Reviewerly/exHarmony huggingface dataset:
A nonlinear multi-label learning model based on Tanh mapping

The relationship between features and labels plays an important role in multi-label learning. The purpose of multi-label learning is to learn a mapping from the feature space to the label space. Many of the existing methods decompose the observed label matrix into the product of a low-rank latent label matrix and a projection matrix, and construct a linear mapping between the feature space and low-rank latent label space. However, in practice, the relationship between two high-dimensional spaces is often nonlinear. Consequently, these linear models may not sufficiently capture the data distribution and the true dependencies between features and labels. Herein, a novel classification model for multi-label learning is proposed, which considers nonlinear mapping from the feature space to the latent label space. Because of correlation between labels, the label matrix is usually of a low rank. To explore the low-rank structure, a latent label matrix is obtained using the decomposition of the original label matrix. To characterize the nonlinear relationship between features and latent labels, the tanh function is employed to learn a nonlinear classifier. The tanh function describes the distribution of labels by constraining the domain of the latent label space to the interval [−1,1]. In addition, the Frobenius norm regularization constraint is imposed on the variables in the model for optimization. The experimental results demonstrate that the proposed method is robust and comparable to state-of-the-art multi-label learning methods.
Monitoring and Modelling Photovoltaic Soiling in an Urban Environment

A growing number of photovoltaic (PV) modules is being installed in cities and urban areas, thanks to the lowering costs of this technology and to the success of building-and infrastructure-integrated applications. Despite this raising capacity, there is still a lack of studies on the performance of PV in urban areas. In particular, the accumulation of dust, dirt and contaminant can cause losses also in this configuration. However, urban soiling has been rarely analysed. This work shares the soiling losses measured by a sensor installed in a suburban area of Rome, Italy. In addition, the quality of two soiling estimation models is assessed, by comparing their predictions with the actual measurements. Both models are found to significantly underestimate the losses at the investigated location during the most intense soiling period. However, the errors are minimized through the recalibration of the models, which can be, this way, used to estimate the losses at additional locations.
NOFA: NeRF-based One-shot Facial Avatar Reconstruction

3D facial avatar reconstruction has been a significant research topic in computer graphics and computer vision, where photo-realistic rendering and flexible controls over poses and expressions are necessary for many related applications. Recently, its performance has been greatly improved with the development of neural radiance fields (NeRF). However, most existing NeRF-based facial avatars focus on subject-specific reconstruction and reenactment, requiring multi-shot images containing different views of the specific subject for training, and the learned model cannot generalize to new identities, limiting its further applications. In this work, we propose a one-shot 3D facial avatar reconstruction framework that only requires a single source image to reconstruct a high-fidelity 3D facial avatar. For the challenges of lacking generalization ability and missing multi-view information, we leverage the generative prior of 3D GAN and develop an efficient encoder-decoder network to reconstruct the canonical neural volume of the source image, and further propose a compensation network to complement facial details. To enable fine-grained control over facial dynamics, we propose a deformation field to warp the canonical volume into driven expressions. Through extensive experimental comparisons, we achieve superior synthesis results compared to several state-of-the-art methods.
Generative Image Prior Constrained Subspace Reconstruction for High-Resolution MRSI

We propose a novel method that integrates generative image priors with subspace constrained MRSI reconstruction. A sufficient and flexible image representation was first generated by adapting a pretrained StyleGAN to subject-specific anatomical images. We validate that StyleGAN can be flexibly adapted to accurately represent different contrast of the same subject. The adapted GAN prior is then used to model the spatial coefficients in the subspace-based reconstruction. Improved performance over the original subspace reconstruction in the SPICE framework is demonstrated using simulation and in vivo data.
Enhancing Arabic Sentiment Analysis Through a Hybrid Deep Learning Approach

Sentiment analysis is a key procedure in many natural language processing systems that extract emotions from textual input.In recent years, Arabic sentiment analysis has become a significant study area.With the growth of social media platforms and data flow, especially in Arabic, substantial difficulties have emerged that call for new strategies to address problems, such as the Arabic language's complicated development and the complexity of the multiple, binary, or massively imbalanced Arabic dataset categorizations.Besides, the system's limitations, whether in online analysis tools, deep or machine learning.This paper proposes a new conjunction method for Arabic sentiment analysis (ASA) called Hybrid Convolution Gate Long (HCGL).This method allows us to extract the best features, handle sequences of different lengths to capture context, address the issue of disappearing error gradients, and improve prediction performance.To match other research works, we conduct studies using a variety of data splits.Furthermore, we pay great attention to Arabic preparation by using allencompassing procedures that help us address the Arabic language context.The proposed method performs highest in terms of 2-class way efficiency (95.88%), followed by 3-class way performance (95.92%).Additionally, we apply it to the massive Arabic sentiment dataset; it performs well, achieving 88.40% in less time.
A Causal Framework to Quantify the Robustness of Mathematical Reasoning with Language Models

We have recently witnessed a number of impressive results on hard mathematical reasoning problems with language models.At the same time, the robustness of these models has also been called into question; recent works have shown that models can rely on shallow patterns in the problem description when generating a solution.Building on the idea of behavioral testing, we propose a novel framework, which pins down the causal effect of various factors in the input, e.g., the surface form of the problem text, the operands, and math operators on the output solution.By grounding the behavioral analysis in a causal graph describing an intuitive reasoning process, we study the behavior of language models in terms of robustness and sensitivity to direct interventions in the input space.We apply our framework on a test bed of math word problems.Our analysis shows that robustness does not appear to continuously improve as a function of size, but the GPT-3 Davinci models (175B) achieve a dramatic improvement in both robustness and sensitivity compared to all other GPT variants. 1* Equal contribution. 1 Our code and data are available at https://github.com/alestolfo/causal-math. Kyle could fit n 1 =26 drawings on each page.If he has n 2 =11 pages, the number of drawings he can make is ___.Kyle could fit n 1 =2 drawings on each page.If he has n 2 =143 pages, the number of drawings he can make is ___.
Membership Inference Attacks against Language Models via Neighbourhood Comparison

Membership Inference attacks (MIAs) aim to predict whether a data sample was present in the training data of a machine learning model or not, and are widely used for assessing the privacy risks of language models. Most existing attacks rely on the observation that models tend toassign higher probabilities to their training samples than non-training points. However, simple thresholding of the model score in isolation tends to lead to high false-positive rates as it does not account for the intrinsic complexity of a sample. Recent work has demonstrated that reference-based attacks which compare model scores to those obtained from a reference model trained on similar data can substantially improve the performance of MIAs.However, in order to train reference models, attacks of this kind make the strong and arguably unrealistic assumption that an adversary has access to samples closely resembling the original training data. Therefore, we investigate their performance in more realistic scenarios and find that they are highly fragile in relation to the data distribution used to train reference models. To investigate whether this fragility provides a layer of safety, we propose and evaluate neighbourhood attacks, which compare model scores for a given sample to scores of synthetically generated neighbour texts and therefore eliminate the need for access to the training data distribution. We show that, in addition to being competitive with reference-based attacks that have perfect knowledge about the training data distribution, our attack clearly outperforms existing reference-free attacks as well as reference-based attacks with imperfect knowledge, which demonstrates the need for a reevaluation of the threat model of adversarial attacks.
EUREKA: EUphemism Recognition Enhanced through Knn-based methods and Augmentation

We introduce EUREKA, an ensemble-based approach for performing automatic euphemism detection.We (1) identify and correct potentially mislabelled rows in the dataset, (2) curate an expanded corpus called EuphAug, (3) leverage model representations of Potentially Euphemistic Terms (PETs), and (4) explore using representations of semantically close sentences to aid in classification.Using our augmented dataset and kNN-based methods, EUREKA 1 was able to achieve state-of-the-art results on the public leaderboard of the Euphemism Detection Shared Task, ranking first with a macro F1 score of 0.881.
A Benchmark and Dataset for Post-OCR text correction in Sanskrit

Sanskrit is a classical language with about 30 million extant manuscripts fit for digitisation, available in written, printed or scannedimage forms.However, it is still considered to be a low-resource language when it comes to available digital resources.In this work, we release a post-OCR text correction dataset containing around 218,000 sentences, with 1.5 million words, from 30 different books.Texts in Sanskrit are known to be diverse in terms of their linguistic and stylistic usage since Sanskrit was the 'lingua franca' for discourse in the Indian subcontinent for about 3 millennia.Keeping this in mind, we release a multi-domain dataset, from areas as diverse as astronomy, medicine and mathematics, with some of them as old as 18 centuries.Further, we release multiple strong baselines as benchmarks for the task, based on pre-trained Seq2Seq language models.We find that our best-performing model, consisting of byte level tokenization in conjunction with phonetic encoding (Byt5+SLP1), yields a 23% point increase over the OCR output in terms of word and character error rates.Moreover, we perform extensive experiments in evaluating these models on their performance and analyse common causes of mispredictions both at the graphemic and lexical levels.
Counterspeeches up my sleeve! Intent Distribution Learning and Persistent Fusion for Intent-Conditioned Counterspeech Generation

Counterspeech has been demonstrated to be an efficacious approach for combating hate speech.While various conventional and controlled approaches have been studied in recent years to generate counterspeech, a counterspeech with a certain intent may not be sufficient in every scenario.Due to the complex and multifaceted nature of hate speech, utilizing multiple forms of counter-narratives with varying intents may be advantageous in different circumstances.In this paper, we explore intent-conditioned counterspeech generation.At first, we develop IntentCONAN, a diversified intent-specific counterspeech dataset with 6831 counterspeeches conditioned on five intents, i.e., informative, denouncing, question, positive, and humour.Subsequently, we propose QUARC, a two-stage framework for intent-conditioned counterspeech generation.QUARC leverages vector-quantized representations learned for each intent category along with PerFuMe, a novel fusion module to incorporate intent-specific information into the model.Our evaluation demonstrates that QUARC outperforms several baselines by an average of ~10% across evaluation metrics.An extensive human evaluation supplements our hypothesis of better and more appropriate responses than comparative systems.
Non-Sequential Graph Script Induction via Multimedia Grounding

Online resources such as wikiHow compile a wide range of scripts for performing everyday tasks, which can assist models in learning to reason about procedures. 1 However, the scripts are always presented in a linear manner, which does not reflect the flexibility displayed by people executing tasks in real life.For example, in the CrossTask Dataset, 64.5% of consecutive step pairs are also observed in the reverse order, suggesting their ordering is not fixed.In addition, each step has an average of 2.56 frequent 2 next steps, demonstrating "branching".In this paper, we propose a new challenging task of non-sequential graph script induction, aiming to capture optional and interchangeable steps in procedural planning.To automate the induction of such graph scripts for given tasks, we propose to take advantage of loosely aligned videos of people performing the tasks.In particular, we design a multimodal framework to ground procedural videos to wikiHow textual steps and thus transform each video into an observed step path on the latent ground truth graph script.This key transformation enables us to train a script knowledge model capable of both generating explicit graph scripts for learnt tasks and predicting future steps given a partial step sequence.Our best model outperforms the strongest pure text/vision baselines by 17.52% absolute gains on F 1 @3 for next step prediction and 13.8% absolute gains on Acc@1 for partial sequence completion.Human evaluation shows our model outperforming the wikiHow linear baseline by 48.76% absolute gains in capturing sequential and non-sequential step relations.
Zero-shot Faithful Factual Error Correction

Faithfully correcting factual errors is critical for maintaining the integrity of textual knowledge bases and preventing hallucinations in generative models.Drawing on humans' ability to identify and correct factual errors, we present a zero-shot framework that formulates questions about input claims, looks for correct answers in the given evidence, and assesses the faithfulness of each correction based on its consistency with the evidence.Our zero-shot framework outperforms fully-supervised approaches, as demonstrated by experiments on the FEVER and SCIFACT datasets, where our outputs are shown to be more faithful.More importantly, the decomposability nature of our framework inherently provides interpretability.Additionally, to reveal the most suitable metrics for evaluating factual error corrections, we analyze the correlation between commonly used metrics with human judgments in terms of three different dimensions regarding intelligibility and faithfulness.1
B2T Connection: Serving Stability and Performance in Deep Transformers

From the perspective of the layer normalization (LN) positions, the architectures of Transformers can be categorized into two types: Post-LN and Pre-LN.Recent Transformers tend to be Pre-LN because, in Post-LN with deep Transformers (e.g., those with ten or more layers), the training is often unstable, resulting in useless models.However, Post-LN has consistently achieved better performance than Pre-LN in relatively shallow Transformers (e.g., those with six or fewer layers).This study first investigates the reason for these discrepant observations empirically and theoretically and made the following discoveries: 1, the LN in Post-LN is the main source of the vanishing gradient problem that leads to unstable training, whereas Pre-LN prevents it, and 2, Post-LN tends to preserve larger gradient norms in higher layers during the back-propagation, which may lead to effective training.Exploiting the new findings, we propose a method that can provide both high stability and effective training by a simple modification of Post-LN.We conduct experiments on a wide range of text generation tasks.The experimental results demonstrate that our method outperforms Pre-LN, and enables stable training regardless of the shallow or deep layer settings.
ChatBack: Investigating Methods of Providing Grammatical Error Feedback in a GUI-based Language Learning Chatbot

The increasing use of AI chatbots as conversation partners for second-language learners highlights the importance of providing effective feedback.To ensure a successful learning experience, it is essential for researchers and practitioners to understand the optimal timing, methods of delivery, and types of feedback that are most beneficial to learners.Synchronous grammar corrective feedback (CF) has been shown to be more effective than asynchronous methods in online writing tasks.Additionally, self-correction by language learners has proven more beneficial than teacherprovided correction, particularly for spoken language skills and non-novice learners.However, existing language-learning AI chatbots often lack synchronous CF and self-correction capabilities.To address this, we propose a synchronous conversational corrective feedback (CCF) method, which allows self-correction and provides metalinguistic explanations (ME).Our experiments examine the effects of different feedback presentation methods and selfcorrection on users' learning experiences and intention to use the system.Our study suggests that in chatbot-driven language-learning tools, corrective feedback is more effectively delivered through means other than the social chatbot, such as a GUI interface.Furthermore, we found that guided self-correction offers a superior learning experience compared to providing explicit corrections, particularly for learners with high learning motivation or lower linguistic ability.
NoisywikiHow: A Benchmark for Learning with Real-world Noisy Labels in Natural Language Processing

Large-scale datasets in the real world inevitably involve label noise.Deep models can gradually overfit noisy labels and thus degrade model generalization.To mitigate the effects of label noise, learning with noisy labels (LNL) methods are designed to achieve better generalization performance.Due to the lack of suitable datasets, previous studies have frequently employed synthetic label noise to mimic realworld label noise.However, synthetic noise is not instance-dependent, making this approximation not always effective in practice.Recent research has proposed benchmarks for learning with real-world noisy labels.However, the noise sources within may be single or fuzzy, making benchmarks different from data with heterogeneous label noises in the real world.To tackle these issues, we contribute Noisywik-iHow, the largest NLP benchmark built with minimal supervision.Specifically, inspired by human cognition, we explicitly construct multiple sources of label noise to imitate human errors throughout the annotation, replicating realworld noise, whose corruption is affected by both ground-truth labels and instances.Moreover, we provide a variety of noise levels to support controlled experiments on noisy data, enabling us to evaluate LNL methods systematically and comprehensively.After that, we conduct extensive multi-dimensional experiments on a broad range of LNL methods, obtaining new and intriguing findings.
FINDINGS OF THE IWSLT 2023 EVALUATION CAMPAIGN

This paper reports on the shared tasks organized by the 20th IWSLT Conference.The shared tasks address 9 scientific challenges in spoken language translation: simultaneous and offline translation, automatic subtitling and dubbing, speech-to-speech translation, multilingual, dialect and low-resource speech translation, and formality control.The shared tasks attracted a total of 38 submissions by 31 teams.The growing interest towards spoken language translation is also witnessed by the constantly increasing number of shared task organizers and contributors to the overview paper, almost evenly distributed across industry and academia.
Dataset Distillation with Attention Labels for Fine-tuning BERT

Dataset distillation aims to create a small dataset of informative synthetic samples to rapidly train neural networks that retain the performance of the original dataset.In this paper, we focus on constructing distilled fewshot datasets for natural language processing (NLP) tasks to fine-tune pre-trained transformers.Specifically, we propose to introduce attention labels, which can efficiently distill the knowledge from the original dataset and transfer it to the transformer models via attention probabilities.We evaluated our dataset distillation methods in four various NLP tasks and demonstrated that it is possible to create distilled few-shot datasets with the attention labels, yielding impressive performances for finetuning BERT.Specifically, in AGNews, a fourclass news classification task, our distilled fewshot dataset achieved up to 93.2% accuracy, which is 98.5% performance of the original dataset even with only one sample per class and only one gradient step.
TempLM: Distilling Language Models into Template-Based Generators

While pretrained language models (PLMs) have greatly improved text generation, they have also been known to produce unfaithful or inappropriate content.In contrast, classic template-based systems provide strong guarantees of faithfulness at the cost of fluency.We propose TempLM, which achieves the best of both worlds by distilling a PLM into a templatebased generator.On the E2E and SynthBio data-to-text datasets, we show that TempLM is more faithful than the original PLM and is more fluent than prior template systems.Notably, on an out-of-domain evaluation, TempLM reduces a finetuned BART model's unfaithfulness rate from 83% to 0%.In a human study, we find that TempLM's templates substantially improve upon human-written ones in BERTScore.
Zero-Shot Classification by Logical Reasoning on Natural Language Explanations

Humans can classify data of an unseen category by reasoning on its language explanations.This ability is owing to the compositional nature of language: we can combine previously seen attributes to describe the new category.For example, we might describe a sage thrasher as "it has a slim straight relatively short bill, yellow eyes and a long tail", so that others can use their knowledge of attributes "slim straight relatively short bill", "yellow eyes" and "long tail" to recognize a sage thrasher.Inspired by this observation, in this work we tackle zero-shot classification task by logically parsing and reasoning on natural language explanations.To this end, we propose the framework CLORE (Classification by LOgical Reasoning on Explanations).While previous methods usually regard textual information as implicit features, CLORE parses explanations into logical structures and then explicitly reasons along thess structures on the input to produce a classification score.Experimental results on explanation-based zero-shot classification benchmarks demonstrate that CLORE is superior to baselines, which we further show mainly comes from higher scores on tasks requiring more logical reasoning.We also demonstrate that our framework can be extended to zeroshot classification on visual modality.Alongside classification decisions, CLORE can provide the logical parsing and reasoning process as a clear form of rationale.Through empirical analysis we demonstrate that CLORE is also less affected by linguistic biases than baselines.1