Reviewerly/exHarmony huggingface dataset:
Logical Reasoning with Span-Level Predictions for Interpretable and Robust NLI Models

Current Natural Language Inference (NLI) models achieve impressive results, sometimes outperforming humans when evaluating on indistribution test sets.However, as these models are known to learn from annotation artefacts and dataset biases, it is unclear to what extent the models are learning the task of NLI instead of learning from shallow heuristics in their training data.We address this issue by introducing a logical reasoning framework for NLI, creating highly transparent model decisions that are based on logical rules.Unlike prior work, we show that improved interpretability can be achieved without decreasing the predictive accuracy.We almost fully retain performance on SNLI, while also identifying the exact hypothesis spans that are responsible for each model prediction.Using the e-SNLI human explanations, we verify that our model makes sensible decisions at a span level, despite not using any span labels during training.We can further improve model performance and span-level decisions by using the e-SNLI explanations during training.Finally, our model is more robust in a reduced data setting.When training with only 1,000 examples, out-of-distribution performance improves on the MNLI matched and mismatched validation sets by 13% and 16% relative to the baseline.Training with fewer observations yields further improvements, both in-distribution and out-ofdistribution.
Empowering Dual-Encoder with Query Generator for Cross-Lingual Dense Retrieval

In monolingual dense retrieval, lots of works focus on how to distill knowledge from crossencoder re-ranker to dual-encoder retriever and these methods achieve better performance due to the effectiveness of cross-encoder re-ranker.However, we find that the performance of the cross-encoder re-ranker is heavily influenced by the number of training samples and the quality of negative samples, which is hard to obtain in the cross-lingual setting.In this paper, we propose to use a query generator as the teacher in the cross-lingual setting, which is less dependent on enough training samples and highquality negative samples.In addition to traditional knowledge distillation, we further propose a novel enhancement method, which uses the query generator to help the dual-encoder align queries from different languages, but does not need any additional parallel sentences.The experimental results show that our method outperforms the state-of-the-art methods on two benchmark datasets.
Data-Efficient Strategies for Expanding Hate Speech Detection into Under-Resourced Languages

Hate speech is a global phenomenon, but most hate speech datasets so far focus on Englishlanguage content.This hinders the development of more effective hate speech detection models in hundreds of languages spoken by billions across the world.More data is needed, but annotating hateful content is expensive, timeconsuming and potentially harmful to annotators.To mitigate these issues, we explore dataefficient strategies for expanding hate speech detection into under-resourced languages.In a series of experiments with mono-and multilingual models across five non-English languages, we find that 1) a small amount of target-language fine-tuning data is needed to achieve strong performance, 2) the benefits of using more such data decrease exponentially, and 3) initial fine-tuning on readily-available English data can partially substitute targetlanguage data and improve model generalisability.Based on these findings, we formulate actionable recommendations for hate speech detection in low-resource language settings.
Event Causality Identification with Causal News Corpus - Shared Task 3, CASE 2022

The Event Causality Identification Shared Task of CASE 2022 involved two subtasks working on the Causal News Corpus.Subtask 1 required participants to predict if a sentence contains a causal relation or not.This is a supervised binary classification task.Subtask 2 required participants to identify the Cause, Effect and Signal spans per causal sentence.This could be seen as a supervised sequence labeling task.For both subtasks, participants uploaded their predictions for a held-out test set, and ranking was done based on binary F1 and macro F1 scores for Subtask 1 and 2, respectively.This paper summarizes the work of the 17 teams that submitted their results to our competition and 12 system description papers that were received.The best F1 scores achieved for Subtask 1 and 2 were 86.19% and 54.15%, respectively.All the top-performing approaches involved pretrained language models fine-tuned to the targeted task.We further discuss these approaches and analyze errors across participants' systems in this paper.
What Do Compressed Multilingual Machine Translation Models Forget?

Recently, very large pre-trained models achieve state-of-the-art results in various natural language processing (NLP) tasks, but their size makes it more challenging to apply them in resource-constrained environments.Compression techniques allow to drastically reduce the size of the models and therefore their inference time with negligible impact on top-tier metrics.However, the general performance averaged across multiple tasks and/or languages may hide a drastic performance drop on underrepresented features, which could result in the amplification of biases encoded by the models.In this work, we assess the impact of compression methods on Multilingual Neural Machine Translation models (MNMT) for various language groups, gender, and semantic biases by extensive analysis of compressed models on different machine translation benchmarks, i.e.FLORES-101, MT-Gender, and DiBiMT.We show that the performance of under-represented languages drops significantly, while the average BLEU metric only slightly decreases.Interestingly, the removal of noisy memorization with compression leads to a significant improvement for some medium-resource languages.Finally, we demonstrate that compression amplifies intrinsic gender and semantic biases, even in high-resource languages. 1* Work done during an internship at NAVER LABS Europe. 1 We release our implementation at https: //github.com/alirezamshi/bias-compressedMT. 10 We choose nine language pairs covering all pairwise combinations of "low", "medium", and "high" language categories.A list of this subset is provided in Appendix C. 11 We did not report actual inference time as implementation of compression techniques is highly dependent on the device. 12The complete spBLEU scores of FLORES-101 language pairs for all models are provided in Appendix D.
CrossRE: A Cross-Domain Dataset for Relation Extraction

Relation Extraction (RE) has attracted increasing attention, but current RE evaluation is limited to in-domain evaluation setups.Little is known on how well a RE system fares in challenging, but realistic out-of-distribution evaluation setups.To address this gap, we propose CROSSRE, a new, freely-available crossdomain benchmark for RE, which comprises six distinct text domains and includes multilabel annotations.An additional innovation is that we release meta-data collected during annotation, to include explanations and flags of difficult instances.We provide an empirical evaluation with a state-of-the-art model for relation classification.As the meta-data enables us to shed new light on the state-of-the-art model, we provide a comprehensive analysis on the impact of difficult cases and find correlations between model and human annotations.Overall, our empirical investigation highlights the difficulty of cross-domain RE.We release our dataset, to spur more research in this direction.
Federated Continual Learning for Text Classification via Selective Inter-client Transfer

In this work, we combine the two paradigms: Federated Learning (FL) and Continual Learning (CL) for text classification task in cloudedge continuum.The objective of Federated Continual Learning (FCL) is to improve deep learning models over life time at each client by (relevant and efficient) knowledge transfer without sharing data.Here, we address challenges in minimizing inter-client interference while knowledge sharing due to heterogeneous tasks across clients in FCL setup.In doing so, we propose a novel framework, Federated Selective Inter-client Transfer (FedSeIT) which selectively combines model parameters of foreign clients.To further maximize knowledge transfer, we assess domain overlap and select informative tasks from the sequence of historical tasks at each foreign client while preserving privacy.Evaluating against the baselines, we show improved performance, a gain of (average) 12.4% in text classification over a sequence of tasks using five datasets from diverse domains.To the best of our knowledge, this is the first work that applies FCL to NLP.
Experimental Standards for Deep Learning in Natural Language Processing Research

The field of Deep Learning (DL) has undergone explosive growth during the last decade, with a substantial impact on Natural Language Processing (NLP) as well.Yet, compared to more established disciplines, a lack of common experimental standards remains an open challenge to the field at large.Starting from fundamental scientific principles, we distill ongoing discussions on experimental standards in NLP into a single, widelyapplicable methodology.Following these best practices is crucial to strengthen experimental evidence, improve reproducibility and support scientific progress.These standards are further collected in a public repository to help them transparently adapt to future needs.
KGI: An Integrated Framework for Knowledge Intensive Language Tasks

In this paper, we present a system to showcase the capabilities of the latest state-of-the-art retrieval augmented generation models trained on knowledge-intensive language tasks, such as slot filling, open domain question answering, dialogue, and fact-checking.Moreover, given a user query, we show how the output from these different models can be combined to cross-examine the outputs of each other.Particularly, we show how accuracy in dialogue can be improved using the question answering model.We are also releasing all models used in the demo as a contribution of this paper.A short video demonstrating the system is available at https://ibm.box.com/v/emnlp2022-demo.
Multi-dataset fusion for multi-task learning on face attribute recognition

The goal of face attribute recognition(FAR) is to recognize the attributes of face images, such as gender, race, etc. Multi-dataset fusion aims to train a network with multiple datasets simultaneously, which has significant advantages in deep learning. For example, in FAR, the attribute labels differ with the datasets. When using multi-dataset fusion on FAR, training data and attribute labels can be increased so that the network performance can be improved and more attributes can be recognized simultaneously. Currently, most related researchers take the multi-task learning approach on FAR, where each attribute is treated as a recognition task. However, the existing multi-task learning framework cannot be applied when using multi-dataset fusion on FAR. That is because multi-task learning requires the training data to contain the labels of all tasks, but no data contains all the attribute labels of multiple datasets. A Multi-Dataset and Multi-Task Framework(MDMTF) that takes knowledge distillation as the core is proposed in this paper to deal with the problem above. Three distillation strategies are designed in the MDMTF according to the characteristics of multi-dataset fusion on FAR. Experimental results on CelebA and MAAD-Face datasets demonstrate the effectiveness of our framework and strategies. Compared to training two networks on both datasets, respectively, that is, single dataset training, our method has significant advantages in accuracy, parameters, and computational complexity.
Map-less End-to-end Navigation of Mobile Robots via Deep Reinforcement Learning

A deep reinforcement learning model with a unique long-term memory capability is proposed in this paper, which addresses the map-less navigation of mobile robots in dynamic environments. Based on the recurrent neural network, the proposed model takes continuous historical states as input and better handles dynamic obstacles. Furthermore, a novel reward function is designed to ensure smooth navigation trajectories and satisfactory navigation results in dynamic environments. The proposed approach is evaluated on the Gazebo simulation platform, and higher navigation success rates in dynamic environments are achieved.
Convolutional Neural Network-Based Entity-Specific Common Feature Aggregation for Knowledge Graph Embedding Learning

Deep learning models present impressive capability for automatic feature extraction, where common features-based aggregation have demonstrated valuable potential in improving the model performance on text classification, sentiment analysis, etc. However, leveraging entity-specific common feature aggregation for enhancing knowledge graph representation learning has not been fully explored yet, though diverse strategies in knowledge graph embedding models have been developed in recent years. This paper proposes an innovative Convolutional Neural Network-based Entity-specific Common Feature Aggregation strategy named CNN-ECFA. Besides, a new universal framework based on the CNN-ECFA strategy is introduced for knowledge graph embedding learning. Experiments are conducted on publicly-available standard datasets for a link prediction task including WN18RR, YAGO3-10 and NELL-995. Results show that the CNN-ECFA strategy outperforms the state-of-the-art feature projection strategies with average improvements of 0.6% and 0.7% of MRR and Hits@1 on all the datasets, demonstrating our CNN-ECFA strategy is more effective for knowledge graph embedding learning. In addition, our universal framework significantly outperforms a generalized relation learning framework on WN18RR and NELL-995 with average improvements of 1.7% and 1.9% on MRR and Hits@1. The source code is publicly available at.
AI-link omnipotent pathological robot: Bridging medical meta-universe to real-world diagnosis and therapy

To address the challenges in pathological diagnosis, a pathological metaverse called the artificial intelligence (AI)-link omnipotent pathological robot (ALOPR) has recently been developed. ALOPR comes from the field of remote sensing, in which images from different sensors are analyzed in a wide spectral range. It is designed for high-resolution multispectral imaging and intelligent analysis of tumor slices with multiple biomarkers. Unlike the traditional digital pathological slice scanner, ALOPR integrates imaging, visualization, AI multimodal diagnosis, spatial omics analysis, data encryption, accurate quantification, and the tumor microenvironment. This integration, along with improvements in efficiency, accuracy, and flexibility, enables ALOPR (Figure 1) to be used in hospitals at multiple levels, including rural hospitals, county hospitals, community hospitals, and tertiary hospitals.
A constrained-time-based algorithm for vehicle maintain prediction

With the rapid development of big data and artificial intelligence, machine learning algorithms have become a popular topic in fault diagnosis and preventive maintenance. This paper focuses on predicting vehicle maintenance and proposes a constrained-time-based model using a Seq2Seq neural network structure based on GRU (gated recurrent unit) to extract the dependency relationship between vehicle maintenance time series data. Our proposed method effectively addresses the problem of the traditional method ignoring the temporal nature of vehicle maintenance data and enhancing the model's generalization ability. We also introduce an attention mechanism to automatically obtain key input time points significantly related to the current prediction output. Furthermore, we expand the model's input by concatenating other features of the vehicle attributes as constraints with the decoder's output to help the model better understand the input sequence. We conducted comparative experiments using real-world data. The results demonstrate that the constrained Seq2Seq model has better prediction performance than the traditional method, indicating that our proposed method has promising application prospects in vehicle maintenance prediction
Redesigning mathematics preservice teachers’ preparation for teaching with technology: A qualitative cross-case analysis using TPACK lenses

This qualitative cross-case study examined how a university redesigned instructional course for a preservice program supported the development of mathematics preservice teachers' Technological Pedagogical Content Knowledge (TPACK) regarding the use of Information and Communication Technologies (ICT) in their instruction. This research occurred on the context of a redesigned Mathematics Teaching Practice IV course. This course was redesigned to include in it the six SQD-model (Synthesis of Qualitative Evidence) micro level for preparing preservice teachers to use ICT in mathematics teaching and learning practices and was taught by professors with strong ICT profiles. These course activities were organized and used during an entire semester and the combination of these group of activities was called Studying, Learning and Teaching mix. We observed the preservice teachers' practices in their design and teaching of high school lessons and conducted semi-structured interviews to assess their TPACK development. While there were observable features of TPACK development in the early TPACK levels as a result of the participants' experiences, the authentic teaching experience clearly challenged their attitudes and beliefs about teaching and learning mathematics and the role ICT might play in this process. The course experience provided multiple teachable moments for the course professors as they observed and described the preservice teachers’ TPACK levels. In the end, we found that the Case 1 of preservice teachers demonstrated multiple descriptors from the Accepting TPACK level while those in Cases 2 and 3 displayed descriptors primarily associated with the Recognizing TPACK level. Results suggest that their attitudes and beliefs were challenged through a constructive and iterative process that ultimately supported their TPACK development.
YOLO-DA: An Efficient YOLO-Based Detector for Remote Sensing Object Detection

In the past few decades, many efficient object detectors have been proposed for natural scene image object detection. However, due to the complex scenes and high interclass similarity of optical remote sensing (RS) images, applying these detectors to optical RS images directly is not very effective. Most of the recent detectors pursue higher accuracy while ignoring the balance between detection accuracy and speed, which hinders the practical application of these detectors, especially in embedded devices. To meet these challenges, a fast and accurate detector based on YOLO (You Only Look Once) with decoupled attention head (YOLO-DA) is proposed, which effectively improves detection performance while only introducing minimal complexity. Specifically, an attention module at the end of the detector is designed for guiding a neural network to extract more efficient features from the complex background while also minimizing the amount of additional computation. Moreover, a lightweight decoupled detection head with enhanced classification and localization capability is developed to detect objects with high interclass similarity. In the experiments, the proposed method effectively solves the problem of high interclass similarity and improves the mAP by 6.8% on the fine-grained optical RS dataset SIMD, compared with YOLOv5-L. In addition, the proposed method improves the mAP by 1.0%, 1.7% and 0.6% on the other three publicly open optical RS datasets, respectively. Experimental results on detection accuracy and inference time demonstrate that our method achieves the best trade-off between detection performance and speed.
Choice of benchmark optimization problems does matter

Various benchmark sets have already been proposed to facilitate comparison between metaheuristics, or Evolutionary Algorithms. During the competition, typically algorithms are allowed either to run until the allowed number of function calls is exhausted (and one compares the quality of solutions found), or until a required objective function value is obtained (one compares the speed in reaching the required solution). During the last 20 years several problem sets were defined using the first approach. In this study, we test 73 optimization algorithms proposed between the 1960′s and 2022 on nine competitions based on four sets of problems (CEC 2011, CEC 2014, CEC 2017, and CEC 2020) with different dimensionalities. We intend to test the original versions of 73 algorithms “as they are”, with control parameters proposed by the authors of the particular method. The recent benchmark set, CEC 2020, includes fewer problems and allows much more function calls than the former sets. As a result, one group of algorithms perform best on older, a different one on the more recent (CEC 2020) benchmark sets. Almost all algorithms that perform best on CEC 2020 set achieve moderate-to-poor performance on older sets, including real-world problems from CEC 2011. Algorithms that perform best on older sets are more flexible than those that perform best on CEC 2020 benchmark. The choice of the benchmark may have a crucial impact on the final ranking of algorithms. The lack of tuning may affect the results that were obtained in this study, hence it is highly recommended to repeat the similar large-scale comparison with control parameters of each algorithm tuned, best by different methods, separately for each benchmark set.
Human-computer intra-action: a relational approach to digital media and technologies

The growing pervasiveness of digital technologies has exposed the entanglements of Human-Computer Interaction (HCI) with its surrounding context, from the immediate vicinity of interfaces to global issues. While often providing solutions to societal issues, interactive technologies also many times contribute to them. They impact and are impacted by the world, in a process of continuous mutual influence and co-constitution that raises important implications not only to user experience, effectiveness and efficiency, but also to ethics, social responsibility and environmental health. In this article, I draw on the new materialism notion of intra-action to propose a conceptual shift to a relational approach for HCI, which I refer to as Human-Computer Intra-Action (HCIA), capable of capturing from the outset the inherent relationships with the world that inform the design of interactive technology. To that end, I propose the notion of relationalities to reflect the synergies and antagonisms around those relationships, and discuss emerging notions of spatiality and temporality by considering the increasing hybridity of socio-technical engagements both in space (local vs. remote) and in time (immediate vs. stretched). Based on that, I propose a preliminary method for mapping intra-actions associated to interactive technologies, envisaged as a starting point to a relational take on their design process.