Reviewerly/exHarmony huggingface dataset:
Joint Optimisation of Privacy and Cost of in-App Mobile User Profiling and Targeted Ads

&lt;p&gt;Online mobile advertising ecosystems provide advertising and analytics services that collect, aggregate, process, and trade a rich amount of consumers’ personal data and carry out interest-based ad targeting, which raised serious privacy risks and growing trends of users feeling uncomfortable while using the internet services. In this paper, we address users’ privacy concerns by developing an optimal dynamic optimisation cost-effective framework for preserving user privacy for profiling, ads-based inferencing, temporal apps usage behavioral patterns, and interest-based ad targeting. A major challenge in solving this dynamic model is the lack of knowledge of time-varying updates during the profiling process. We formulate a mixed-integer optimisation problem and develop an equivalent problem to show that the proposed algorithm does not require knowledge of time-varying updates in user behavior. Following, we develop an online control algorithm to solve the equivalent problem and overcome the difficulty of solving nonlinear programming by decomposing it into various cases and to achieve a trade-off between user privacy, cost, and targeted ads. We carry out extensive experimentations and demonstrate the proposed framework’s applicability by implementing its critical components using POC (Proof Of Concept) ‘System App’. We compare the proposed framework with other privacy-protecting approaches and investigate whether it achieves better privacy and functionality for various performance parameters.&lt;/p&gt;
Are Visual Explanations Useful? A Case Study in Model-in-the-Loop Prediction

We present a randomized controlled trial for a model-in-the-loop regression task, with the goal of measuring the extent to which (1) good explanations of model predictions increase human accuracy, and (2) faulty explanations decrease human trust in the model. We study explanations based on visual saliency in an image-based age prediction task for which humans and learned models are individually capable but not highly proficient and frequently disagree. Our experimental design separates model quality from explanation quality, and makes it possible to compare treatments involving a variety of explanations of varying levels of quality. We find that presenting model predictions improves human accuracy. However, visual explanations of various kinds fail to significantly alter human accuracy or trust in the model - regardless of whether explanations characterize an accurate model, an inaccurate one, or are generated randomly and independently of the input image. These findings suggest the need for greater evaluation of explanations in downstream decision making tasks, better design-based tools for presenting explanations to users, and better approaches for generating explanations.
A Machine Learning Assisted Device Fingerprint Identification Technique for TDM-PON System

A device fingerprint identification technique is proposed based on wavelet transform and CNN classification. It can significantly accelerate the multi-path timing alignment process and improve user security. © 2021 The Author(s)
Assessing Disease Exposure Risk with Location Data: A Proposal for Cryptographic Preservation of Privacy

Governments and researchers around the world are implementing digital contact tracing solutions to stem the spread of infectious disease, namely COVID-19. Many of these solutions threaten individual rights and privacy. Our goal is to break past the false dichotomy of effective versus privacy-preserving contact tracing. We offer an alternative approach to assess and communicate users' risk of exposure to an infectious disease while preserving individual privacy. Our proposal uses recent GPS location histories, which are transformed and encrypted, and a private set intersection protocol to interface with a semi-trusted authority. There have been other recent proposals for privacy-preserving contact tracing, based on Bluetooth and decentralization, that could further eliminate the need for trust in authority. However, solutions with Bluetooth are currently limited to certain devices and contexts while decentralization adds complexity. The goal of this work is two-fold: we aim to propose a location-based system that is more privacy-preserving than what is currently being adopted by governments around the world, and that is also practical to implement with the immediacy needed to stem a viral outbreak.
An empirical investigation of the challenges of real-world reinforcement learning

Reinforcement learning (RL) has proven its worth in a series of artificial domains, and is beginning to show some successes in real-world scenarios. However, much of the research advances in RL are hard to leverage in real-world systems due to a series of assumptions that are rarely satisfied in practice. In this work, we identify and formalize a series of independent challenges that embody the difficulties that must be addressed for RL to be commonly deployed in real-world systems. For each challenge, we define it formally in the context of a Markov Decision Process, analyze the effects of the challenge on state-of-the-art learning algorithms, and present some existing attempts at tackling it. We believe that an approach that addresses our set of proposed challenges would be readily deployable in a large number of real world problems. Our proposed challenges are implemented in a suite of continuous control environments called the realworldrl-suite which we propose an as an open-source benchmark.
DocEE: A Large-Scale and Fine-grained Benchmark for Document-level Event Extraction

Event extraction aims to identify an event and then extract the arguments participating in the event.Despite the great success in sentencelevel event extraction, events are more naturally presented in the form of documents, with event arguments scattered in multiple sentences.However, a major barrier to promote documentlevel event extraction has been the lack of large-scale and practical training and evaluation datasets.In this paper, we present Do-cEE, a new document-level event extraction dataset including 27,000+ events, 180,000+ arguments.We highlight three features: largescale manual annotations, fine-grained argument types and application-oriented settings.Experiments show that there is still a big gap between state-of-the-art models and human beings (41% Vs 85% in F1 score), indicating that DocEE is an open issue.
daminglu123 at SemEval-2022 Task 2: Using BERT and LSTM to Do Text Classification

Multiword expressions (MWEs) or idiomaticity are common phenomenon in natural languages. Current pre-trained language models cannot effectively capture the meaning of these MWEs. The reason is that two normal words, after combining together, could have an abruptly different meaning than the compositionality of the meanings of each word, whereas pre-trained language models reply on words compositionality. We proposed an improved method of adding an LSTM layer to the BERT model in order to get better results on a text classification task (Subtask A). Our result is slightly better than the baseline. We also tried adding TextCNN to BERT and adding both LSTM and TextCNN to BERT. We find that adding only LSTM gives the best performance.
What do Large Language Models Learn about Scripts?

Script Knowledge (Schank and Abelson, 1975) has long been recognized as crucial for language understanding as it can help in filling in unstated information in a narrative.However, such knowledge is expensive to produce manually and difficult to induce from text due to reporting bias (Gordon and Van Durme, 2013).In this work, we are interested in the scientific question of whether explicit script knowledge is present and accessible through pre-trained generative language models (LMs).To this end, we introduce the task of generating full event sequence descriptions (ESDs) given a scenario as a natural language prompt.Through zero-shot probing, we find that generative LMs produce poor ESDs with mostly omitted, irrelevant, repeated or misordered events.To address this, we propose a pipeline-based script induction framework (SIF) which can generate good quality ESDs for unseen scenarios (e.g., bake a cake).SIF is a two-staged framework that fine-tunes LM on a small set of ESD examples in the first stage.In the second stage, ESD generated for an unseen scenario is post-processed using RoBERTa-based models to filter irrelevant events, remove repetitions, and reorder the temporally misordered events.Through automatic and manual evaluations, we demonstrate that SIF yields substantial improvements (1-3 BLEU points) over a fine-tuned LM.However, manual analysis shows that there is great room for improvement, offering a new research direction for inducing script knowledge 1 .
CS-UM6P at SemEval-2022 Task 6: Transformer-based Models for Intended Sarcasm Detection in English and Arabic

Sarcasm is a form of figurative language where the intended meaning of a sentence differs from its literal meaning.This poses a serious challenge to several Natural Language Processing (NLP) applications such as Sentiment Analysis, Opinion Mining, and Author Profiling.In this paper, we present our participating system to the intended sarcasm detection task in English and Arabic languages.Our system 1 consists of three deep learning-based models leveraging two existing pre-trained language models for Arabic and English.We have participated in all sub-tasks.Our official submissions achieve the best performance on sub-task A for Arabic language and rank second in sub-task B. For sub-task C, our system is ranked 7th and 11th on Arabic and English datasets, respectively.
ID10M: Idiom Identification in 10 Languages

Idioms are phrases which present a figurative meaning that cannot be (completely) derived by looking at the meaning of their individual components.Identifying and understanding idioms in context is a crucial goal and a key challenge in a wide range of Natural Language Understanding tasks.Although efforts have been undertaken in this direction, the automatic identification and understanding of idioms is still a largely underinvestigated area, especially when operating in a multilingual scenario.In this paper, we address such limitations and put forward several new contributions: we propose a novel multilingual Transformer-based system for the identification of idioms; we produce a highquality automatically-created training dataset in 10 languages, along with a novel manuallycurated evaluation benchmark; finally, we carry out a thorough performance analysis and release our evaluation suite at https:// github.com/Babelscape/ID10M.
On the Use of External Data for Spoken Named Entity Recognition

Spoken language understanding (SLU) tasks involve mapping from speech signals to semantic labels.Given the complexity of such tasks, good performance is expected to require large labeled datasets, which are difficult to collect for each new task and domain.However, recent advances in self-supervised speech representations have made it feasible to consider learning SLU models with limited labeled data.In this work, we focus on low-resource spoken named entity recognition (NER) and address the question: Beyond selfsupervised pre-training, how can we use external speech and/or text data that are not annotated for the task?We consider self-training, knowledge distillation, and transfer learning for end-to-end (E2E) and pipeline (speech recognition followed by text NER) approaches.We find that several of these approaches improve performance in resource-constrained settings beyond the benefits from pre-trained representations.Compared to prior work, we find relative improvements in F1 of up to 16%.While the best baseline model is a pipeline approach, the best performance using external data is ultimately achieved by an E2E model.We provide detailed comparisons and analyses, developing insights on, for example, the effects of leveraging external data on (i) different categories of NER errors and (ii) the switch in performance trends between pipeline and E2E models.
R2D2 at SemEval-2022 Task 5: Attention is only as good as its Values! A multimodal system for identifying misogynist memes

This paper describes the multimodal deep learning system proposed for SemEval 2022 Task 5: MAMI -Multimedia Automatic Misogyny Identification.We participated in both Subtasks, i.e.Subtask A: Misogynous meme identification, and Subtask B: Identifying type of misogyny among potential overlapping categories (stereotype, shaming, objectification, violence).The proposed architecture uses pretrained models as feature extractors for text and images.We use these features to learn multimodal representation using methods like concatenation and scaled dot product attention.Classification layers are used on fused features as per the subtask definition.We also performed experiments using unimodal models for setting up comparative baselines.Our best performing system achieved an F1 score of 0.757 and was ranked 3 rd in Subtask A. On Subtask B, our system performed well with an F1 score of 0.690 and was ranked 10 th on the leaderboard.We further show extensive experiments using combinations of different pre-trained models which will be helpful as baselines for future work.
ALLSH: Active Learning Guided by Local Sensitivity and Hardness

Active learning, which effectively collects informative unlabeled data for annotation, reduces the demand for labeled data.In this work, we propose to retrieve unlabeled samples with a local sensitivity and hardness-aware acquisition function.The proposed method generates data copies through local perturbations and selects data points whose predictive likelihoods diverge the most from their copies.We further empower our acquisition function by injecting the select-worst case perturbation.Our method achieves consistent gains over the commonly used active learning strategies in various classification tasks.Furthermore, we observe consistent improvements over the baselines on the study of prompt selection in prompt-based fewshot learning.These experiments demonstrate that our acquisition guided by local sensitivity and hardness can be effective and beneficial for many NLP tasks.
The subtle language of exclusion: Identifying the Toxic Speech of Trans-exclusionary Radical Feminists

Toxic language can take many forms, from explicit hate speech to more subtle microaggressions.Within this space, models identifying transphobic language have largely focused on overt forms.However, a more pernicious and subtle source of transphobic comments comes in the form of statements made by Transexclusionary Radical Feminists (TERFs); these statements often appear seemingly-positive and promote women's causes and issues, while simultaneously denying the inclusion of transgender women as women.Here, we introduce two models to mitigate this antisocial behavior.The first model identifies TERF users in social media, recognizing that these users are a main source of transphobic material that enters mainstream discussion and whom other users may not desire to engage with in good faith.The second model tackles the harder task of recognizing the masked rhetoric of TERF messages and introduces a new dataset to support this task.Finally, we discuss the ethics of deploying these models to mitigate the harm of this language, arguing for a balanced approach that allows for restorative interactions.* Work performed in part at the University of Michigan 1 We acknowledge that the use of the term TERF is potentially contentious, as some individuals who identify these
Maximum Bayes Smatch Ensemble Distillation for AMR Parsing

AMR parsing has experienced an unprecendented increase in performance in the last three years, due to a mixture of effects including architecture improvements and transfer learning.Self-learning techniques have also played a role in pushing performance forward.However, for most recent high performant parsers, the effect of self-learning and silver data augmentation seems to be fading.In this paper we propose to overcome this diminishing returns of silver data by combining Smatch-based ensembling techniques with ensemble distillation.In an extensive experimental setup, we push single model English parser performance to a new state-of-the-art, 85.9 (AMR2.0)and 84.3 (AMR3.0),and return to substantial gains from silver data augmentation.We also attain a new state-of-the-art for cross-lingual AMR parsing for Chinese, German, Italian and Spanish.Finally we explore the impact of the proposed technique on domain adaptation, and show that it can produce gains rivaling those of human annotated data for QALD-9 and achieve a new state-of-the-art for BioAMR.
Static and Dynamic Speaker Modeling based on Graph Neural Network for Emotion Recognition in Conversation

Prakhar Saxena, Yin Jou Huang, Sadao Kurohashi. Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Student Research Workshop. 2022.
Inducing and Using Alignments for Transition-based AMR Parsing

Transition-based parsers for Abstract Meaning Representation (AMR) rely on node-toword alignments.These alignments are learned separately from parser training and require a complex pipeline of rule-based components, pre-processing, and post-processing to satisfy domain-specific constraints.Parsers also train on a point-estimate of the alignment pipeline, neglecting the uncertainty due to the inherent ambiguity of alignment.In this work we explore two avenues for overcoming these limitations.First, we propose a neural aligner for AMR that learns node-to-word alignments without relying on complex pipelines.We subsequently explore a tighter integration of aligner and parser training by considering a distribution over oracle action sequences arising from aligner uncertainty.Empirical results show this approach leads to more accurate alignments and generalization better from the AMR2.0 to AMR3.0 corpora.We attain a new state-ofthe art for gold-only trained models, matching silver-trained performance without the need for beam search on AMR3.0.
Cross-linguistic Comparison of Linguistic Feature Encoding in BERT Models for Typologically Different Languages

Though recently there have been an increased interest in how pre-trained language models encode different linguistic features, there is still a lack of systematic comparison between languages with different morphology and syntax.In this paper, using BERT as an example of a pre-trained model, we compare how three typologically different languages (English, Korean, and Russian) encode morphology and syntax features across different layers.In particular, we contrast languages which differ in a particular aspect, such as flexibility of word order, head directionality, morphological type, presence of grammatical gender, and morphological richness, across four different tasks.
Looking for a Handsome Carpenter! Debiasing GPT-3 Job Advertisements

The growing capability and availability of generative language models has enabled a wide range of new downstream tasks.Academic research has identified, quantified and mitigated biases present in language models but is rarely tailored to downstream tasks where wider impact on individuals and society can be felt.In this work, we leverage one popular generative language model, GPT-3, with the goal of writing unbiased and realistic job advertisements.We first assess the bias and realism of zero-shot generated advertisements and compare them to real-world advertisements.We then evaluate prompt-engineering and fine-tuning as debiasing methods.We find that prompt-engineering with diversity-encouraging prompts gives no significant improvement to bias, nor realism.Conversely, fine-tuning, especially on unbiased real advertisements, can improve realism and reduce bias.
Towards Computationally Feasible Deep Active Learning

Active learning (AL) is a prominent technique for reducing the annotation effort required for training machine learning models.Deep learning offers a solution for several essential obstacles to deploying AL in practice but introduces many others.One of such problems is the excessive computational resources required to train an acquisition model and estimate its uncertainty on instances in the unlabeled pool.We propose two techniques that tackle this issue for text classification and tagging tasks, offering a substantial reduction of AL iteration duration and the computational overhead introduced by deep acquisition models in AL.We also demonstrate that our algorithm that leverages pseudo-labeling and distilled models overcomes one of the essential obstacles revealed previously in the literature.Namely, it was shown that due to differences between an acquisition model used to select instances during AL and a successor model trained on the labeled data, the benefits of AL can diminish.We show that our algorithm, despite using a smaller and faster acquisition model, is capable of training a more expressive successor model with higher performance. 1
Accounting for Offensive Speech as a Practice of Resistance

Tasks such as toxicity detection, hate speech detection, and online harassment detection have been developed for identifying interactions involving offensive speech.In this work we articulate the need for a relational understanding of offensiveness to help distinguish denotative offensive speech from offensive speech serving as a mechanism through which marginalized communities resist oppressive social norms.Using examples from the queer community, we argue that evaluations of offensive speech must focus on the impacts of language use.We motivate this use of language in Cynic philosophy and use it to frame a use of offensive speech as a practice of resistance.We also explore the degree to which NLP systems may encounter limits to modeling relational context.
Commonsense and Named Entity Aware Knowledge Grounded Dialogue Generation

Grounding dialogue on external knowledge and interpreting linguistic patterns in dialogue history context, such as ellipsis, anaphora, and co-references is critical for dialogue comprehension and generation.In this paper, we present a novel open-domain dialogue generation model which effectively utilizes the large-scale commonsense and named entity based knowledge in addition to the unstructured topic-specific knowledge associated with each utterance.We enhance the commonsense knowledge with named entity-aware structures using co-references.Our proposed model utilizes a multi-hop attention layer to preserve the most accurate and critical parts of the dialogue history and the associated knowledge.In addition, we employ a Commonsense and Named Entity Enhanced Attention Module, which starts with the extracted triples from various sources and gradually finds the relevant supporting set of triples using multi-hop attention with the query vector obtained from the interactive dialogue-knowledge module.Empirical results on two benchmark dataset demonstrate that our model significantly outperforms the state-of-the-art methods in terms of both automatic evaluation metrics and human judgment.
Pairwise Representation Learning for Event Coreference

Natural Language Processing tasks such as resolving the coreference of events require understanding the relations between two text snippets.These tasks are typically formulated as (binary) classification problems over independently induced representations of the text snippets.In this work, we develop a Pairwise Representation Learning (PAIRWISERL) scheme for the event mention pairs, in which we jointly encode a pair of text snippets so that the representation of each mention in the pair is induced in the context of the other one.Furthermore, our representation supports a finer, structured representation of the text snippet to facilitate encoding events and their arguments.We show that PAIRWISERL, despite its simplicity, outperforms the prior state-of-the-art event coreference systems on both cross-document and within-document event coreference benchmarks.We also conduct in-depth analysis in terms of the improvement and the limitation of pairwise representation so as to provide insights for future work. 1