pwc-archive/papers-with-abstracts huggingface dataset:
Findings of the Shared Task on Multimodal Sentiment Analysis and Troll Meme Classification in Dravidian Languages
Authors: Premjith B, Bharathi Raja Chakravarthi, Malliga Subramanian, Bharathi B, Soman Kp, Dhanalakshmi V, Sreelakshmi K, Arunaggiri Pandian, Prasanna Kumaresan
Date: 2022-05-01 00:00:00
Proceeding: DravidianLangTech (ACL) 2022 5
Tasks: Classification, Meme Classification, Multimodal Sentiment Analysis, Sentiment Analysis

This paper presents the findings of the shared task on Multimodal Sentiment Analysis and Troll meme classification in Dravidian languages held at ACL 2022. Multimodal sentiment analysis deals with the identification of sentiment from video. In addition to video data, the task requires the analysis of corresponding text and audio features for the classification of movie reviews into five classes. We created a dataset for this task in Malayalam and Tamil. The Troll meme classification task aims to classify multimodal Troll memes into two categories. This task assumes the analysis of both text and image features for making better predictions. The performance of the participating teams was analysed using the F1-score. Only one team submitted their results in the Multimodal Sentiment Analysis task, whereas we received six submissions in the Troll meme classification task. The only team that participated in the Multimodal Sentiment Analysis shared task obtained an F1-score of 0.24. In the Troll meme classification task, the winning team achieved an F1-score of 0.596.
Findings of the Shared Task on Offensive Span Identification fromCode-Mixed Tamil-English Comments
Authors: Manikandan Ravikiran, Bharathi Raja Chakravarthi, Anand Kumar Madasamy, Sangeetha S, Ratnavel Rajalakshmi, Sajeetha Thavareesan, Rahul Ponnusamy, Shankar Mahadevan
Date: 2022-05-01 00:00:00
Proceeding: DravidianLangTech (ACL) 2022 5

Offensive content moderation is vital in social media platforms to support healthy online discussions. However, their prevalence in code-mixed Dravidian languages is limited to classifying whole comments without identifying part of it contributing to offensiveness. Such limitation is primarily due to the lack of annotated data for offensive spans. Accordingly, in this shared task, we provide Tamil-English code-mixed social comments with offensive spans. This paper outlines the dataset so released, methods, and results of the submitted systems.
Overview of the Shared Task on Machine Translation in Dravidian Languages
Authors: Anand Kumar Madasamy, Asha Hegde, Shubhanker Banerjee, Bharathi Raja Chakravarthi, Ruba Priyadharshini, Hosahalli Shashirekha, John McCrae
Date: 2022-05-01 00:00:00
Proceeding: DravidianLangTech (ACL) 2022 5
Tasks: Machine Translation, Translation

This paper presents an outline of the shared task on translation of under-resourced Dravidian languages at DravidianLangTech-2022 workshop to be held jointly with ACL 2022. A description of the datasets used, approach taken for analysis of submissions and the results have been illustrated in this paper. Five sub-tasks organized as a part of the shared task include the following translation pairs: Kannada to Tamil, Kannada to Telugu, Kannada to Sanskrit, Kannada to Malayalam and Kannada to Tulu. Training, development and test datasets were provided to all participants and results were evaluated on the gold standard datasets. A total of 16 research groups participated in the shared task and a total of 12 submission runs were made for evaluation. Bilingual Evaluation Understudy (BLEU) score was used for evaluation of the translations.
Findings of the Shared Task on Emotion Analysis in Tamil
Authors: Anbukkarasi Sampath, Thenmozhi Durairaj, Bharathi Raja Chakravarthi, Ruba Priyadharshini, Subalalitha Cn, Kogilavani Shanmugavadivel, Sajeetha Thavareesan, Sathiyaraj Thangasamy, Parameswari Krishnamurthy, Adeep Hande, Sean Benhur, Kishore Ponnusamy, Santhiya Pandiyan
Date: 2022-05-01 00:00:00
Proceeding: DravidianLangTech (ACL) 2022 5
Tasks: Emotion Recognition

This paper presents the overview of the shared task on emotional analysis in Tamil. The result of the shared task is presented at the workshop. This paper presents the dataset used in the shared task, task description, and the methodology used by the participants and the evaluation results of the submission. This task is organized as two Tasks. Task A is carried with 11 emotions annotated data for social media comments in Tamil and Task B is organized with 31 fine-grained emotion annotated data for social media comments in Tamil. For conducting experiments, training and development datasets were provided to the participants and results are evaluated for the unseen data. Totally we have received around 24 submissions from 13 teams. For evaluating the models, Precision, Recall, micro average metrics are used.
Findings of the Shared Task on Multi-task Learning in Dravidian Languages
Authors: Bharathi Raja Chakravarthi, Ruba Priyadharshini, Subalalitha Cn, Sangeetha S, Malliga Subramanian, Kogilavani Shanmugavadivel, Parameswari Krishnamurthy, Adeep Hande, Siddhanth U Hegde, Roshan Nayak, Swetha Valli
Date: 2022-05-01 00:00:00
Proceeding: DravidianLangTech (ACL) 2022 5
Tasks: Language Identification, Multi-Task Learning, Sentence

We present our findings from the first shared task on Multi-task Learning in Dravidian Languages at the second Workshop on Speech and Language Technologies for Dravidian Languages. In this task, a sentence in any of three Dravidian Languages is required to be classified into two closely related tasks namely Sentiment Analyis (SA) and Offensive Language Identification (OLI). The task spans over three Dravidian Languages, namely, Kannada, Malayalam, and Tamil. It is one of the first shared tasks that focuses on Multi-task Learning for closely related tasks, especially for a very low-resourced language family such as the Dravidian language family. In total, 55 people signed up to participate in the task, and due to the intricate nature of the task, especially in its first iteration, 3 submissions have been received.
Overview of Abusive Comment Detection in Tamil-ACL 2022
Authors: Ruba Priyadharshini, Bharathi Raja Chakravarthi, Subalalitha Cn, Thenmozhi Durairaj, Malliga Subramanian, Kogilavani Shanmugavadivel, Siddhanth U Hegde, Prasanna Kumaresan
Date: 2022-05-01 00:00:00
Proceeding: DravidianLangTech (ACL) 2022 5

The social media is one of the significantdigital platforms that create a huge im-pact in peoples of all levels. The commentsposted on social media is powerful enoughto even change the political and businessscenarios in very few hours. They alsotend to attack a particular individual ora group of individuals. This shared taskaims at detecting the abusive comments in-volving, Homophobia, Misandry, Counter-speech, Misogyny, Xenophobia, Transpho-bic. The hope speech is also identified. Adataset collected from social media taggedwith the above said categories in Tamiland Tamil-English code-mixed languagesare given to the participants. The par-ticipants used different machine learningand deep learning algorithms. This paperpresents the overview of this task compris-ing the dataset details and results of theparticipants.
Discovering and Characterizing Mobility Patterns in Urban Spaces: A Study of Manhattan Taxi Data
Authors: Lisette Espín-Noboa, Florian Lemmerich, Philipp Singer, Markus Strohmaier
Date: 2016-01-20 00:00:00
Tasks: Recommendation Systems

Nowadays, human movement in urban spaces can be traced digitally in many cases. It can be observed that movement patterns are not constant, but vary across time and space. In this work,we characterize such spatio-temporal patterns with an innovative combination of two separate approaches that have been utilized for studying human mobility in the past. First, by using non-negative tensor factorization (NTF), we are able to cluster human behavior based on spatio-temporal dimensions. Second, for understanding these clusters, we propose to use HypTrails, a Bayesian approach for expressing and comparing hypotheses about human trails. To formalize hypotheses we utilize data that is publicly available on the Web, namely Foursquare data and census data provided by an open data platform. By applying this combination of approaches to taxi data in Manhattan, we can discover and explain different patterns in human mobility that cannot be identified in a collective analysis. As one example, we can find a group of taxi rides that end at locations with a high number of party venues (according to Foursquare) on weekend nights. Overall, our work demonstrates that human mobility is not one-dimensional but rather contains different facets both in time and space which we explain by utilizing online data. The findings of this paper argue for a more fine-grained analysis of human mobility in order to make more informed decisions for e.g., enhancing urban structures, tailored traffic control and location-based recommender systems.
Emergent Bartering Behaviour in Multi-Agent Reinforcement Learning
Authors: Michael Bradley Johanson, Edward Hughes, Finbarr Timbers, Joel Z. Leibo
Date: 2022-05-13 00:00:00
Tasks: Multi-agent Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Advances in artificial intelligence often stem from the development of new environments that abstract real-world situations into a form where research can be done conveniently. This paper contributes such an environment based on ideas inspired by elementary Microeconomics. Agents learn to produce resources in a spatially complex world, trade them with one another, and consume those that they prefer. We show that the emergent production, consumption, and pricing behaviors respond to environmental conditions in the directions predicted by supply and demand shifts in Microeconomics. We also demonstrate settings where the agents' emergent prices for goods vary over space, reflecting the local abundance of goods. After the price disparities emerge, some agents then discover a niche of transporting goods between regions with different prevailing prices -- a profitable strategy because they can buy goods where they are cheap and sell them where they are expensive. Finally, in a series of ablation experiments, we investigate how choices in the environmental rewards, bartering actions, agent architecture, and ability to consume tradable goods can either aid or inhibit the emergence of this economic behavior. This work is part of the environment development branch of a research program that aims to build human-like artificial general intelligence through multi-agent interactions in simulated societies. By exploring which environment features are needed for the basic phenomena of elementary microeconomics to emerge automatically from learning, we arrive at an environment that differs from those studied in prior multi-agent reinforcement learning work along several dimensions. For example, the model incorporates heterogeneous tastes and physical abilities, and agents negotiate with one another as a grounded form of communication.
Learning Keypoints from Synthetic Data for Robotic Cloth Folding
Authors: Thomas Lips, Victor-Louis De Gusseme, Francis wyffels
Date: 2022-05-13 00:00:00
Tasks: Keypoint Detection

Robotic cloth manipulation is challenging due to its deformability, which makes determining its full state infeasible. However, for cloth folding, it suffices to know the position of a few semantic keypoints. Convolutional neural networks (CNN) can be used to detect these keypoints, but require large amounts of annotated data, which is expensive to collect. To overcome this, we propose to learn these keypoint detectors purely from synthetic data, enabling low-cost data collection. In this paper, we procedurally generate images of towels and use them to train a CNN. We evaluate the performance of this detector for folding towels on a unimanual robot setup and find that the grasp and fold success rates are 77% and 53%, respectively. We conclude that learning keypoint detectors from synthetic data for cloth folding and related tasks is a promising research direction, discuss some failures and relate them to future work. A video of the system, as well as the codebase, more details on the CNN architecture and the training setup can be found at https://github.com/tlpss/workshop-icra-2022-cloth-keypoints.git.
Hyper-parameter tuning of physics-informed neural networks: Application to Helmholtz problems
Authors: Paul Escapil-Inchauspé, Gonzalo A. Ruz
Date: 2022-05-13 00:00:00
Tasks: Bayesian Optimization, Gaussian Processes

We consider physics-informed neural networks (PINNs) [Raissi et al., J.~Comput. Phys. 278 (2019) 686-707] for forward physical problems. In order to find optimal PINNs configuration, we introduce a hyper-parameter optimization (HPO) procedure via Gaussian processes-based Bayesian optimization. We apply the HPO to Helmholtz equation for bounded domains and conduct a thorough study, focusing on: (i) performance, (ii) the collocation points density $r$ and (iii) the frequency $\kappa$, confirming the applicability and necessity of the method. Numerical experiments are performed in two and three dimensions, including comparison to finite element methods.
MOPaC: The Multiple Offers Protocol for Multilateral Negotiations with Partial Consensus
Authors: Pradeep K. Murukannaiah, Catholijn M. Jonker
Date: 2022-05-13 00:00:00

Existing protocols for multilateral negotiation require a full consensus among the negotiating parties. In contrast, we propose a protocol for multilateral negotiation that allows partial consensus, wherein only a subset of the negotiating parties can reach an agreement. We motivate problems that require such a protocol and describe the protocol formally.
The Case for a Legal Compliance API for the Enforcement of the EU's Digital Services Act on Social Media Platforms
Authors: Catalina Goanta, Thales Bertaglia, Adriana Iamnitchi
Date: 2022-05-13 00:00:00

In the course of under a year, the European Commission has launched some of the most important regulatory proposals to date on platform governance. The Commission's goals behind cross-sectoral regulation of this sort include the protection of markets and democracies alike. While all these acts propose sophisticated rules for setting up new enforcement institutions and procedures, one aspect remains highly unclear: how digital enforcement will actually take place in practice. Focusing on the Digital Services Act (DSA), this discussion paper critically addresses issues around social media data access for the purpose of digital enforcement and proposes the use of a legal compliance application programming interface (API) as a means to facilitate compliance with the DSA and complementary European and national regulation. To contextualize this discussion, the paper pursues two scenarios that exemplify the harms arising out of content monetization affecting a particularly vulnerable category of social media users: children. The two scenarios are used to further reflect upon essential issues surrounding data access and legal compliance with the DSA and further applicable legal standards in the field of labour and consumer law.
Analyzing Hate Speech Data along Racial, Gender and Intersectional Axes
Authors: Antonis Maronikolakis, Philip Baader, Hinrich Schütze
Date: 2022-05-13 00:00:00
Proceeding: NAACL (GeBNLP) 2022 7

To tackle the rising phenomenon of hate speech, efforts have been made towards data curation and analysis. When it comes to analysis of bias, previous work has focused predominantly on race. In our work, we further investigate bias in hate speech datasets along racial, gender and intersectional axes. We identify strong bias against African American English (AAE), masculine and AAE+Masculine tweets, which are annotated as disproportionately more hateful and offensive than from other demographics. We provide evidence that BERT-based models propagate this bias and show that balancing the training data for these protected attributes can lead to fairer models with regards to gender, but not race.
The Devil is in the Details: On the Pitfalls of Vocabulary Selection in Neural Machine Translation
Authors: Tobias Domhan, Eva Hasler, Ke Tran, Sony Trenous, Bill Byrne, Felix Hieber
Date: 2022-05-13 00:00:00
Proceeding: NAACL 2022 7
Tasks: Machine Translation, Sentence, Translation

Vocabulary selection, or lexical shortlisting, is a well-known technique to improve latency of Neural Machine Translation models by constraining the set of allowed output words during inference. The chosen set is typically determined by separately trained alignment model parameters, independent of the source-sentence context at inference time. While vocabulary selection appears competitive with respect to automatic quality metrics in prior work, we show that it can fail to select the right set of output words, particularly for semantically non-compositional linguistic phenomena such as idiomatic expressions, leading to reduced translation quality as perceived by humans. Trading off latency for quality by increasing the size of the allowed set is often not an option in real-world scenarios. We propose a model of vocabulary selection, integrated into the neural translation model, that predicts the set of allowed output words from contextualized encoder representations. This restores translation quality of an unconstrained system, as measured by human evaluations on WMT newstest2020 and idiomatic expressions, at an inference latency competitive with alignment-based selection using aggressive thresholds, thereby removing the dependency on separately trained alignment models.
Improving Contextual Representation with Gloss Regularized Pre-training
Authors: Yu Lin, Zhecheng An, Peihao Wu, Zejun Ma
Date: 2022-05-13 00:00:00
Proceeding: Findings (NAACL) 2022 7
Tasks: Semantic Similarity, Semantic Textual Similarity, Sentence, STS, Word Similarity

Though achieving impressive results on many NLP tasks, the BERT-like masked language models (MLM) encounter the discrepancy between pre-training and inference. In light of this gap, we investigate the contextual representation of pre-training and inference from the perspective of word probability distribution. We discover that BERT risks neglecting the contextual word similarity in pre-training. To tackle this issue, we propose an auxiliary gloss regularizer module to BERT pre-training (GR-BERT), to enhance word semantic similarity. By predicting masked words and aligning contextual embeddings to corresponding glosses simultaneously, the word similarity can be explicitly modeled. We design two architectures for GR-BERT and evaluate our model in downstream tasks. Experimental results show that the gloss regularizer benefits BERT in word-level and sentence-level semantic representation. The GR-BERT achieves new state-of-the-art in lexical substitution task and greatly promotes BERT sentence representation in both unsupervised and supervised STS tasks.
Upside-Down Reinforcement Learning Can Diverge in Stochastic Environments With Episodic Resets
Authors: Miroslav Štrupl, Francesco Faccio, Dylan R. Ashley, Jürgen Schmidhuber, Rupesh Kumar Srivastava
Date: 2022-05-13 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning (RL)

Upside-Down Reinforcement Learning (UDRL) is an approach for solving RL problems that does not require value functions and uses only supervised learning, where the targets for given inputs in a dataset do not change over time. Ghosh et al. proved that Goal-Conditional Supervised Learning (GCSL) -- which can be viewed as a simplified version of UDRL -- optimizes a lower bound on goal-reaching performance. This raises expectations that such algorithms may enjoy guaranteed convergence to the optimal policy in arbitrary environments, similar to certain well-known traditional RL algorithms. Here we show that for a specific episodic UDRL algorithm (eUDRL, including GCSL), this is not the case, and give the causes of this limitation. To do so, we first introduce a helpful rewrite of eUDRL as a recursive policy update. This formulation helps to disprove its convergence to the optimal policy for a wide class of stochastic environments. Finally, we provide a concrete example of a very simple environment where eUDRL diverges. Since the primary aim of this paper is to present a negative result, and the best counterexamples are the simplest ones, we restrict all discussions to finite (discrete) environments, ignoring issues of function approximation and limited sample size.
Knowledge Graph Question Answering Datasets and Their Generalizability: Are They Enough for Future Research?
Authors: Longquan Jiang, Ricardo Usbeck
Date: 2022-05-13 00:00:00
Tasks: Graph Question Answering, Knowledge Graphs, Out-of-Distribution Generalization, Question Answering

Existing approaches on Question Answering over Knowledge Graphs (KGQA) have weak generalizability. That is often due to the standard i.i.d. assumption on the underlying dataset. Recently, three levels of generalization for KGQA were defined, namely i.i.d., compositional, zero-shot. We analyze 25 well-known KGQA datasets for 5 different Knowledge Graphs (KGs). We show that according to this definition many existing and online available KGQA datasets are either not suited to train a generalizable KGQA system or that the datasets are based on discontinued and out-dated KGs. Generating new datasets is a costly process and, thus, is not an alternative to smaller research groups and companies. In this work, we propose a mitigation method for re-splitting available KGQA datasets to enable their applicability to evaluate generalization, without any cost and manual effort. We test our hypothesis on three KGQA datasets, i.e., LC-QuAD, LC-QuAD 2.0 and QALD-9). Experiments on re-splitted KGQA datasets demonstrate its effectiveness towards generalizability. The code and a unified way to access 18 available datasets is online at https://github.com/semantic-systems/KGQA-datasets as well as https://github.com/semantic-systems/KGQA-datasets-generalization.
Convergence Analysis of Deep Residual Networks
Authors: Wentao Huang, Haizhang Zhang
Date: 2022-05-13 00:00:00
Tasks: Deep Learning

Various powerful deep neural network architectures have made great contribution to the exciting successes of deep learning in the past two decades. Among them, deep Residual Networks (ResNets) are of particular importance because they demonstrated great usefulness in computer vision by winning the first place in many deep learning competitions. Also, ResNets were the first class of neural networks in the development history of deep learning that are really deep. It is of mathematical interest and practical meaning to understand the convergence of deep ResNets. We aim at characterizing the convergence of deep ResNets as the depth tends to infinity in terms of the parameters of the networks. Toward this purpose, we first give a matrix-vector description of general deep neural networks with shortcut connections and formulate an explicit expression for the networks by using the notions of activation domains and activation matrices. The convergence is then reduced to the convergence of two series involving infinite products of non-square matrices. By studying the two series, we establish a sufficient condition for pointwise convergence of ResNets. Our result is able to give justification for the design of ResNets. We also conduct experiments on benchmark machine learning data to verify our results.