithm, Energy-based Active Domain Adaptation (EADA), queries groups of target data that incorporate both domain characteristic and instance uncertainty into every selection round. Meanwhile, by aligning the free energy of target data compact around the source domain via a regularization term, domain gap can be implicitly diminished. Through extensive experiments, we show that EADA surpasses state-of-the-art methods on well-known challenging benchmarks with substantial improvements, making it a useful option in the open world. Code is available at https://github.com/BIT-DA/EADA.

Active Learning for Domain Adaptation: An Energy-Based Approach

In point cloud generation and completion, previous methods for transforming latent features to point clouds are generally based on fully connected layers (FC-based) or folding operations (Folding-based). However, point clouds generated by FC-based methods are usually troubled by outliers and rough surfaces. For folding-based methods, their data flow is large, convergence speed is slow, and they are also hard to handle the generation of non-smooth surfaces. In this work, we propose AXform, an attention-based method to transform latent features to point clouds. AXform first generates points in an interim space, using a fully connected layer. These interim points are then aggregated to generate the target point cloud. AXform takes both parameter sharing and data flow into account, which makes it has fewer outliers, fewer network parameters, and a faster convergence speed. The points generated by AXform do not have the strong 2-manifold constraint, which improves the generation of non-smooth surfaces. When AXform is expanded to multiple branches for local generations, the centripetal constraint makes it has properties of self-clustering and space consistency, which further enables unsupervised semantic segmentation. We also adopt this scheme and design AXformNet for point cloud completion. Considerable experiments on different datasets show that our methods achieve state-of-the-art results.

Attention-Based Transformation from Latent Features to Point Clouds

We study the problem of predicting an event sequence given some meta data. In particular, we are interested in learning easily interpretable models that can accurately generate a sequence based on an attribute vector. To this end, we propose to learn a sparse event-flow graph over the training sequences, and statistically robust rules that use meta data to determine which paths to follow. We formalize the problem in terms of the Minimum Description Length (MDL) principle, by which we identify the best model as the one that compresses the data best. As the resulting optimization problem is NP-hard, we propose the efficient ConSequence algorithm to discover good event-flow graphs from data. 
 Through an extensive set of experiments including a case study, we show that it ably discovers compact, interpretable and accurate models for the generation and prediction of event sequences from data, has a low sample complexity, and is particularly robust against noise.

Discovering Interpretable Data-to-Sequence Generators

Weighted First-Order Model Counting (WFOMC) computes the weighted sum of the models of a first-order logic theory on a given finite domain. First-Order Logic theories that admit polynomial-time WFOMC w.r.t domain cardinality are called domain liftable. We introduce the concept of lifted interpretations as a tool for formulating closed forms for WFOMC. Using lifted interpretations, we reconstruct the closed-form formula for polynomial-time FOMC in the universally quantified fragment of FO2, earlier proposed by Beame et al. We then expand this closed-form to incorporate cardinality constraints, existential quantifiers, and counting quantifiers (a.k.a C2) without losing domain-liftability. Finally, we show that the obtained closed-form motivates a natural definition of a family of weight functions strictly larger than symmetric weight functions.

Weighted Model Counting in FO2 with Cardinality Constraints and Counting Quantifiers: A Closed Form Formula

Following up on purely theoretical work, we contribute further theoretical insights into adapting stable two-sided matchings to change. Moreover, we perform extensive empirical studies hinting at numerous practically useful properties. Our theoretical extensions include the study of new problems (that is, incremental variants of Almost Stable Marriage and Hospital Residents), focusing on their (parameterized) computational complexity and the equivalence of various change types (thus simplifying algorithmic and complexity-theoretic studies for various natural change scenarios). Our experimental findings reveal, for instance, that allowing the new matching to be blocked by a few pairs significantly decreases the difference between the old and the new matching.

Theory of and Experiments on Minimally Invasive Stability Preservation in Changing Two-Sided Matching Markets

In sequential recommender system applications, it is important to develop models that can capture users' evolving interest over time to successfully recommend future items that they are likely to interact with. For users with long histories, typical models based on recurrent neural networks tend to forget important items in the distant past. Recent works have shown that storing a small sketch of past items can improve sequential recommendation tasks. However, these works all rely on static sketching policies, i.e., heuristics to select items to keep in the sketch, which are not necessarily optimal and cannot improve over time with more training data. In this paper, we propose a differentiable policy for sketching (DiPS), a framework that learns a data-driven sketching policy in an end-to-end manner together with the recommender system model to explicitly maximize recommendation quality in the future. 
 We also propose an approximate estimator of the gradient for optimizing the sketching algorithm parameters that is computationally efficient. We verify the effectiveness of DiPS on real-world datasets under various practical settings and show that it requires up to 50% fewer sketch items to reach the same predictive quality than existing sketching policies.

DiPS: Differentiable Policy for Sketching in Recommender Systems

Event cameras asynchronously output the polarity values of pixel-level log intensity alterations. They are robust against motion blur and can be adopted in challenging light conditions. Owing to these advantages, event cameras have been employed in various vision tasks such as depth estimation, visual odometry, and object detection. In particular, event cameras are effective in stereo depth estimation to ï¬nd correspondence points between two cameras under challenging illumination conditions and/or fast motion. However, because event cameras provide spatially sparse event stream data, it is difï¬cult to obtain a dense disparity map. Although it is possible to estimate disparity from event data at the edge of a structure where intensity changes are likely to occur, estimating the disparity in a region where event occurs rarely is challenging. In this study, we propose a deep network that combines the features of an image with the features of an event to generate a dense disparity map. The proposed network uses images to obtain spatially dense features that are lacking in events. In addition, we propose a spatial multi-scale correlation between two fused feature maps for an accurate disparity map. To validate our method, we conducted experiments using synthetic and real-world datasets.

Event-Image Fusion Stereo Using Cross-Modality Feature Propagation

Learning to understand grounded language, which connects natural language to percepts, is a critical research area. Prior work in grounded language acquisition has focused primarily on textual inputs. In this work, we demonstrate the feasibility of performing grounded language acquisition on paired visual percepts and raw speech inputs. This will allow human-robot interactions in which language about novel tasks and environments is learned from end-users, reducing dependence on textual inputs and potentially mitigating the effects of demographic bias found in widely available speech recognition systems. We leverage recent work in self-supervised speech representation models and show that learned representations of speech can make language grounding systems more inclusive towards specific groups while maintaining or even increasing general performance.

Downloads

Next from AAAI 2022

Towards Accurate Facial Motion Retargeting with Identity-Consistent and Expression-Exclusive Constraints

Similar lecture

Improving Zero-Shot Entity Linking Candidate Generation with Ultra-Fine Entity Type Information

.css-70qvj9{display:-webkit-box;display:-webkit-flex;display:-ms-flexbox;display:flex;-webkit-align-items:center;-webkit-box-align:center;-ms-flex-align:center;align-items:center;}Downloads

Next from AAAI 2022

Towards Accurate Facial Motion Retargeting with Identity-Consistent and Expression-Exclusive Constraints

Similar lecture

Improving Zero-Shot Entity Linking Candidate Generation with Ultra-Fine Entity Type Information

Downloads