pwc-archive/papers-with-abstracts huggingface dataset:
ARGUABLY at ComMA@ICON: Detection of Multilingual Aggressive, Gender Biased, and Communally Charged Tweets Using Ensemble and Fine-Tuned IndicBERT
Authors: Guneet Kohli, Prabsimran Kaur, Jatin Bedi
Date: 2021-12-01 00:00:00
Proceeding: ICON 2021 12
Tasks: Hate Speech Detection, Multi-class Classification, Transliteration

The proliferation in Social Networking has increased offensive language, aggression, and hate-speech detection, which has drawn the focus of the NLP community. However, people’s difference in perception makes it difficult to distinguish between acceptable content and aggressive/hateful content, thus making it harder to create an automated system. In this paper, we propose multi-class classification techniques to identify aggressive and offensive language used online. Two main approaches have been developed for the classification of data into aggressive, gender-biased, and communally charged. The first approach is an ensemble-based model comprising of XG-Boost, LightGBM, and Naive Bayes applied on vectorized English data. The data used was obtained using an Indic Transliteration on the original data comprising of Meitei, Bangla, Hindi, and English language. The second approach is a BERT-based architecture used to detect misogyny and aggression. The proposed model employs IndicBERT Embeddings to define contextual understanding. The results of the models are validated on the ComMA v 0.2 dataset.
Sdutta at ComMA@ICON: A CNN-LSTM Model for Hate Detection
Authors: Sandip Dutta, Utso Majumder, Sudip Naskar
Date: 2021-12-01 00:00:00
Proceeding: ICON 2021 12
Tasks: Word Embeddings

In today’s world, online activity and social media are facing an upsurge of cases of aggression, gender-biased comments and communal hate. In this shared task, we used a CNN-LSTM hybrid method to detect aggression, misogynistic and communally charged content in social media texts. First, we employ text cleaning and convert the text into word embeddings. Next we proceed to our CNN-LSTM based model to predict the nature of the text. Our model achieves 0.288, 0.279, 0.294 and 0.335 Overall Micro F1 Scores in multilingual, Meitei, Bengali and Hindi datasets, respectively, on the 3 prediction labels.
MUCIC at ComMA@ICON: Multilingual Gender Biased and Communal Language Identification Using N-grams and Multilingual Sentence Encoders
Authors: Fazlourrahman Balouchzahi, Oxana Vitman, Hosahalli Lakshmaiah Shashirekha, Grigori Sidorov, Alexander Gelbukh
Date: 2021-12-01 00:00:00
Proceeding: ICON 2021 12
Tasks: Blocking, Language Identification, Multi Label Text Classification, Multi-Label Text Classification, Sentence, text-classification, Text Classification

Social media analytics are widely being explored by researchers for various applications. Prominent among them are identifying and blocking abusive contents especially targeting individuals and communities, for various reasons. The increasing abusive contents and the increasing number of users on social media demands automated tools to detect and filter the abusive contents as it is highly impossible to handle this manually. To address the challenges of detecting abusive contents, this paper describes the approaches proposed by our team MUCIC for Multilingual Gender Biased and Communal Language Identification shared task (ComMA@ICON) at International Conference on Natural Language Processing (ICON) 2021. This shared task dataset consists of code-mixed multi-script texts in Meitei, Bangla, Hindi as well as in Multilingual (a combination of Meitei, Bangla, Hindi, and English). The shared task is modeled as a multi-label Text Classification (TC) task combining word and char n-grams with vectors obtained from Multilingual Sentence Encoder (MSE) to train the Machine Learning (ML) classifiers using Pre-aggregation and Post-aggregation of labels. These approaches obtained the highest performance in the shared task for Meitei, Bangla, and Multilingual texts with instance-F1 scores of 0.350, 0.412, and 0.380 respectively using Pre-aggregation of labels.
MUM at ComMA@ICON: Multilingual Gender Biased and Communal Language Identification Using Supervised Learning Approaches
Authors: Asha Hegde, Mudoor Devadas Anusha, Sharal Coelho, Hosahalli Lakshmaiah Shashirekha
Date: 2021-12-01 00:00:00
Proceeding: ICON 2021 12
Tasks: Language Identification

Due to the rapid rise of social networks and micro-blogging websites, communication between people from different religion, caste, creed, cultural and psychological backgrounds has become more direct leading to the increase in cyber conflicts between people. This in turn has given rise to more and more hate speech and usage of abusive words to the point that it has become a serious problem creating negative impacts on the society. As a result, it is imperative to identify and filter such content on social media to prevent its further spread and the damage it is going to cause. Further, filtering such huge data requires automated tools since doing it manually is labor intensive and error prone. Added to this is the complex code-mixed and multi-scripted nature of social media text. To address the challenges of abusive content detection on social media, in this paper, we, team MUM, propose Machine Learning (ML) and Deep Learning (DL) models submitted to Multilingual Gender Biased and Communal Language Identification (ComMA@ICON) shared task at International Conference on Natural Language Processing (ICON) 2021. Word uni-grams, char n-grams, and emoji vectors are combined as features to train a ML Elastic-net regression model and multi-lingual Bidirectional Encoder Representations from Transformers (mBERT) is fine-tuned for a DL model. Out of the two, fine-tuned mBERT model performed better with an instance-F1 score of 0.326, 0.390, 0.343, 0.359 for Meitei, Bangla, Hindi, Multilingual texts respectively.
BFCAI at ComMA@ICON 2021: Support Vector Machines for Multilingual Gender Biased and Communal Language Identification
Authors: Fathy Elkazzaz, Fatma Sakr, Rasha Orban, Hamada Nayel
Date: 2021-12-01 00:00:00
Proceeding: ICON 2021 12
Tasks: Language Identification

This paper presents the system that has been submitted to the multilingual gender biased and communal language identification shared task by BFCAI team. The proposed model used Support Vector Machines (SVMs) as a classification algorithm. The features have been extracted using TF/IDF model with unigram and bigram. The proposed model is very simple and there are no external resources are needed to build the model.
Classifying Emotional Utterances by Employing Multi-modal Speech Emotion Recognition
Authors: Dipankar Das
Date: 2021-12-01 00:00:00
Proceeding: SMP (ICON) 2021 12
Tasks: Emotion Recognition, Gender Classification, Speech Emotion Recognition

Deep learning methods are being applied to several speech processing problems in recent years. In the present work, we have explored different deep learning models for speech emotion recognition. We have employed normal deep feedforward neural network (FFNN) and convolutional neural network (CNN) to classify audio files according to their emotional content. Comparative study indicates that CNN model outperforms FFNN in case of emotions as well as gender classification. It was observed that the sole audio based models can capture the emotions up to a certain limit. Thus, we attempted a multi-modal framework by combining the benefits of the audio and text features and employed them into a recurrent encoder. Finally, the audio and text encoders are merged to provide the desired impact on various datasets. In addition, a database consists of emotional utterances of several words has also been developed as a part of this work. It contains same word in different emotional utterances. Though the size of the database is not that large but this database is ideally supposed to contain all the English words that exist in an English dictionary.
Prosody Labelled Dataset for Hindi
Authors: Esha Banerjee, Atul Kr. Ojha, Girish Jha
Date: 2021-12-01 00:00:00
Proceeding: SMP (ICON) 2021 12
Tasks: Machine Translation, Translation

This study aims to develop an intonation labelled database for Hindi, for enhancing prosody in ASR and TTS systems, which is also helpful for building Speech to Speech Machine Translation systems. Although no single standard for prosody labelling exists in Hindi, researchers in the past have employed perceptual and statistical methods in literature to draw inferences about the behaviour of prosody patterns in Hindi. Based on such existing research and largely agreed upon intonational theories in Hindi, this study attempts to develop a manually annotated prosodic corpus of Hindi speech data, which can be used for training speech models for natural-sounding speech in the future. 500 sentences (2,550 words) for declarative and interrogative types have been labelled using Praat.
Multitask Learning based Deep Learning Model for Music Artist and Language Recognition
Authors: Yeshwant Singh, Anupam Biswas
Date: 2021-12-01 00:00:00
Proceeding: SMP (ICON) 2021 12
Tasks: Information Retrieval, Music Information Retrieval, Retrieval

Artist and music language recognitions of music recordings are crucial tasks in the music information retrieval domain. These tasks have many industrial applications and become much important with the advent of music streaming platforms. This work proposed a multitask learning-based deep learning model that leverages the shared latent representation between these two related tasks. Experimentally, we observe that applying multitask learning over a simple few blocks of a convolutional neural network-based model pays off with improvement in the performance. We conduct experiments on a regional music dataset curated for this task and released for others. Results show improvement up to 8.7 percent in AUC-PR, similar improvements observed in AUC-ROC.
Comparative Analysis of Melodia and Time-Domain Adaptive Filtering based Model for Melody Extraction from Polyphonic Music
Authors: Ranjeet Kumar, Anupam Biswas, Pinki Roy, Yeshwant Singh
Date: 2021-12-01 00:00:00
Proceeding: SMP (ICON) 2021 12
Tasks: Information Retrieval, Melody Extraction, Music Information Retrieval, Retrieval

Among the many applications of Music Information Retrieval (MIR), melody extraction is one of the most essential. It has risen to the top of the list of current research challenges in the field of MIR applications. We now need new means of defining, indexing, finding, and interacting with musical information, given the tremendous amount of music available at our fingertips. This article looked at some of the approaches that open the door to a broad variety of applications, such as automatically predicting the pitch sequence of a melody straight from the audio signal of a polyphonic music recording, commonly known as melody extraction. It is pretty easy for humans to identify the pitch of a melody, but doing so on an automated basis is very difficult and time-consuming. In this article, a comparison is made between the performance of the currently available melody extraction approach that is state-of-the-art Melodia and the technique based on time-domain adaptive filtering for melody extraction in terms of evaluation metrics introduced in MIREX 2005. Motivating by the same, this paper focuses on the discussion of datasets and state-of-the-art approaches for the extraction of the main melody from music signals. Additionally, a summary of the evaluation matrices based on which methodologies have been examined on various datasets is also present in this paper.
Dorabella Cipher as Musical Inspiration
Authors: Bradley Hauer, Colin Choi, Abram Hindle, Scott Smallwood, Grzegorz Kondrak
Date: 2021-12-01 00:00:00
Proceeding: SMP (ICON) 2021 12
Tasks: Decipherment, Position

The Dorabella cipher is an encrypted note of English composer Edward Elgar, which has defied decipherment attempts for more than a century. While most proposed solutions are English texts, we investigate the hypothe- sis that Dorabella represents enciphered music. We weigh the evidence in favor of and against the hypothesis, devise a simplified music nota- tion, and attempt to reconstruct a melody from the cipher. Our tools are n-gram models of mu- sic which we validate on existing music cor- pora enciphered using monoalphabetic substi- tution. By applying our methods to Dorabella, we produce a decipherment with musical qual- ities, which is then transformed via artful com- position into a listenable melody. Far from ar- guing that the end result represents the only true solution, we instead frame the process of decipherment as part of the composition pro- cess.
Developing Universal Dependencies Treebanks for Magahi and Braj
Authors: Mohit Raj, Shyam Ratan, Deepak Alok, Ritesh Kumar, Atul Kr. Ojha
Date: 2021-12-01 00:00:00
Proceeding: PAIL (ICON) 2021 12

In this paper, we discuss the development of treebanks for two low-resourced Indian languages - Magahi and Braj - based on the Universal Dependencies framework. The Magahi treebank contains 945 sentences and Braj treebank around 500 sentences marked with their lemmas, part-of-speech, morphological features and universal dependencies. This paper gives a description of the different dependency relationship found in the two languages and give some statistics of the two treebanks. The dataset will be made publicly available on Universal Dependency (UD) repository in the next (v2.10) release.
Parsing Subordinate Clauses in Telugu using Rule-based Dependency Parser
Authors: P Sangeetha, Parameswari Krishnamurthy, Amba Kulkarni
Date: 2021-12-01 00:00:00
Proceeding: PAIL (ICON) 2021 12

Parsing has been gaining popularity in recent years and attracted the interest of NLP researchers around the world. It is challenging when the language under study is a free-word order language that allows ellipsis like Telugu. In this paper, an attempt is made to parse subordinate clauses especially, non-finite verb clauses and relative clauses in Telugu which are highly productive and constitute a large chunk in parsing tasks. This study adopts a knowledge-driven approach to parse subordinate structures using linguistic cues as rules. Challenges faced in parsing ambiguous structures are elaborated alongside providing enhanced tags to handle them. Results are encouraging and this parser proves to be efficient for Telugu.
Dependency Parsing in a Morphological rich language, Tamil
Authors: Vijay Sundar Ram, Sobha Lalitha Devi
Date: 2021-12-01 00:00:00
Proceeding: PAIL (ICON) 2021 12
Tasks: Dependency Parsing, Sentence

Dependency parsing is the process of analysing the grammatical structure of a sentence based on the dependencies between the words in a sentence. The annotation of dependency parsing is done using different formalisms at word-level namely Universal Dependencies and chunk-level namely AnnaCorra. Though dependency parsing is deeply dealt in languages such as English, Czech etc the same cannot be adopted for the morphologically rich and agglutinative languages. In this paper, we discuss the development of a dependency parser for Tamil, a South Dravidian language. The different characteristics of the language make this task a challenging task. Tamil, a morphologically rich and agglutinative language, has copula drop, accusative and genitive case drop and pro-drop. Coordinative constructions are introduced by affixation of morpheme ‘um’. Embedded clausal structures are common in relative participle and complementizer clauses. In this paper, we have discussed our approach to handle some of these challenges. We have used Malt parser, a supervised learning- approach based implementation. We have obtained an accuracy of 79.27% for Unlabelled Attachment Score, 73.64% for Labelled Attachment Score and 68.82% for Labelled Accuracy.
Neural-based Tamil Grammar Error Detection
Authors: Dineskumar Murugesapillai, Anankan Ravinthirarasa, Gihan Dias, Kengatharaiyer Sarveswaran
Date: 2021-12-01 00:00:00
Proceeding: PAIL (ICON) 2021 12
Tasks: Multi-class Classification

This paper describes an ongoing development of a grammar error checker for the Tamil language using a state-of-the-art deep neural-based approach. This proposed checker capture a vital type of grammar error called subject-predicate agreement errors. In this case, we specifically target the agreement error that occurs between nominal subject and verbal predicates. We also created the first-ever grammar error annotated corpus for Tamil. In addition, we experimented with different multi-lingual pre-trained language models to capture syntactic information and found that IndicBERT gives better performance for our tasks. We implemented this grammar checker as a multi-class classification on top of the IndicBERT pre-trained model, which we fine-tuned using our annotated data. This baseline model gives an F1 Score of 73.4. We are now in the process of improving this proposed system with the use of a dependency parser.
In Search of a Robust Facial Expressions Recognition Model: A Large-Scale Visual Cross-Corpus Study
Authors: Elena Ryumina, Denis Dresvyanskiy, Alexey Karpov
Date: 2022-10-07 00:00:00
Proceeding: Neurocomputing 2022 10
Tasks: Cross-corpus, Emotion Recognition, Face Detection, Facial Expression Recognition (FER)

Many researchers have been seeking robust emotion recognition system for already last two decades. It would advance computer systems to a new level of interaction, providing much more natural feedback during human–computer interaction due to analysis of user affect state. However, one of the key problems in this domain is a lack of generalization ability: we observe dramatic degradation of model performance when it was trained on one corpus and evaluated on another one. Although some studies were done in this direction, visual modality still remains under-investigated. Therefore, we introduce the visual cross-corpus study conducted with the utilization of eight corpora, which differ in recording conditions, participants’ appearance characteristics, and complexity of data processing. We propose a visual-based end-to-end emotion recognition framework, which consists of the robust pre-trained backbone model and temporal sub-system in order to model temporal dependencies across many video frames. In addition, a detailed analysis of mistakes and advantages of the backbone model is provided, demonstrating its high ability of generalization. Our results show that the backbone model has achieved the accuracy of 66.4% on the AffectNet dataset, outperforming all the state-of-the-art results. Moreover, the CNN-LSTM model has demonstrated a decent efficacy on dynamic visual datasets during cross-corpus experiments, achieving comparable with state-of-the-art results. In addition, we provide backbone and CNN-LSTM models for future researchers: they can be accessed via GitHub.
Unifying paragraph embeddings and neural collaborative filtering for hybrid recommendation
Authors: Yihao Zhang a, Zhi Liu a, ∗, Chunyan Sang b
Date: 2020-01-20 00:00:00
Proceeding: 03/16 2020 1
Tasks: Collaborative Filtering

Collaborative filtering is one of widely used recommendation techniques. Despite the effectiveness
of matrix factorization for collaborative filtering; however, the inner product operator, combining the
multiplication of latent features linearly, may not be sufficient to capture the complex structure of user
interaction ratings. On the other hand, we argue that there is a great deviation between user ratings
and their real interest preference. In this paper, we propose a novel hybrid recommendation algorithm.
It adopts neural networks to exploit user–item ratings for collaborative filtering, which is endowed a
high level of non-linearity for capturing the complex structure of user interaction ratings. At the same
time, it exploits item embeddings to capture the content feature for auxiliary information, which solves
the cold start problem to some extent. In particular, we introduce paragraph embeddings to represent
user reviews and item descriptions, and design two neural networks to capture the sentiment of
user reviews and the content feature of items, respectively. And then, we treat these embeddings
as attention weights of users and items, and unify them with user–item ratings to model the hybrid
recommendation system. Extensive experiments on Amazon product dataset demonstrates that our
algorithm performs better on rating prediction than other state-of-the-art algorithms
WaveMix-Lite: A Resource-efficient Neural Network for Image Analysis
Authors: Jeevan, Pranav; Sethi, Amit
Date: 2022-10-13 00:00:00
Proceeding: ICLR 2022 10
Tasks: Efficient Neural Network, GPU, image-classification, Image Classification, Scene Classification, Semantic Segmentation

Gains in the ability to generalize on image analysis tasks for neural networks have come at the cost of increased number of parameters and layers, dataset sizes, training and test computations, and GPU RAM. We introduce a new architecture -- WaveMix-Lite -- that can generalize on par with contemporary transformers and convolutional neural networks (CNNs) while needing fewer resources. WaveMix-Lite uses 2D-discrete wavelet transform to efficiently mix spatial information from pixels. WaveMix-Lite seems to be a versatile and scalable architectural framework that can be used for multiple vision tasks, such as image classification and semantic segmentation, without requiring significant architectural changes, unlike transformers and CNNs. It is able to meet or exceed several accuracy benchmarks while training on a single GPU. For instance, it achieves state-of-the-art accuracy on five EMNIST datasets, outperforms CNNs and transformers in ImageNet-1K and Places-365, and achieves an mIoU of 77\% on Cityscapes validation set, while using less than one-fifth the number parameters and half the GPU RAM of comparable CNNs or transformers. Our experiments show that while the convolutional elements of neural architectures exploit the shift-invariance property of images, new types of layers (e.g., wavelet transform) can exploit additional properties of images, such as scale-invariance and finite spatial extents of objects.
A PyTorch Operations Based Approach for Computing Local Binary Patterns
Authors: Devrim Akgun
Date: 2021-11-26 00:00:00
Proceeding: U.Porto Journal of Engineering 2021 11

Advances in machine learning frameworks like PyTorch provides users with various machine learning algorithms together with general purpose operations. PyTorch framework provides Numpy like functions and makes it practical to use computational resources for accelerating computations. Also users may define their custom layers or operations for feature extraction algorithms based on the tensor operations. In this paper, Local Binary Patterns (LBP) which is one of the important feature extraction approaches in computer vision were realized using tensor operations of PyTorch framework. The algorithm was written both using Python code with standard libraries and tensor operations of PyTorch in Python. According to experimental measurements which were realized for various batches of images, the algorithm based on tensor operations considerably reduced the computation time and provides significant accelerations over Python implementation with standard libraries.