pwc-archive/papers-with-abstracts huggingface dataset:
Treatment of optional forms in Mathematical modelling of Pāṇini
Authors: Anupriya Aggarwal, Malhar Kulkarni
Date: 2020-12-01 00:00:00
Proceeding: ICON 2020 12

Pāṇini in his Aṣṭādhyāyī has written the grammar of Sanskrit in an extremely concise manner in the form of about 4000 sūtras. We have attempted to mathematically remodel the data produced by these sūtras. The mathematical modelling is a way to show that the Pāṇinian approach is a minimal method of capturing the grammatical data for Sanskrit which is a natural language. The sūtras written by Pāṇini can be written as functions, that is for a single input the function produces a single output of the form y=f(x), where x and y is the input and output respectively. However, we observe that for some input dhātus, we get multiple outputs. For such cases, we have written multivalued functions that is the functions which give two or more outputs for a single input. In other words, multivalued function is a way to represent optional output forms which are expressed in Pāṇinian grammar with the help of 3 terms i.e. vā, vibhaṣā, and anyatarasyam. Comparison between the techniques employed by Pāṇini and our notation of functions helps us understand how Pāṇinian techniques ensure brevity and terseness, hence illustrating that Pāṇinian grammar is minimal.
Automatic Hadith Segmentation using PPM Compression
Authors: Taghreed Tarmom, Eric Atwell, Mohammad Alsalka
Date: 2020-12-01 00:00:00
Proceeding: ICON 2020 12
Tasks: Segmentation

In this paper we explore the use of Prediction by partial matching (PPM) compression based to segment Hadith into its two main components (Isnad and Matan). The experiments utilized the PPMD variant of the PPM, showing that PPMD is effective in Hadith segmentation. It was also tested on Hadith corpora of different structures. In the first experiment we used the non- authentic Hadith (NAH) corpus for train- ing models and testing, and in the second experiment we used the NAH corpus for training models and the Leeds University and King Saud University (LK) Hadith cor- pus for testing PPMD segmenter. PPMD of order 7 achieved an accuracy of 92.76% and 90.10% in the first and second experiments, respectively.
A Grammatical Sketch of Asur: A North Munda language
Authors: Zoya Khalid
Date: 2020-12-01 00:00:00
Proceeding: ICON 2020 12
Tasks: Descriptive, Negation

Asur belongs to North Munda sub-branch of Austro-Asiatic languages which now has less than 10,000 speakers. This is a very first attempt at describing and documenting Asur language, therefore the approach of this paper is descriptive rather than that of answering research questions. The paper attempts to describe the grammatical features such as gender, number, case, pronouns, tense-aspect-mood, negation, question formation, etc. of Asur language. It briefly touches upon the morphosyntactic and typological features of Asur, with the intent to present a concise overview of the language, which has so far remained almost untouched by documentary linguistics.
English to Manipuri and Mizo Post-Editing Effort and its Impact on Low Resource Machine Translation
Authors: Loitongbam Sanayai Meetei, Thoudam Doren Singh, Sivaji Bandyopadhyay, Mihaela Vela, Josef van Genabith
Date: 2020-12-01 00:00:00
Proceeding: ICON 2020 12
Tasks: Machine Translation, Translation

We present the first study on the post-editing (PE) effort required to build a parallel dataset for English-Manipuri and English-Mizo, in the context of a project on creating data for machine translation (MT). English source text from a local daily newspaper are machine translated into Manipuri and Mizo using PBSMT systems built in-house. A Computer Assisted Translation (CAT) tool is used to record the time, keystroke and other indicators to measure PE effort in terms of temporal and technical effort. A positive correlation between the technical effort and the number of function words is seen for English-Manipuri and English-Mizo but a negative correlation between the technical effort and the number of noun words for English-Mizo. However, average time spent per token in PE English-Mizo text is negatively correlated with the temporal effort. The main reason for these results are due to (i) English and Mizo using the same script, while Manipuri uses a different script and (ii) the agglutinative nature of Manipuri. Further, we check the impact of training a MT system in an incremental approach, by including the post-edited dataset as additional training data. The result shows an increase in HBLEU of up to 4.6 for English-Manipuri.
Learning to Interact: An Adaptive Interaction Framework for Knowledge Graph Embeddings
Authors: . Chandrahas, Nilesh Agrawal, Partha Talukdar
Date: 2020-12-01 00:00:00
Proceeding: ICON 2020 12
Tasks: Knowledge Graph Embeddings, Link Prediction

Knowledge Graph (KG) Embedding methods have been widely studied in the past few years and many methods have been proposed. These methods represent entities and relations in the KG as vectors in a vector space, trained to distinguish correct edges from the incorrect ones. For this distinction, simple functions of vectors’ dimensions, called interactions, are used. These interactions are used to calculate the candidate tail entity vector which is matched against all entities in the KG. However, for most of the existing methods, these interactions are fixed and manually specified. In this work, we propose an automated framework for discovering the interactions while training the KG Embeddings. The proposed method learns relevant interactions along with other parameters during training, allowing it to adapt to different datasets. Many of the existing methods can be seen as special cases of the proposed framework. We demonstrate the effectiveness of the proposed method on link prediction task by extensive experiments on multiple benchmark datasets.
Solving Arithmetic Word Problems Using Transformer and Pre-processing of Problem Texts
Authors: Kaden Griffith, Jugal Kalita
Date: 2020-12-01 00:00:00
Proceeding: ICON 2020 12
Tasks: Math

This paper outlines the use of Transformer networks trained to translate math word problems to equivalent arithmetic expressions in infix, prefix, and postfix notations. We compare results produced by a large number of neural configurations and find that most configurations outperform previously reported approaches on three of four datasets with significant increases in accuracy of over 20 percentage points. The best neural approaches boost accuracy by 30% on average when compared to the previous state-of-the-art.
Self Attended Stack-Pointer Networks for Learning Long Term Dependencies
Authors: Salih Tuc, Burcu Can
Date: 2020-12-01 00:00:00
Proceeding: ICON 2020 12
Tasks: Dependency Parsing, Sentence

We propose a novel deep neural architecture for dependency parsing, which is built upon a Transformer Encoder (Vaswani et al. 2017) and a Stack Pointer Network (Ma et al. 2018). We first encode each sentence using a Transformer Network and then the dependency graph is generated by a Stack Pointer Network by selecting the head of each word in the sentence through a head selection process. We evaluate our model on Turkish and English treebanks. The results show that our trasformer-based model learns long term dependencies efficiently compared to sequential models such as recurrent neural networks. Our self attended stack pointer network improves UAS score around 6% upon the LSTM based stack pointer (Ma et al. 2018) for Turkish sentences with a length of more than 20 words.
Creation of Corpus and Analysis in Code-Mixed Kannada-English Social Media Data for POS Tagging
Authors: Abhinav Reddy Appidi, Vamshi Krishna Srirangam, Darsi Suhas, Manish Shrivastava
Date: 2020-12-01 00:00:00
Proceeding: ICON 2020 12
Tasks: coreference-resolution, Coreference Resolution, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), POS, POS Tagging, Question Answering, Sentiment Analysis

Part-of-Speech (POS) is one of the essential tasks for many Natural Language Processing (NLP) applications. There has been a significant amount of work done in POS tagging for resource-rich languages. POS tagging is an essential phase of text analysis in understanding the semantics and context of language. These tags are useful for higher-level tasks such as building parse trees, which can be used for Named Entity Recognition, Coreference resolution, Sentiment Analysis, and Question Answering. There has been work done on code-mixed social media corpus but not on POS tagging of Kannada-English code-mixed data. Here, we present Kannada-English code- mixed social media corpus annotated with corresponding POS tags. We also experimented with machine learning classification models CRF, Bi-LSTM, and Bi-LSTM-CRF models on our corpus.
Identifying Complaints from Product Reviews: A Case Study on Hindi
Authors: Raghvendra Pratap Singh, Rejwanul Haque, Mohammed Hasanuzzaman, Andy Way
Date: 2020-12-01 00:00:00
Proceeding: ICON 2020 12
Tasks: Machine Translation, NMT, Translation

Automatic recognition of customer complaints on products or services that they purchase can be crucial for the organisations, multinationals and online retailers since they can exploit this information to fulfil their customers’ expectations including managing and resolving the complaints. Recently, researchers have applied supervised learning strategies to automatically identify users’ complaints expressed in English on Twitter. The downside of these approaches is that they require labeled training data for learning, which is expensive to create. This poses a barrier for them being applied to low-resource languages and domains for which task-specific data is not available. Machine translation (MT) can be used as an alternative to the tools that require such task-specific data. In this work, we use state-of-the-art neural MT (NMT) models for translating Hindi reviews into English and investigate performance of the downstream classification task (complaints identification) on their English translations.
BertAA : BERT fine-tuning for Authorship Attribution
Authors: Maël Fabien, Esau Villatoro-Tello, Petr Motlicek, Shantipriya Parida
Date: 2020-12-01 00:00:00
Proceeding: ICON 2020 12
Tasks: Authorship Attribution, Feature Engineering, Language Modeling, Language Modelling

Identifying the author of a given text can be useful in historical literature, plagiarism detection, or police investigations. Authorship Attribution (AA) has been well studied and mostly relies on a large feature engineering work. More recently, deep learning-based approaches have been explored for Authorship Attribution (AA). In this paper, we introduce BertAA, a fine-tuning of a pre-trained BERT language model with an additional dense layer and a softmax activation to perform authorship classification. This approach reaches competitive performances on Enron Email, Blog Authorship, and IMDb (and IMDb62) datasets, up to 5.3% (relative) above current state-of-the-art approaches. We performed an exhaustive analysis allowing to identify the strengths and weaknesses of the proposed method. In addition, we evaluate the impact of including additional features (e.g. stylometric and hybrid features) in an ensemble approach, improving the macro-averaged F1-Score by 2.7% (relative) on average.
TREE ADJOINING GRAMMAR BASED “LANGUAGE INDEPENDENT GENERATOR”
Authors: Pavan Kurariya, Prashant Chaudhary, Jahnavi Bodhankar, Lenali Singh, Ajai Kumar, Hemant Darbari
Date: 2020-12-01 00:00:00
Proceeding: ICON 2020 12
Tasks: Machine Translation, TAG, Translation

This paper proposes language independent natural language generator for Tree Adjoining Grammar (TAG)[8] based Machine Translation System. In this model, the TAG based parsing and generation approach considered for the syntactic and semantic analysis of a source language. This model provides an efficient and a systematic way of encapsulating language resources with engineering solution to develop the machine translation System. A TAG based Generator is developed with existing resources using TAG formalism to generate the target language from TAG based parser derivation. The process allows syntactic feature-marking, the Subject-Predicate Agreement marking and multiple synthesized generated outputs in complex and morphological rich language. The challenge in applying such approach is to handle the linguistically diversified features. It is achieved using rule-based translation grammar model to align the source language to corresponding target languages. The computational experiments demonstrate that substantial performance in terms of time and memory could also be obtained by using this approach. Nevertheless, this paper also describes the process of lexicalization and explain the state charts, TAG based adjunction and substitution function and the complexity and challenges beneath parsing-generation process.
Exploration of Cross-lingual Summarization for Kannada-EnglishLanguage Pair
Authors: Vinayaka R Kamath, Rachana Aithal K R, Vennela K, Mamatha Hr
Date: 2020-12-01 00:00:00
Proceeding: ICON 2020 12
Tasks: Dataset Generation

Cross-lingual summarization(CLS) is the process of generating a summary in one particular language for a source document in a different language. Low resource languages like Kannada greatly benefit from such systems because they help in delivering a concise representation of the same information in a different popular language. We propose a novel dataset generation pipeline and a first of its kind dataset that will aid in CLS for Kannada-English language pair. This work is also an attempt to inspect the existing systems and extend them to the Kannada-English language pair using our dataset.
A New Approach to Claim Check-Worthiness Prediction and Claim Verification
Authors: Shukrity Si, Anisha Datta, Sudip Naskar
Date: 2020-12-01 00:00:00
Proceeding: ICON 2020 12
Tasks: Articles, Claim Verification, Fact Checking, POS

The more we are advancing towards a modern world, the more it opens the path to falsification in every aspect of life. Even in case of knowing the surrounding, common people can not judge the actual scenario as the promises, comments and opinions of the influential people at power keep changing every day. Therefore computationally determining the truthfulness of such claims and comments has a very important societal impact. This paper describes a unique method to extract check-worthy claims from the 2016 US presidential debates and verify the truthfulness of the check-worthy claims. We classify the claims for check-worthiness with our modified Tf-Idf model which is used in background training on fact-checking news articles (NBC News and Washington Post). We check the truthfulness of the claims by using POS, sentiment score and cosine similarity features.
Improving Passage Re-Ranking with Word N-Gram Aware Coattention Encoder
Authors: Chaitanya Alaparthi, Manish Shrivastava
Date: 2020-12-01 00:00:00
Proceeding: ICON 2020 12
Tasks: Passage Ranking, Passage Re-Ranking, Re-Ranking, Text Matching

In text matching applications, coattentions have proved to be highly effective attention mechanisms. Coattention enables the learning to attend based on computing word level affinity scores between two texts. In this paper, we propose two improvements to coattention mechanism in the context of passage ranking (re-ranking). First, we extend the coattention mechanism by applying it across all word n-grams of query and passage. We show that these word n-gram coattentions can capture local context in query and passage to better judge the relevance between them. Second, we further improve the model performance by proposing a query based attention pooling on passage encodings. We evaluate these two methods on MSMARCO passage re-ranking task. The experiment results shows that these two methods resulted in a relative increase of 8.04% in Mean Reciprocal Rank @10 (MRR@10) compared to the naive coattention mechanism. At the time of writing this paper, our methods are the best non transformer model on MS MARCO passage re-ranking task and are competitive to BERT base while only having less than 10% of the parameters.
Automated Arabic Essay Evaluation
Authors: Abeer Alqahtani, Amal Alsaif
Date: 2020-12-01 00:00:00
Proceeding: ICON 2020 12

Although the manual evaluation of essays is a time-consuming process, writing essays has a significant role in assessing learning outcomes. Therefore, automated essay evaluation represents a solution, especially for schools, universities, and testing companies. Moreover, the existence of such systems overcomes some factors that influence manual evaluation such as the evaluator’s mental state, the disparity between evaluators, and others. In this paper, we propose an Arabic essay evaluation system based on a support vector regression (SVR) model along with a wide range of features including morphological, syntactic, semantic, and discourse features. The system evaluates essays according to five criteria: spelling, essay structure, coherence level, style, and punctuation marks, without the need for domain-representative essays (a model essay). A specific model is developed for each criterion; thus, the overall evaluation of the essay is a combination of the previous criteria results. We develop our dataset based on essays written by university students and journalists whose native language is Arabic. The dataset is then evaluated by experts. The experimental results show that 96% of our dataset is correctly evaluated in the overall score and the correlation between the system and the experts’ evaluation is 0.87. Additionally, the system shows variant results in evaluating criteria separately.
Constructing a Korean Named Entity Recognition Dataset for the Financial Domain using Active Learning
Authors: Dong-Ho Jeong, Min-Kang Heo, Hyung-Chul Kim, Sang-Won Park
Date: 2020-12-01 00:00:00
Proceeding: ICON 2020 12
Tasks: Active Learning, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER)

The performance of deep learning models depends on the quality and quantity of data. Data construction, however, is time- consuming and costly. In addition, when expert domain data are constructed, the availability of experts is limited. In such cases, active learning can efficiently increase the performance of the learning models with minimal data construction. Although various datasets have been constructed using active learning techniques, vigorous studies on the construction of Korean data on expert domains are yet to be conducted. In this study, a corpus for named entity recognition was constructed for the financial domain using the active learning technique. The contributions of the study are as follows. (1) It was verified that the active learning technique could effectively construct the named entity recognition corpus for the financial domain, and (2) a named entity recognizer for the financial domain was developed. Data of 8,043 sentences were constructed using the proposed method, and the performance of the named entity recognizer reached 80.84%. Moreover, the proposed method reduced data construction costs by 12–25%
SUKHAN: Corpus of Hindi Shayaris annotated with Sentiment Polarity Information
Authors: Salil Aggarwal, Abhigyan Ghosh, Radhika Mamidi
Date: 2020-12-01 00:00:00
Proceeding: ICON 2020 12
Tasks: Sentiment Analysis, Sentiment Classification

Shayari is a form of poetry mainly popular in the Indian subcontinent, in which the poet expresses his emotions and feelings in a very poetic manner. It is one of the best ways to express our thoughts and opinions. Therefore, it is of prime importance to have an annotated corpus of Hindi shayaris for the task of sentiment analysis. In this paper, we introduce SUKHAN, a dataset consisting of Hindi shayaris along with sentiment polarity labels. To the best of our knowledge, this is the first corpus of Hindi shayaris annotated with sentiment polarity information. This corpus contains a total of 733 Hindi shayaris of various genres. Also, this dataset is of utmost value as all the annotation is done manually by five annotators and this makes it a very rich dataset for training purposes. This annotated corpus is also used to build baseline sentiment classification models using machine learning techniques.
Improving Neural Machine Translation for Sanskrit-English
Authors: Ravneet Punia, Aditya Sharma, Sarthak Pruthi, Minni Jain
Date: 2020-12-01 00:00:00
Proceeding: ICON 2020 12
Tasks: Machine Translation, reinforcement-learning, Reinforcement Learning (RL), Transfer Learning, Translation

Sanskrit is one of the oldest languages of the Asian Subcontinent that fell out of common usage around 600 B.C. In this paper, we attempt to translate Sanskrit to English using Neural Machine Translation approaches based on Reinforcement Learning and Transfer learning that were never tried and tested on Sanskrit. Along with the paper, we also release monolingual Sanskrit and parallel aligned Sanskrit-English corpora for the research community. Our methodologies outperform the previous approaches applied to Sanskrit by various re- searchers and will further help the linguistic community to accelerate the costly and time consuming manual translation process.
Parsing Indian English News Headlines
Authors: Samapika Roy, Sukhada Sukhada, Anil Kumar Singh
Date: 2020-12-01 00:00:00
Proceeding: ICON 2020 12
Tasks: Emotion Classification, World Knowledge

Parsing news Headlines is one of the difficult tasks of Natural Language Processing. It is mostly because news Headlines (NHs) are not complete grammatical sentences. News editors use all sorts of tricks to grab readers’ attention, for instance, unusual capitalization as in the headline’ Ear SHOT ashok rajagopalan’; some are world knowledge demanding like ‘Church reformation celebrated’ where the ‘Church reformation’ refers to a historical event and not a piece of news about an ordinary church. The lack of transparency in NHs can be linguistic, cultural, social, or contextual. The lack of space provided for a news headline has led to creative liberty. Though many works like news value extraction, summary generation, emotion classification of NHs have been going on, parsing them had been a tough challenge. Linguists have also been interested in NHs for creativity in the language used by bending traditional grammar rules. Researchers have conducted studies on news reportage, discourse analysis of NHs, and many more. While the creativity seen in NHs is fascinating for language researchers, it poses a computational challenge for Natural Language Processing researchers. This paper presents an outline of the ongoing doctoral research on the parsing of Indian English NHs. The ultimate aim of this research is to provide a module that will generate correctly parsed NHs. The intention is to enhance the broad applicability of newspaper corpus for future Natural Language Processing applications.
WORD SENSE DISAMBIUATION FOR KASHMIRI LANGUAGE USING SUPERVISED MACHINE LEARNING
Authors: Tawseef Ahmad Mir, Aadil Ahmad Lawaye
Date: 2020-12-01 00:00:00
Proceeding: ICON 2020 12
Tasks: BIG-bench Machine Learning, Word Sense Disambiguation

Every language used in this word has ambiguous words. The process of analyzing the word tokens and assigning the correct meanings to the ambiguous words according the context in which they are used is called word sense disambiguation(WSD). WSD is a very hot research topic in Natural Language Processing. The main purpose of my research work is to tackle the WSD problem for Kashmiri language using Supervised Machine Learning Approaches