pwc-archive/papers-with-abstracts huggingface dataset:
End-to-End Automatic Speech Recognition for Gujarati
Authors: Deepang Raval, Vyom Pathak, Muktan Patel, Brijesh Bhatt
Date: 2020-12-01 00:00:00
Proceeding: ICON 2020 12
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Language Modeling, Language Modelling, speech-recognition, Speech Recognition

We present a novel approach for improving the performance of an End-to-End speech recognition system for the Gujarati language. We follow a deep learning based approach which includes Convolutional Neural Network (CNN), Bi-directional Long Short Term Memory (BiLSTM) layers, Dense layers, and Connectionist Temporal Classification (CTC) as a loss function. In order to improve the performance of the system with the limited size of the dataset, we present a combined language model (WLM and CLM) based prefix decoding technique and Bidirectional Encoder Representations from Transformers (BERT) based post-processing technique. To gain key insights from our Automatic Speech Recognition (ASR) system, we proposed different analysis methods. These insights help to understand our ASR system based on a particular language (Gujarati) as well as can govern ASR systems’ to improve the performance for low resource languages. We have trained the model on the Microsoft Speech Corpus, and we observe a 5.11% decrease in Word Error Rate (WER) with respect to base-model WER.
Deep Neural Model for Manipuri Multiword Named Entity Recognition with Unsupervised Cluster Feature
Authors: Jimmy Laishram, Kishorjit Nongmeikapam, Sudip Naskar
Date: 2020-12-01 00:00:00
Proceeding: ICON 2020 12
Tasks: Language Modelling, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), POS

The recognition task of Multi-Word Named Entities (MNEs) in itself is a challenging task when the language is inflectional and agglutinative. Having breakthrough NLP researches with deep neural network and language modelling techniques, the applicability of such techniques/algorithms for Indian language like Manipuri remains unanswered. In this paper an attempt to recognize Manipuri MNE is performed using a Long Short Term Memory (LSTM) recurrent neural network model in conjunction with Part Of Speech (POS) embeddings. To further improve the classification accuracy, word cluster information using K-means clustering approach is added as a feature embedding. The cluster information is generated using a Skip-gram based words vector that contains the semantic and syntactic information of each word. The model so proposed does not use extensive language morphological features to elevate its accuracy. Finally the model’s performance is compared with the other machine learning based Manipuri MNE models.
ScAA: A Dataset for Automated Short Answer Grading of Children’s free-text Answers in Hindi and Marathi
Authors: Dolly Agarwal, Somya Gupta, Nishant Baghel
Date: 2020-12-01 00:00:00
Proceeding: ICON 2020 12
Tasks: automatic short answer grading, Descriptive

Automatic short answer grading (ASAG) techniques are designed to automatically assess short answers written in natural language. Apart from MCQs, evaluating free text answer is essential to assess the knowledge and understanding of children in the subject. But assessing descriptive answers in low resource languages in a linguistically diverse country like India poses significant hurdles. To solve this assessment problem and advance NLP research in regional Indian languages, we present the Science Answer Assessment (ScAA) dataset of children’s answers in the age group of 8-14. ScAA dataset is a 2-way (correct/incorrect) labeled dataset and contains 10,988 and 1,955 pairs of natural answers along with model answers for Hindi and Marathi respectively for 32 questions. We benchmark various state-of-the-art ASAG methods, and show the data presents a strong challenge for future research.
Only text? only image? or both? Predicting sentiment of internet memes
Authors: Pranati Behera, Mamta ., Asif Ekbal
Date: 2020-12-01 00:00:00
Proceeding: ICON 2020 12
Tasks: Classification, Meme Classification, Sentiment Analysis, Sentiment Classification

Nowadays, the spread of Internet memes on online social media platforms such as Instagram, Facebook, Reddit, and Twitter is very fast. Analyzing the sentiment of memes can provide various useful insights. Meme sentiment classification is a new area of research that is not explored yet. Recently SemEval provides a dataset for meme sentiment classification. As this dataset is highly imbalanced, we extend this dataset by annotating new instances and use a sampling strategy to build a meme sentiment classifier. We propose a multi-modal framework for meme sentiment classification by utilizing textual and visual features of the meme. We found that for meme sentiment classification, only textual or only visual features are not sufficient. Our proposed framework utilizes textual as well as visual features together. We propose to use the attention mechanism to improve meme classification performance. Our proposed framework achieves macro F1 and accuracy of 34.23 and 50.02, respectively. It increases the accuracy by 6.77 and 7.86 compared to only textual and visual features, respectively.
Towards Bengali Word Embedding: Corpus Creation, Intrinsic and Extrinsic Evaluations
Authors: Md. Rajib Hossain, Mohammed Moshiul Hoque
Date: 2021-05-01 00:00:00
Proceeding: ICON: 17th International Conference on Natural Language Processing 2021 5
Tasks: Document Classification, Information Retrieval, Machine Translation, Question Answering, Retrieval, text-classification, Text Classification, Translation, Word Similarity

Distributional word vector representation or word embedding has become an essential ingredient in many natural language processing (NLP) tasks such as machine translation, document classification, information retrieval and question answering. Investigation of embedding model helps to reduce the feature space and improves textual semantic as well as syntactic relations. This paper presents three embedding techniques (such as Word2Vec, GloVe, and FastText) with different hyperparameters implemented on a Bengali corpus consists of 180 million words. The performance of the embedding techniques is evaluated with extrinsic and intrinsic ways. Extrinsic performance evaluated by text classification, which achieved a maximum of 96.48% accuracy. Intrinsic performance evaluated by word similarity (e.g., semantic, syntactic and relatedness) and analogy tasks. The maximum Pearson (rˆ) correlation accuracy of 60.66% (Ssrˆ) achieved for semantic similarities and 71.64% (Syrˆ) for syntactic similarities whereas the relatedness obtained 79.80% (Rsrˆ). The semantic word analogy tasks achieved 44.00% of accuracy while syntactic word analogy tasks obtained 36.00%.
Annotated Corpus of Tweets in English from Various Domains for Emotion Detection
Authors: Soumitra Ghosh, Asif Ekbal, Pushpak Bhattacharyya, Sriparna Saha, Vipin Tyagi, Alka Kumar, Shikha Srivastava, Nitish Kumar
Date: 2020-12-01 00:00:00
Proceeding: ICON 2020 12
Tasks: Emotion Recognition

Emotion recognition is a very well-attended problem in Natural Language Processing (NLP). Most of the existing works on emotion recognition focus on the general domain and in some cases to specific domains like fairy tales, blogs, weather, Twitter etc. But emotion analysis systems in the domains of security, social issues, technology, politics, sports, etc. are very rare. In this paper, we create a benchmark setup for emotion recognition in these specialised domains. First, we construct a corpus of 18,921 tweets in English annotated with Paul Ekman’s six basic emotions (Anger, Disgust, Fear, Happiness, Sadness, Surprise) and a non-emotive class Others. Thereafter, we propose a deep neural framework to perform emotion recognition in an end-to-end setting. We build various models based on Convolutional Neural Network (CNN), Bi-directional Long Short Term Memory (Bi-LSTM), Bi-directional Gated Recurrent Unit (Bi-GRU). We propose a Hierarchical Attention-based deep neural network for Emotion Detection (HAtED). We also develop multiple systems by considering different sets of emotion classes for each system and report the detailed comparative analysis of the results. Experiments show the hierarchical attention-based model achieves best results among the considered baselines with accuracy of 69%.
PhraseOut: A Code Mixed Data Augmentation Method for MultilingualNeural Machine Tranlsation
Authors: Binu Jasim, Vinay Namboodiri, C V Jawahar
Date: 2020-12-01 00:00:00
Proceeding: ICON 2020 12
Tasks: Data Augmentation, Machine Translation, NMT, Sentence, Translation

Data Augmentation methods for Neural Machine Translation (NMT) such as back- translation (BT) and self-training (ST) are quite popular. In a multilingual NMT system, simply copying monolingual source sentences to the target (Copying) is an effective data augmentation method. Back-translation aug- ments parallel data by translating monolingual sentences in the target side to source language. In this work we propose to use a partial back- translation method in a multilingual setting. Instead of translating the entire monolingual target sentence back into the source language, we replace selected high confidence phrases only and keep the rest of the words in the target language itself. (We call this method PhraseOut). Our experiments on low resource multilingual translation models show that PhraseOut gives reasonable improvements over the existing data augmentation methods.
CLPLM: Character Level Pretrained Language Model for ExtractingSupport Phrases for Sentiment Labels
Authors: Raj Pranesh, Sumit Kumar, Ambesh Shekhar
Date: 2020-12-01 00:00:00
Proceeding: ICON 2020 12
Tasks: Language Modeling, Language Modelling

In this paper, we have designed a character-level pre-trained language model for extracting support phrases from tweets based on the sentiment label. We also propose a character-level ensemble model designed by properly blending Pre-trained Contextual Embeddings (PCE) models- RoBERTa, BERT, and ALBERT along with Neural network models- RNN, CNN and WaveNet at different stages of the model. For a given tweet and associated sentiment label, our model predicts the span of phrases in a tweet that prompts the particular sentiment in the tweet. In our experiments, we have explored various model architectures and configuration for both single as well as ensemble models. We performed a systematic comparative analysis of all the model’s performance based on the Jaccard score obtained. The best performing ensemble model obtained the highest Jaccard scores of 73.5, giving it a relative improvement of 2.4% over the best performing single RoBERTa based character-level model, at 71.5(Jaccard score).
Developing a Faroese PoS-tagging solution using Icelandic methods
Authors: Hinrik Hafsteinsson, Anton Karl Ingason
Date: 2020-12-01 00:00:00
Proceeding: ICON 2020 12
Tasks: Part-Of-Speech Tagging, POS, POS Tagging

We describe the development of a dedicated, high-accuracy part-of-speech (PoS) tagging solution for Faroese, a North Germanic language with about 50,000 speakers. To achieve this, a state-of-the-art neural PoS tagger for Icelandic, ABLTagger, was trained on a 100,000 word PoS-tagged corpus for Faroese, standardised with methods previously applied to Icelandic corpora. This tagger was supplemented with a novel Experimental Database of Faroese Inflection (EDFM), which contains morphological information on 67,488 Faroese words with about one million inflectional forms. This approach produced a PoS-tagging model for Faroese which achieves a 91.40% overall accuracy when evaluated with 10-fold cross validation, which is currently the highest reported accuracy for a dedicated Faroese PoS-tagger. The tagging model, morphological database, proposed revised PoS tagset for Faroese as well as a revised and standardised PoS tagged corpus are all presented as products of this project and are made available for use in further research in Faroese language technology
MUCS@TechDOfication using FineTuned Vectors and n-grams
Authors: Fazlourrahman Balouchzahi, M D Anusha, H L Shashirekha
Date: 2020-12-01 00:00:00
Proceeding: ICON 2020 12
Tasks: Machine Translation, Question Answering, text-classification, Text Classification

The increase in domain specific text processing applications are demanding tools and techniques for domain specific Text Classification (TC) which may be helpful in many downstream applications like Machine Translation, Summarization, Question Answering etc. Further, many TC algorithms are applied on globally recognized languages like English giving less importance for local languages particularly Indian languages. To boost the research for technical domains and text processing activities in Indian languages, a shared task named ”TechDOfication2020” is organized by ICON’20. The objective of this shared task is to automatically identify the technical domain of a given text which provides information about coarse grained technical domains and fine grained subdomains in eight languages. To tackle this challenge we, team MUCS have proposed three models, namely, DL-FineTuned model applied for all subtasks, and VC-FineTuned and VC-ngrams models applied only for some subtasks. n-grams and word embedding with a step of fine-tuning are used as features and machine learning and deep learning algorithms are used as classifiers in the proposed models. The proposed models outperformed in most of subtasks and also obtained first rank in subTask1b (Bangla) and subTask1e (Malayalam) with f1 score of 0.8353 and 0.3851 respectively using DL-FineTuned model for both the subtasks.
A Graph Convolution Network-based System for Technical Domain Identification
Authors: Alapan Kuila, Ayan Das, Sudeshna Sarkar
Date: 2020-12-01 00:00:00
Proceeding: ICON 2020 12
Tasks: Classification, domain classification, POS, TAG

This paper presents the IITKGP contribution at the Technical DOmain Identification (TechDOfication) shared task at ICON 2020. In the preprocessing stage, we applied part-of-speech (PoS) taggers and dependency parsers to tag the data. We trained a graph convolution neural network (GCNN) based system that uses the tokens along with their PoS and dependency relations as features to identify the domain of a given document. We participated in the subtasks for coarse-grained domain classification in the English (Subtask 1a), Bengali (Subtask 1b) and Hindi language (Subtask 1d), and, the subtask for fine-grained domain classification task within Computer Science domain in English language (Subtask 2a).
Multichannel LSTM-CNN for Telugu Text Classification
Authors: Sunil Gundapu, Radhika Mamidi
Date: 2020-12-01 00:00:00
Proceeding: ICON 2020 12
Tasks: Classification, Information Retrieval, Machine Translation, Question Answering, Retrieval, Sentiment Analysis, text-classification, Text Classification, Text Summarization, Translation

With the instantaneous growth of text information, retrieving domain-oriented information from the text data has a broad range of applications in Information Retrieval and Natural language Processing. Thematic keywords give a compressed representation of the text. Usually, Domain Identification plays a significant role in Machine Translation, Text Summarization, Question Answering, Information Extraction, and Sentiment Analysis. In this paper, we proposed the Multichannel LSTM-CNN methodology for Technical Domain Identification for Telugu. This architecture was used and evaluated in the context of the ICON shared task “TechDOfication 2020” (task h), and our system got 69.9% of the F1 score on the test dataset and 90.01% on the validation set.
Technical Domain Identification using word2vec and BiLSTM
Authors: Koyel Ghosh, Dr. Apurbalal Senapati, Dr. Ranjan Maity
Date: 2020-12-01 00:00:00
Proceeding: ICON 2020 12
Tasks: Classification, domain classification, Emotion Recognition, Management, Math, Sentiment Analysis

Coarse-grained and Fine-grained classification tasks are mostly based on sentiment or basic emotion analysis. Now, switching from emotion and sentiment analysis to another domain, in this paper, we are going to work on technical domain identification. The task is to identify the technical domain of a given English text. In the case of Coarse-grained domain classification, such a piece of text provides information about specific Coarse-grained technical domains like Computer Science, Physics, Math, etc, and in Fine-grained domain classification, Fine-grained subdomains for Computer science domain, it can be like Artificial Intelligence, Algorithm, Computer Architecture, Computer Networks, Database Management system, etc. To do the task, Word2Vec skip-gram model is used for word embedding, later, applied the Bidirectional Long Short Term memory (BiLSTM) model to classify Coarse-grained domains and Fine-grained sub-domains. To evaluate the performance of the approached model accuracy, precision, recall, and F1-score have been applied.
Automatic Technical Domain Identification
Authors: Hema Ala, Dipti Sharma
Date: 2020-12-01 00:00:00
Proceeding: ICON 2020 12
Tasks: BIG-bench Machine Learning

In this paper we present two Machine Learning algorithms namely Stochastic Gradient Descent and Multi Layer Perceptron to Identify the technical domain of given text as such text provides information about the specific domain. We performed our experiments on Coarse-grained technical domains like Computer Science, Physics, Law, etc for English, Bengali, Gujarati, Hindi, Malayalam, Marathi, Tamil, and Telugu languages, and on fine-grained sub domains for Computer Science like Operating System, Computer Network, Database etc for only English language. Using TFIDF as a feature extraction method we show how both the machine learning models perform on the mentioned languages.
Fine-grained domain classification using Transformers
Authors: Akshat Gahoi, Akshat Chhajer, Dipti Mishra Sharma
Date: 2020-12-01 00:00:00
Proceeding: ICON 2020 12
Tasks: Classification, domain classification, text-classification, Text Classification, Transfer Learning

The introduction of transformers in 2017 and successively BERT in 2018 brought about a revolution in the field of natural language processing. Such models are pretrained on vast amounts of data, and are easily extensible to be used for a wide variety of tasks through transfer learning. Continual work on transformer based architectures has led to a variety of new models with state of the art results. RoBERTa(CITATION) is one such model, which brings about a series of changes to the BERT architecture and is capable of producing better quality embeddings at an expense of functionality. In this paper, we attempt to solve the well known text classification task of fine-grained domain classification using BERT and RoBERTa and perform a comparative analysis of the same. We also attempt to evaluate the impact of data preprocessing specially in the context of fine-grained domain classification. The results obtained outperformed all the other models at the ICON TechDOfication 2020 (subtask-2a) Fine-grained domain classification task and ranked first. This proves the effectiveness of our approach.
Graph Based Automatic Domain Term Extraction
Authors: Hema Ala, Dipti Sharma
Date: 2020-12-01 00:00:00
Proceeding: ICON 2020 12
Tasks: Term Extraction

We present a Graph Based Approach to automatically extract domain specific terms from technical domains like Biochemistry, Communication, Computer Science and Law. Our approach is similar to TextRank with an extra post-processing step to reduce the noise. We performed our experiments on the mentioned domains provided by ICON TermTraction - 2020 shared task. Presented precision, recall and f1-score for all experiments. Further, it is observed that our method gives promising results without much noise in domain terms.
N-Grams TextRank A Novel Domain Keyword Extraction Technique
Authors: Saransh Rajput, Akshat Gahoi, Manvith Reddy, Dipti Mishra Sharma
Date: 2020-12-01 00:00:00
Proceeding: ICON 2020 12
Tasks: Keyword Extraction, Management

The rapid growth of the internet has given us a wealth of information and data spread across the web. However, as the data begins to grow we simultaneously face the grave problem of an Information Explosion. An abundance of data can lead to large scale data management problems as well as the loss of the true meaning of the data. In this paper, we present an advanced domain specific keyword extraction algorithm in order to tackle this problem of paramount importance. Our algorithm is based on a modified version of TextRank algorithm - an algorithm based on PageRank to successfully determine the keywords from a domain specific document. Furthermore, this paper proposes a modification to the traditional TextRank algorithm that takes into account bigrams and trigrams and returns results with an extremely high precision. We observe how the precision and f1-score of this model outperforms other models in many domains and the recall can be easily increased by increasing the number of results without affecting the precision. We also discuss about the future work of extending the same algorithm to Indian languages.
JUNLP@ICON2020: Low Resourced Machine Translation for Indic Languages
Authors: Sainik Mahata, Dipankar Das, Sivaji Bandyopadhyay
Date: 2020-12-01 00:00:00
Proceeding: ICON 2020 12
Tasks: Machine Translation, Translation

In the current work, we present the description of the systems submitted to a machine translation shared task organized by ICON 2020: 17th International Conference on Natural Language Processing. The systems were developed to show the capability of general domain machine translation when translating into Indic languages, English-Hindi, in our case. The paper shows the training process and quantifies the performance of two state-of-the-art translation systems, viz., Statistical Machine Translation and Neural Machine Translation. While Statistical Machine Translation systems work better in a low-resource setting, Neural Machine Translation systems are able to generate sentences that are fluent in nature. Since both these systems have contrasting advantages, a hybrid system, incorporating both, was also developed to leverage all the strong points. The submitted systems garnered BLEU scores of 8.701943312, 0.6361336198, and 11.78873307 respectively and the scores of the hybrid system helped us to the fourth spot in the competition leaderboard.
AdapNMT : Neural Machine Translation with Technical Domain Adaptation for Indic Languages
Authors: Hema Ala, Dipti Sharma
Date: 2020-12-01 00:00:00
Proceeding: ICON 2020 12
Tasks: Domain Adaptation, Machine Translation, NMT, Translation

Adapting new domain is highly challenging task for Neural Machine Translation (NMT). In this paper we show the capability of general domain machine translation when translating into Indic languages (English - Hindi , English - Telugu and Hindi - Telugu), and low resource domain adaptation of MT systems using existing general parallel data and small in domain parallel data for AI and Chemistry Domains. We carried out our experiments using Byte Pair Encoding(BPE) as it solves rare word problems. It has been observed that with addition of little amount of in-domain data to the general data improves the BLEU score significantly.