Publications

Investigating the Performance of Transformer-Based NLI Models on Presuppositional Inferences

Jad Kabbara

Jackie Cheung

Presuppositions are assumptions that are taken for granted by an utterance, and identifying them is key to a pragmatic interpretation of lan… (voir plus)guage. In this paper, we investigate the capabilities of transformer models to perform NLI on cases involving presupposition. First, we present simple heuristics to create alternative “contrastive” test cases based on the ImpPres dataset and investigate the model performance on those test cases. Second, to better understand how the model is making its predictions, we analyze samples from sub-datasets of ImpPres and examine model performance on them. Overall, our findings suggest that NLI-trained transformer models seem to be exploiting specific structural and lexical cues as opposed to performing some kind of pragmatic reasoning.

2022-01-01

COLING (publié)

dblp.uni-trier.de

KNIFE: Kernelized-Neural Differential Entropy Estimation

Georg Pichler

Pierre Colombo

Malik Boudiaf

Gunther Koliander

Pablo Piantanida

Mutual Information (MI) has been widely used as a loss regularizer for training neural networks. This has been particularly effective when l… (voir plus)earn dis-entangled or compressed representations of high dimensional data. However, differential entropy (DE), another fundamental measure of information, has not found widespread use in neural network training. Although DE offers a potentially wider range of applications than MI, off-the-shelf DE estimators are either non differentiable, computationally intractable or fail to adapt to changes in the underlying distribution. These drawbacks prevent them from being used as regularizers in neural networks training. To address shortcomings in previously proposed estimators for DE, here we introduce K NIFE , a fully parameterized, differentiable kernel-based estimator of DE. The ﬂexibility of our approach also allows us to construct K NIFE -based estimators for conditional (on either discrete or continuous variables) DE, as well as MI. We empirically validate our method on high-dimensional synthetic data and further apply it to guide the training of neural networks for real-world tasks. Our experiments on a large variety of tasks, including visual domain adaptation, textual fair classiﬁcation, and textual ﬁne-tuning demonstrate the effectiveness of K NIFE - based estimation. Code can be found at https: //github.com/g-pichler/knife .

2022-01-01

arXiv.org (prépublication)

dblp.uni-trier.de

Lazy vs hasty: linearization in deep networks impacts learning schedule based on example difficulty

Thomas George

Guillaume Lajoie

Aristide Baratin

Among attempts at giving a theoretical account of the success of deep neural networks, a recent line of work has identified a so-called `laz… (voir plus)y' training regime in which the network can be well approximated by its linearization around initialization. Here we investigate the comparative effect of the lazy (linear) and feature learning (non-linear) regimes on subgroups of examples based on their difficulty. Specifically, we show that easier examples are given more weight in feature learning mode, resulting in faster training compared to more difficult ones. In other words, the non-linear dynamics tends to sequentialize the learning of examples of increasing difficulty. We illustrate this phenomenon across different ways to quantify example difficulty, including c-score, label noise, and in the presence of easy-to-learn spurious correlations. Our results reveal a new understanding of how deep networks prioritize resources across example difficulty.

2022-01-01

Trans. Mach. Learn. Res. (publié)

On Learning Fairness and Accuracy on Multiple Subgroups

Changjian Shui

Gezheng Xu

Qi CHEN

Jiaqi Li

Charles Ling

Tal Arbel

Boyu Wang

Christian Gagné

We propose an analysis in fair learning that preserves the utility of the data while reducing prediction disparities under the criteria of g… (voir plus)roup sufficiency. We focus on the scenario where the data contains multiple or even many subgroups, each with limited number of samples. As a result, we present a principled method for learning a fair predictor for all subgroups via formulating it as a bilevel objective. Specifically, the subgroup specific predictors are learned in the lower-level through a small amount of data and the fair predictor. In the upper-level, the fair predictor is updated to be close to all subgroup specific predictors. We further prove that such a bilevel objective can effectively control the group sufficiency and generalization error. We evaluate the proposed framework on real-world datasets. Empirical evidence suggests the consistently improved fair predictions, as well as the comparable accuracy to the baselines.

Learning Inter-Modal Correspondence and Phenotypes From Multi-Modal Electronic Health Records

Kejing Yin

William K. Cheung

Benjamin Fung

Jonathan Poon

Non-negative tensor factorization has been shown a practical solution to automatically discover phenotypes from the electronic health record… (voir plus)s (EHR) with minimal human supervision. Such methods generally require an input tensor describing the inter-modal interactions to be pre-established; however, the correspondence between different modalities (e.g., correspondence between medications and diagnoses) can often be missing in practice. Although heuristic methods can be applied to estimate them, they inevitably introduce errors, and leads to sub-optimal phenotype quality. This is particularly important for patients with complex health conditions (e.g., in critical care) as multiple diagnoses and medications are simultaneously present in the records. To alleviate this problem and discover phenotypes from EHR with unobserved inter-modal correspondence, we propose the collective hidden interaction tensor factorization (cHITF) to infer the correspondence between multiple modalities jointly with the phenotype discovery. We assume that the observed matrix for each modality is marginalization of the unobserved inter-modal correspondence, which are reconstructed by maximizing the likelihood of the observed matrices. Extensive experiments conducted on the real-world MIMIC-III dataset demonstrate that cHITF effectively infers clinically meaningful inter-modal correspondence, discovers phenotypes that are more clinically relevant and diverse, and achieves better predictive performance compared with a number of state-of-the-art computational phenotyping models.

2022-01-01

IEEE Transactions on Knowledge and Data Engineering (publié)

A Learning Metaheuristic Algorithm for a Scheduling Application

Nazgol Niroumandrad

Nadia Lahrichi

Andrea Lodi

2022-01-01

Modeling, Identification and Control (publié)

Learning Representations for New Sound Classes With Continual Self-Supervised Learning

Zhepei Wang

Cem (Yusuf) Subakan

Xilin Jiang

Junkai Wu

Efthymios Tzinis

Mirco Ravanelli

Paris Smaragdis

In this article, we work on a sound recognition system that continually incorporates new sound classes. Our main goal is to develop a framew… (voir plus)ork where the model can be updated without relying on labeled data. For this purpose, we propose adopting representation learning, where an encoder is trained using unlabeled data. This learning framework enables the study and implementation of a practically relevant use case where only a small amount of the labels is available in a continual learning context. We also make the empirical observation that a similarity-based representation learning method within this framework is robust to forgetting even if no explicit mechanism against forgetting is employed. We show that this approach obtains similar performance compared to several distillation-based continual learning methods when employed on self-supervised representation learning methods.

2022-01-01

IEEE Signal Processing Letters (publié)

Learning What You Need from What You Did: Product Taxonomy Expansion with User Behaviors Supervision

Sijie Cheng

Zhouhong Gu

Bang Liu

Rui Xie

Wei Wu

Yanghua Xiao

Taxonomies have been widely used in various domains to underpin numerous applications. Specially, product taxonomies serve an essential role… (voir plus) in the e-commerce domain for the recommendation, browsing, and query understanding. However, taxonomies need to constantly capture the newly emerged terms or concepts in e-commerce platforms to keep up-to-date, which is expensive and labor-intensive if it relies on manual maintenance and updates. Therefore, we target the taxonomy expansion task to attach new concepts to existing taxonomies automatically. In this paper, we present a self-supervised and user behavior-oriented product taxonomy expansion framework to append new concepts into existing taxonomies. Our framework extracts hyponymy relations that conform to users' intentions and cognition. Specifically, i) to fully exploit user behavioral information, we extract candidate hyponymy relations that match user interests from query-click concepts; ii) to enhance the semantic information of new concepts and better detect hyponymy relations, we model concepts and relations through both user-generated content and structural information in existing taxonomies and user click logs, by leveraging Pre-trained Language Models and Graph Neural Network combined with Contrastive Learning; iii) to reduce the cost of dataset construction and overcome data skews, we construct a high-quality and balanced training dataset from existing taxonomy with no supervision. Extensive experiments on real-world product taxonomies in Meituan Platform, a leading Chinese vertical e-commerce platform to order take-out with more than 70 million daily active users, demonstrate the superiority of our proposed framework over state-of-the-art methods. Notably, our method enlarges the size of real-world product taxonomies from 39,263 to 94,698 relations with 88% precision. Our implementation is available: https://github.com/AdaCheng/Product_Taxonomy_Expansion.

2022-01-01

ICDE (publié)

Learning with Rejection for Abstractive Text Summarization

Meng Cao

Yue Dong

Jingyi He

Jackie Cheung

2022-01-01

EMNLP (publié)

Long Range Graph Benchmark

Vijay Prakash Dwivedi

Ladislav Rampášek

Mikhail Galkin

Ali Parviz

Guy Wolf

Anh Tuan Luu

Dominique Beaini

Graph Neural Networks (GNNs) that are based on the message passing (MP) paradigm generally exchange information between 1-hop neighbors to b… (voir plus)uild node representations at each layer. In principle, such networks are not able to capture long-range interactions (LRI) that may be desired or necessary for learning a given task on graphs. Recently, there has been an increasing interest in development of Transformer-based methods for graphs that can consider full node connectivity beyond the original sparse structure, thus enabling the modeling of LRI. However, MP-GNNs that simply rely on 1-hop message passing often fare better in several existing graph benchmarks when combined with positional feature representations, among other innovations, hence limiting the perceived utility and ranking of Transformer-like architectures. Here, we present the Long Range Graph Benchmark (LRGB) with 5 graph learning datasets: PascalVOC-SP, COCO-SP, PCQM-Contact, Peptides-func and Peptides-struct that arguably require LRI reasoning to achieve strong performance in a given task. We benchmark both baseline GNNs and Graph Transformer networks to verify that the models which capture long-range dependencies perform significantly better on these tasks. Therefore, these datasets are suitable for benchmarking and exploration of MP-GNNs and Graph Transformer architectures that are intended to capture LRI.

MCVD: Masked Conditional Video Diffusion for Prediction, Generation, and Interpolation

Vikram Voleti

Alexia Jolicoeur-Martineau

Chris Pal

Video prediction is a challenging task. The quality of video frames from current state-of-the-art (SOTA) generative models tends to be poor … (voir plus)and generalization beyond the training data is difficult. Furthermore, existing prediction frameworks are typically not capable of simultaneously handling other video-related tasks such as unconditional generation or interpolation. In this work, we devise a general-purpose framework called Masked Conditional Video Diffusion (MCVD) for all of these video synthesis tasks using a probabilistic conditional score-based denoising diffusion model, conditioned on past and/or future frames. We train the model in a manner where we randomly and independently mask all the past frames or all the future frames. This novel but straightforward setup allows us to train a single model that is capable of executing a broad range of video tasks, specifically: future/past prediction -- when only future/past frames are masked; unconditional generation -- when both past and future frames are masked; and interpolation -- when neither past nor future frames are masked. Our experiments show that this approach can generate high-quality frames for diverse types of videos. Our MCVD models are built from simple non-recurrent 2D-convolutional architectures, conditioning on blocks of frames and generating blocks of frames. We generate videos of arbitrary lengths autoregressively in a block-wise manner. Our approach yields SOTA results across standard video prediction and interpolation benchmarks, with computation times for training models measured in 1-12 days using