pwc-archive/papers-with-abstracts huggingface dataset:
Sentimental Poetry Generation
Authors: Kasper Aalberg Røstvold, Björn Gambäck
Date: 2020-12-01 00:00:00
Proceeding: ICON 2020 12

The paper investigates how well poetry can be generated to contain a specific sentiment, and whether readers of the poetry experience the intended sentiment. The poetry generator consists of a bi-directional Long Short-Term Memory (LSTM) model, combined with rhyme pair generation, rule-based word prediction methods, and tree search for extending generation possibilities. The LSTM network was trained on a set of English poetry written and published by users on a public website. Human judges evaluated poems generated by the system, both with a positive and negative sentiment. The results indicate that while there are some weaknesses in the system compared to other state-of-the-art solutions, it is fully capable of generating poetry with an inherent sentiment that is perceived by readers.
WEKA in Forensic Authorship Analysis: A corpus-based approach of Saudi Authors
Authors: Mashael AlAmr, Eric Atwell
Date: 2020-12-01 00:00:00
Proceeding: ICON 2020 12

This is a pilot study that aims to explore the potential of using WEKA in forensic authorship analysis. It is a corpus-based research using data from Twitter collected from thirteen authors from Riyadh, Saudi Arabia. It examines the performance of unbalanced and balanced data sets using different classifiers and parameters of word grams. The attributes are dialect-specific linguistic features categorized as word grams. The findings further support previous studies in computational authorship identification.
Native-Language Identification with Attention
Authors: Stian Steinbakken, Björn Gambäck
Date: 2020-12-01 00:00:00
Proceeding: ICON 2020 12
Tasks: Language Identification, Native Language Identification

The paper explores how an attention-based approach can increase performance on the task of native-language identification (NLI), i.e., to identify an author’s first language given information expressed in a second language. Previously, Support Vector Machines have consistently outperformed deep learning-based methods on the TOEFL11 data set, the de facto standard for evaluating NLI systems. The attention-based system BERT (Bidirectional Encoder Representations from Transformers) was first tested in isolation on the TOEFL11 data set, then used in a meta-classifier stack in combination with traditional techniques to produce an accuracy of 0.853. However, more labelled NLI data is now available, so BERT was also trained on the much larger Reddit-L2 data set, containing 50 times as many examples as previously used for English NLI, giving an accuracy of 0.902 on the Reddit-L2 in-domain test scenario, improving the state-of-the-art by 21.2 percentage points.
Efforts Towards Developing a Tamang Nepali Machine Translation System
Authors: Binaya Kumar Chaudhary, Bal Krishna Bal, Rasil Baidar
Date: 2020-12-01 00:00:00
Proceeding: ICON 2020 12
Tasks: Machine Translation, NMT, Sentence, Translation

The Tamang language is spoken mainly in Nepal, Sikkim, West Bengal, some parts of Assam, and the North East region of India. As per the 2011 census conducted by the Nepal Government, there are about 1.35 million Tamang speakers in Nepal itself. In this regard, a Machine Translation System for Tamang-Nepali language pair is significant both from research and practical outcomes in terms of enabling communication between the Tamang and the Nepali communities. In this work, we train the Transformer Neural Machine Translation (NMT) architecture with attention using a small hand-labeled or aligned Tamang-Nepali corpus (15K sentence pairs). Our preliminary results show BLEU scores of 27.74 for the Nepali→Tamang direction and 23.74 in the Tamang→Nepali direction. We are currently working on increasing the datasets as well as improving the model to obtain better BLEU scores. We also plan to extend the work to add the English language to the model, thus making it a trilingual Machine Translation System for Tamang-Nepali-English languages.
Claim extraction from text using transfer learning.
Authors: Acharya Ashish Prabhakar, Salar Mohtaj, Sebastian Möller
Date: 2020-12-01 00:00:00
Proceeding: ICON 2020 12
Tasks: Fake News Detection, Transfer Learning

Building an end to end fake news detection system consists of detecting claims in text and later verifying them for their authenticity. Although most of the recent works have focused on political claims, fake news can also be propagated in the form of religious intolerance, conspiracy theories etc. Since there is a lack of training data specific to all these scenarios, we compiled a homogeneous and balanced dataset by combining some of the currently available data. Moreover, it is shown in the paper that how recent advancements in transfer learning can be leveraged to detect claims, in general. The obtained result shows that the recently developed transformers can transfer the tendency of research from claim detection to the problem of check worthiness of claims in domains of interest.
Assamese Word Sense Disambiguation using Genetic Algorithm
Authors: Arjun Gogoi, Nomi Baruah, Shikhar Kr. Sarma
Date: 2020-12-01 00:00:00
Proceeding: ICON 2020 12
Tasks: Word Sense Disambiguation

Word sense disambiguation (WSD) is a problem to determine a word according to a context in which it occurs. There are plenty amount of works done in WSD for some languages such as English, but research work on Assamese WSD remains limited. It is a more exigent task because Assamese has an intrinsic complexity in its writing structure and ambiguity, such as syntactic, semantic, and anaphoric ambiguity levels.A novel unsupervised genetic word sense disambiguation algorithm is proposed in this paper. The algorithm first uses WordNet to extract all possible senses for a given ambiguous word, then a genetic algorithm is used taking Wu-Palmer’s similarity measure as the fitness function and calculating the similarity measure for all extracted senses. The winner sense which will have the highest score declared as he winner sense.
Free Word Order in Sanskrit and Well-nestedness
Authors: Sanal Vikram, Amba Kulkarni
Date: 2020-12-01 00:00:00
Proceeding: ICON 2020 12

The common wisdom about Sanskrit is that it is free word order language. This word order poses challenges such as handling non-projectivity in parsing. The earlier works on the word order of Sanskrit have shown that there are syntactic structures in Sanskrit which cannot be covered under even the non-planarity. In this paper, we study these structures further to investigate if they can fall under well-nestedness or not. A small manually tagged corpus of the verses of Śrīmad-Bhagavad-Gītā was considered for this study. It was noticed that there are as many well-nested trees as there are ill-nested ones. From the linguistic point of view, we could get a list of relations that are involved in the planarity violations. All these relations had one thing in common - that they have unilateral expectancy. It was this loose binding, as against the mutual expectancy with certain other relations, that allowed them to cross the phrasal boundaries.
A Multi-modal Personality Prediction System
Authors: Chanchal Suman, Aditya Gupta, Sriparna Saha, Pushpak Bhattacharyya
Date: 2020-12-01 00:00:00
Proceeding: ICON 2020 12
Tasks: Personality Trait Recognition by Face, Prediction, Recommendation Systems

Automatic prediction of personality traits has many real-life applications, e.g., in forensics, recommender systems, personalized services etc.. In this work, we have proposed a solution framework for solving the problem of predicting the personality traits of a user from videos. Ambient, facial and the audio features are extracted from the video of the user. These features are used for the final output prediction. The visual and audio modalities are combined in two different ways: averaging of predictions obtained from the individual modalities, and concatenation of features in multi-modal setting. The dataset released in Chalearn-16 is used for evaluating the performance of the system. Experimental results illustrate that it is possible to obtain better performance with a hand full of images, rather than using all the images present in the video
D-Coref: A Fast and Lightweight Coreference Resolution Model using DistilBERT
Authors: Chanchal Suman, Jeetu Kumar, Sriparna Saha, Pushpak Bhattacharyya
Date: 2020-12-01 00:00:00
Proceeding: ICON 2020 12
Tasks: coreference-resolution, Coreference Resolution

Smart devices are often deployed in some edge-devices, which require quality solutions in limited amount of memory usage. In most of the user-interaction based smart devices, coreference resolution is often required. Keeping this in view, we have developed a fast and lightweight coreference resolution model which meets the minimum memory requirement and converges faster. In order to generate the embeddings for solving the task of coreference resolution, DistilBERT, a light weight BERT module is utilized. DistilBERT consumes less memory (only 60% of memory in comparison to BERT-based heavy model) and it is suitable for deployment in edge devices. DistilBERT embedding helps in 60% faster convergence with an accuracy compromise of 2.59%, and 6.49% with respect to its base model and current state-of-the-art, respectively.
Semantic Slot Prediction on low corpus data using finite user defined list
Authors: Bharatram Natarajan, Dharani Simma, Chirag Singh, Anish Nediyanchath, Sreoshi Sengupta
Date: 2020-12-01 00:00:00
Proceeding: ICON 2020 12
Tasks: Decoder, Natural Language Understanding

Semantic slot prediction is one of the important task for natural language understanding (NLU). They depend on the quality and quantity of the human crafted training data, which affects model generalization. With the advent of voice assistants exposing AI platforms to third party developers, training data quality and quantity matters for any machine learning algorithm to learn and generalize properly.AI platforms provides provision to add custom external plist defined by the developers for the training data. Hence we are exploring dataset, called LowCorpusSlotData, containing low corpus training data with larger number of slots and significant test data. We also use external plist for the above dataset to aid in slot identification. We experimented using state of the art architectures like Bi-directional Encoder Representations from Transformers (BERT) with variants and Bi-directional Encoder with Custom Decoder. To address the low corpus problem, we propose a pipeline approach where we extract candidate slot information using the external plist extractor module and feed as input along with utterance.
Leveraging Latent Representations of Speech for Indian Language Identification
Authors: Samarjit Karmakar, P Radha Krishna
Date: 2020-12-01 00:00:00
Proceeding: ICON 2020 12
Tasks: Diversity, Feature Engineering, Language Identification, Representation Learning

Identification of the language spoken from speech utterances is an interesting task because of the diversity associated with different languages and human voices. Indian languages have diverse origins and identifying them from speech utterances would help several language recognition, translation and relationship mining tasks. The current approaches for tackling the problem of languages identification in the Indian context heavily use feature engineering and classical speech processing techniques. This is a bottleneck for language identification systems, as we require to exploit necessary features in speech, required for machine identification, which are learnt by a probabilistic framework, rather than handcrafted feature engineering. In this paper, we tackle the problem of language identification using latent representations learnt from speech using Variational Autoencoders (VAEs) and leverage the representations learnt to train sequence models. Our framework attains an accuracy of 89% in the identification of 8 well known Indian languages (namely Tamil, Telugu, Punjabi, Marathi, Gujarati, Hindi, Kannada and Bengali) from the CMU Indic Speech Database. The presented approach can be applied to several scenarios for speech processing by employing representation learning and leveraging them for sequence models.
Acoustic Analysis of Native (L1) Bengali Speakers’ Phonological Realization of English Lexical Stress Contrast
Authors: Shambhu Nath Saha, Shyamal Kr. Das Mandal
Date: 2020-12-01 00:00:00
Proceeding: ICON 2020 12
Tasks: Sentence

Acoustically, English lexical stress is multidimensional and involving manipulation of duration, intensity, fundamental frequency (F0) and vowel quality. The current study investigates the acquisition of English lexical stress by L1 Bengali speakers at the phonological level in terms of the properties of acoustic cues. For this purpose, this study compares 20 L1 Bengali speakers’ use of acoustic correlates for the production of English lexical stress in context sentence and neutral frame sentence. The result of this study showed that L1 Bengali speakers were not able to achieve neutral frame sentence like control over duration, intensity, F0 and to a limited extent vowel quality in context sentence. As a result, unlike neutral frame sentence, L1 Bengali speakers were not sensitive to English lexical stress contrast in context sentence. This analysis reveals that, the difference between the neutral frame and context sentences in terms of L1 Bengali speakers’ realization of phonology of English lexical stress contrast was probably due to the influence of Bengali phonology of lexical stress placement (restricted to the initial syllable of a word) on L1 Bengali speakers’ English speech.
Towards Performance Improvement in Indian Sign Language Recognition
Authors: Kinjal Mistree, Devendra Thakor, Brijesh Bhatt
Date: 2020-12-01 00:00:00
Proceeding: ICON 2020 12
Tasks: Image Augmentation, Image Manipulation, Sign Language Recognition

Sign language is a complete natural language used by deaf and dumb people. It has its own grammar and it differs with spoken language to a great extent. Since people without hearing and speech impairment lack the knowledge of the sign language, the deaf and dumb people find it difficult to communicate with them. The conception of system that would be able to translate the sign language into text would facilitate understanding of sign language without human interpreter. This paper describes a systematic approach that takes Indian Sign Language (ISL) video as input and converts it into text using frame sequence generator and image augmentation techniques. By incorporating these two concepts, we have increased dataset size and reduced overfitting. It is demonstrated that using simple image manipulation techniques and batch of shifted frames of videos, performance of sign language recognition can be significantly improved. Approach described in this paper achieves 99.57% accuracy on the dynamic gesture dataset of ISL.
Question and Answer pair generation for Telugu short stories
Authors: Meghana Bommadi, Shreya Terupally, Radhika Mamidi
Date: 2020-12-01 00:00:00
Proceeding: ICON 2020 12
Tasks: Hybrid Machine Learning, POS, Question Answering, Question Generation, Question-Generation

Question Answer pair generation is a task that has been worked upon by multiple researchers in many languages. It has been a topic of interest due to its extensive uses in different fields like self assessment, academics, business website FAQs etc. Many experiments were conducted on Question Answering pair generation in English, concentrating on basic Wh-questions with a rule-based approach. We have built the first hybrid machine learning and rule-based solution in Telugu which is efficient for short stories or short passages in children’s books. Our work covers the fundamental question forms with the question types: adjective, yes/no, adverb, verb, when, where, whose, quotative, and quantitative(how many/ how much). We constructed rules for question generation using POS tags and UD tags along with linguistic information of the surrounding context of the word.
Detection of Similar Languages and Dialects Using Deep Supervised Autoencoder
Authors: Shantipriya Parida, Esau Villatoro-Tello, Sajit Kumar, Maël Fabien, Petr Motlicek
Date: 2020-12-01 00:00:00
Proceeding: ICON 2020 12
Tasks: Language Identification

Language detection is considered a difficult task especially for similar languages, varieties, and dialects. With the growing number of online content in different languages, the need for reliable and robust language detection tools also increased. In this work, we use supervised autoencoders with a bayesian optimizer for language detection and highlights its efficiency in detecting similar languages with dialect variance in comparison to other state-of-the-art techniques. We evaluated our approach on multiple datasets (Ling10, Discriminating between Similar Language (DSL), and Indo-Aryan Language Identification (ILI)). Obtained results demonstrate that SAE are higly effective in detecting languages, up to a 100% accuracy in the Ling10. Similarly, we obtain a competitive performance in identifying similar languages, and dialects, 92% and 85% for DSL ans ILI datasets respectively.
Weak Supervision using Linguistic Knowledge for Information Extraction
Authors: Sachin Pawar, Girish Palshikar, Ankita Jain, Jyoti Bhat, Simi Johnson
Date: 2020-12-01 00:00:00
Proceeding: ICON 2020 12

In this paper, we propose to use linguistic knowledge to automatically augment a small manually annotated corpus to obtain a large annotated corpus for training Information Extraction models. We propose a powerful patterns specification language for specifying linguistic rules for entity extraction. We define an Enriched Text Format (ETF) to represent rich linguistic information about a text in the form of XML-like tags. The patterns in our patterns specification language are then matched on the ETF text rather than raw text to extract various entity mentions. We demonstrate how an entity extraction system can be quickly built for a domain-specific entity type for which there are no readily available annotated datasets.
Leveraging Alignment and Phonology for low-resource Indic to English Neural Machine Transliteration
Authors: Parth Patel, Manthan Mehta, Pushpak Bhattacharya, Arjun Atreya
Date: 2020-12-01 00:00:00
Proceeding: ICON 2020 12
Tasks: Transliteration

In this paper we present a novel transliteration technique based on Orthographic Syllable(OS) segmentation for low-resource Indian languages (ILs). Given that alignment has produced promising results in Statistical Machine Transliteration systems and phonology plays an important role in transliteration, we introduce a new model which uses alignment representation similar to that of IBM model 3 to pre-process the tokenized input sequence and then use pre-trained source and target OS-embeddings for training. We apply our model for transliteration from ILs to English and report our accuracy based on Top-1 Exact Match. We also compare our accuracy with a previously proposed Phrase-Based model and report improvements.
STHAL: Location-mention Identification in Tweets of Indian-context
Authors: Kartik Verma, Shobhit Sinha, Md. Shad Akhtar, Vikram Goyal
Date: 2020-12-01 00:00:00
Proceeding: ICON 2020 12

We investigate the problem of extracting Indian-locations from a given crowd-sourced textual dataset. The problem of extracting fine-grained Indian-locations has many challenges. One challenge in the task is to collect relevant dataset from the crowd-sourced platforms that contain locations. The second challenge lies in extracting the location entities from the collected data. We provide an in-depth review of the information collection process and our annotation guidelines such that a reliable dataset annotation is guaranteed. We evaluate many recent algorithms and models, including Conditional Random fields (CRF), Bi-LSTM-CNN and BERT (Bidirectional Encoder Representations from Transformers), on our developed dataset named . The study shows the best F1-score of 72.49% for BERT, followed by Bi-LSTM-CNN and CRF. As a result of our work, we prepare a publicly-available annotated dataset of Indian geolocations that can be used by the research community. Code and dataset are available at https://github.com/vkartik2k/STHAL.
On-Device detection of sentence completion for voice assistants with low-memory footprint
Authors: Rahul Kumar, Vijeta Gour, Chandan Pandey, Godawari Sudhakar Rao, Priyadarshini Pai, Anmol Bhasin, Ranjan Samal
Date: 2020-12-01 00:00:00
Proceeding: ICON 2020 12
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Boundary Detection, Sentence, Sentence Completion, speech-recognition, Speech Recognition

Sentence completion detection (SCD) is an important task for various downstream Natural Language Processing (NLP) based applications. For NLP based applications, which use the Automatic Speech Recognition (ASR) from third parties as a service, SCD is essential to prevent unnecessary processing. Conventional approaches for SCD operate within the confines of sentence boundary detection using language models or sentence end detection using speech and text features. These have limitations in terms of relevant available data for training, performance within the memory and latency constraints, and the generalizability across voice assistant domains. In this paper, we propose a novel sentence completion detection method with low memory footprint for On-Device applications. We explore various sequence-level and sentence-level experiments using state-of-the-art Bi-LSTM and BERT based models for English language.
Polarization and its Life on Social Media: A Case Study on Sabarimala and Demonetisation
Authors: Ashutosh Ranjan, Dipti Sharma, Radhika Krishnan
Date: 2020-12-01 00:00:00
Proceeding: ICON 2020 12

This paper is an attempt to study polarisation on social media data. We focus on two hugely controversial and talked about events in the Indian diaspora, namely 1) the Sabarimala Temple (located in Kerala, India) incident which became a nationwide controversy when two women under the age of 50 secretly entered the temple breaking a long standing temple rule that disallowed women of menstruating age (10-50) to enter the temple and 2) the Indian government’s move to demonetise all existing 500 and 1000 denomination banknotes, comprising of 86% of the currency in circulation, in November 2016. We gather tweets around these two events in various time periods, preprocess and annotate them with their sentiment polarity and emotional category, and analyse trends to help us understand changing polarity over time around controversial events. The tweets collected are in English, Hindi and code-mixed Hindi-English. Apart from the analysis on the annotated data, we also present the twitter data comprising a total of around 1.5 million tweets.
A Rule Based Lightweight Bengali Stemmer
Authors: Souvick Das, Rajat Pandit, Sudip Kumar Naskar
Date: 2020-12-01 00:00:00
Proceeding: ICON 2020 12
Tasks: Information Retrieval, Retrieval

In the field of Natural Language Processing (NLP) the process of stemming plays a significant role. Stemmer transforms an inflected word to its root form. Stemmer significantly increases the efficiency of Information Retrieval (IR) systems. It is a very basic yet fundamental text pre-processing task widely used in many NLP tasks. Several important works on stemming have been carried out by researchers in English and other major languages. In this paper, we study and review existing works on stemming in Bengali and other Indian languages. Finally, we propose a rule based approach that explores Bengali morphology and leverages WordNet to achieve better accuracy. Our algorithm produced stemming accuracy of 98.86% for Nouns and 99.75% for Verbs.