Noname manuscript No. (will be inserted by the editor) Abstract Video Question Answering (Video QA) challenges show consistent improvements over state-of-the-art methods modelers in multiple fronts. Modeling video necessitates on well-studied benchmarks including large-scale real-world building not only spatiotemporal models for the dynamic datasets such as TGIF-QA and TVQA, demonstrating the visual channel but also multimodal structures for associated strong capabilities of our CRN unit and the HCRN for complex information channels such as subtitles or audio. To the best of our knowledge, adds at least two more layers of complexity - selecting relevant the HCRN is the very first method attempting to handle content for each channel in the context of the linguistic long and short-form multimodal Video QA at the same time. To address these modules · Hierarchy requirements, we start with two insights: (a) content selection and relation construction can be jointly encapsulated into a conditional computational structure, and (b) video-length 1 Introduction structures can be composed hierarchically. For (a) this paper introduces a general-reusable reusable neural unit dubbed Answering natural questions about a video is a powerful Conditional Relation Network (CRN) taking as input a set of demonstration of cognitive capability. The task involves acquisition tensorial objects and translating into a new set of objects that and manipulation of spatiotemporal visual, acoustic encode relations of the inputs. The generic design of CRN and linguistic representations from the video guided by helps ease the common complex model building process the compositional semantics of linguistic cues [1, 2, 3, 4, 5, of Video QA by simple block stacking and rearrangements 6]. As questions are potentially unconstrained, Video QA with flexibility in accommodating diverse input modalities requires deep modeling capacity to encode and represent crucial and conditioning features across both visual and linguistic multimodal video properties such as linguistic content, domains. As a result, we realize insight (b) by introducing object permanence, motion profiles, prolonged actions, and Hierarchical Conditional Relation Networks (HCRN) for varying-length temporal relations in a hierarchical manner. The HCRN primarily aims at exploiting intrinsic For Video QA, the visual and textual representations should properties of the visual content of a video as well as its accompanying ideally be question-specific and answer-ready.

Neural abstractive summarization systems have achieved promising progress, thanks to the availability of large-scale datasets and models pre-trained with self-supervised methods. However, ensuring the factual consistency of the generated summaries for abstractive summarization systems is a challenge. We propose a post-editing corrector module to address this issue by identifying and correcting factual errors in generated summaries. The neural corrector model is pre-trained on artificial examples that are created by applying a series of heuristic transformations on reference summaries. These transformations are inspired by an error analysis of state-of-the-art summarization model outputs. Experimental results show that our model is able to correct factual errors in summaries generated by other neural summarization models and outperforms previous models on factual consistency evaluation on the CNN/DailyMail dataset. We also find that transferring from artificial error correction to downstream settings is still very challenging.

The prior work on natural language inference (NLI) debiasing mainly targets at one or few known biases while not necessarily making the models more robust. In this paper, we focus on the model-agnostic debiasing strategies and explore how to (or is it possible to) make the NLI models robust to multiple distinct adversarial attacks while keeping or even strengthening the models' generalization power. We firstly benchmark prevailing neural NLI models including pretrained ones on various adversarial datasets. We then try to combat distinct known biases by modifying a mixture of experts (MoE) ensemble method and show that it's nontrivial to mitigate multiple NLI biases at the same time, and that model-level ensemble method outperforms MoE ensemble method. We also perform data augmentation including text swap, word substitution and paraphrase and prove its efficiency in combating various (though not all) adversarial attacks at the same time. Finally, we investigate several methods to merge heterogeneous training data (1.35M) and perform model ensembling, which are straightforward but effective to strengthen NLI models.

Healthbot, a Healthcare robot developed at The University of Auckland. In the image a user is interacting with Healthbot using a touch screen. Robots are gradually making their way into hospitals and other clinical facilities, providing basic assistance to doctors and patients. To facilitate their widespread use in health care settings, however, robotics researchers need to ensure that users feel at ease with robots and accept the help they can offer. This could potentially be achieved by developing robots that communicate in empathetic and compassionate ways.

Although the image of the tech-confused Boomer is a deeply-rooted stereotype, TechRepublic has reported that this is, in fact, a myth: In actuality, a Dropbox survey found that "people over age 55 are actually less likely than their younger colleagues to find using tech in the workplace stressful." A new report from security advisors Exabeam--2020 Cybersecurity Professionals Salary, Skills and Stress Survey--emphasizes these findings, as well. The research shows that although a whopping 88% of cybersecurity professionals embrace new technology, confident that automation will help them in their roles, it is the younger generation that is skeptical: 53% of respondents under the age of 45 "agreed or strongly agreed that AI and ML are a threat to their job security," according to the report. The findings, part of an annual survey, looked at attitudes regarding salary, training, innovation, and emerging technologies like artificial intelligence (AI) and machine learning (ML), among 350 cybersecurity professionals worldwide, hailing from the US, Germany, Singapore, Australia, and the UK. Overall, the results were positive, and the findings show that cybersecurity professionals continue to be satisfied in their jobs.

We consider the theory of regression on a manifold using reproducing kernel Hilbert space methods. Manifold models arise in a wide variety of modern machine learning problems, and our goal is to help understand the effectiveness of various implicit and explicit dimensionality-reduction methods that exploit manifold structure. Our first key contribution is to establish a novel nonasymptotic version of the Weyl law from differential geometry. From this we are able to show that certain spaces of smooth functions on a manifold are effectively finite-dimensional, with a complexity that scales according to the manifold dimension rather than any ambient data dimension. Finally, we show that given (potentially noisy) function values taken uniformly at random over a manifold, a kernel regression estimator (derived from the spectral decomposition of the manifold) yields minimax-optimal error bounds that are controlled by the effective dimension.

A key challenge of dialog systems research is to effectively and efficiently adapt to new domains. A scalable paradigm for adaptation necessitates the development of generalizable models that perform well in few-shot settings. In this paper, we focus on the intent classification problem which aims to identify user intents given utterances addressed to the dialog system. We propose two approaches for improving the generalizability of utterance classification models: (1) example-driven training and (2) observers. Example-driven training learns to classify utterances by comparing to examples, thereby using the underlying encoder as a sentence similarity model. Prior work has shown that BERT-like models tend to attribute a significant amount of attention to the [CLS] token, which we hypothesize results in diluted representations. Observers are tokens that are not attended to, and are an alternative to the [CLS] token. The proposed methods attain state-of-the-art results on three intent prediction datasets (Banking, Clinc}, and HWU) in both the full data and few-shot (10 examples per intent) settings. Furthermore, we demonstrate that the proposed approach can transfer to new intents and across datasets without any additional training.

Clustering is frequently used in the energy domain to identify dominant electricity consumption patterns of households, which can be used to construct customer archetypes for long term energy planning. Selecting a useful set of clusters however requires extensive experimentation and domain knowledge. While internal clustering validation measures are well established in the electricity domain, limited research is available for external measures. We present a method that distills expert knowledge into competency questions, which we operationalised as external evaluation measures to specify the clustering objective for our application. This approach supported a structured and formal cluster validation process that combined internal and external measures to select a cluster set that is useful for creating residential electricity customer archetypes from electricity meter data in South Africa. We validated the approach in a case study application where we successfully reconstructed customer archetypes previously developed by experts. Our approach enables transparent and repeatable cluster ranking and selection by data scientists, even if they have limited domain knowledge.

Fine-tuning deep neural networks pre-trained on large scale datasets is one of the most practical transfer learning paradigm given limited quantity of training samples. To obtain better generalization, using the starting point as the reference, either through weights or features, has been successfully applied to transfer learning as a regularizer. However, due to the domain discrepancy between the source and target tasks, there exists obvious risk of negative transfer. In this paper, we propose a novel transfer learning algorithm, introducing the idea of Target-awareness REpresentation Disentanglement (TRED), where the relevant knowledge with respect to the target task is disentangled from the original source model and used as a regularizer during fine-tuning the target model. Experiments on various real world datasets show that our method stably improves the standard fine-tuning by more than 2% in average. TRED also outperforms other state-of-the-art transfer learning regularizers such as L2-SP, AT, DELTA and BSS.

A technological revolution is occurring in the field of robotics with the data-driven deep learning technology. However, building datasets for each local robot is laborious. Meanwhile, data islands between local robots make data unable to be utilized collaboratively. To address this issue, the work presents Peer-Assisted Robotic Learning (PARL) in robotics, which is inspired by the peer-assisted learning in cognitive psychology and pedagogy. PARL implements data collaboration with the framework of cloud robotic systems. Both data and models are shared by robots to the cloud after semantic computing and training locally. The cloud converges the data and performs augmentation, integration, and transferring. Finally, fine tune this larger shared dataset in the cloud to local robots. Furthermore, we propose the DAT Network (Data Augmentation and Transferring Network) to implement the data processing in PARL. DAT Network can realize the augmentation of data from multi-local robots. We conduct experiments on a simplified self-driving task for robots (cars). DAT Network has a significant improvement in the augmentation in self-driving scenarios. Along with this, the self-driving experimental results also demonstrate that PARL is capable of improving learning effects with data collaboration of local robots.