pwc-archive/papers-with-abstracts huggingface dataset:
Identifying Nuanced Dialect for Arabic Tweets with Deep Learning and Reverse Translation Corpus Extension System
Authors: Rawan Tahssin, Youssef Kishk, Marwan Torki
Date: 2020-12-01 00:00:00
Proceeding: COLING (WANLP) 2020 12
Tasks: Dialect Identification, Translation

In this paper, we present our work for the NADI Shared Task (Abdul-Mageed and Habash, 2020): Nuanced Arabic Dialect Identification for Subtask-1: country-level dialect identification. We introduce a Reverse Translation Corpus Extension Systems (RTCES) to handle data imbalance along with reported results on several experimented approaches of word and document representations and different models architectures. The top scoring model was based on AraBERT (Antoun et al., 2020), with our modified extended corpus based on reverse translation of the given Arabic tweets. The selected system achieved a macro average F1 score of 20.34% on the test set, which places us as the 7th out of 18 teams in the final ranking Leaderboard.
The QMUL/HRBDT contribution to the NADI Arabic Dialect Identification Shared Task
Authors: Abdulrahman Aloraini, Massimo Poesio, Ayman Alhelbawy
Date: 2020-12-01 00:00:00
Proceeding: COLING (WANLP) 2020 12
Tasks: Dialect Identification

We present the Arabic dialect identification system that we used for the country-level subtask of the NADI challenge. Our model consists of three components: BiLSTM-CNN, character-level TF-IDF, and topic modeling features. We represent each tweet using these features and feed them into a deep neural network. We then add an effective heuristic that improves the overall performance. We achieved an F1-Macro score of 20.77% and an accuracy of 34.32% on the test set. The model was also evaluated on the Arabic Online Commentary dataset, achieving results better than the state-of-the-art.
Arabic Dialects Identification for All Arabic countries
Authors: Ahmed Aliwy, Hawraa Taher, Zena AboAltaheen
Date: 2020-12-01 00:00:00
Proceeding: COLING (WANLP) 2020 12
Tasks: All, Clustering

Arabic dialects are among of three main variant of Arabic language (Classical Arabic, modern standard Arabic and dialectal Arabic). It has many variants according to the country, city (provinces) or town. In this paper, several techniques with multiple algorithms are applied for Arabic dialects identification starting from removing noise till classification task using all Arabic countries as 21 classes. Three types of classifiers (Naïve Bayes, Logistic Regression, and Decision Tree) are combined using voting with two different methodologies. Also clustering technique is used for decreasing the noise that result from the existing of MSA tweets in the data set for training phase. The results of f-measure were 27.17, 41.34 and 52.38 for first methodology without clustering, second methodology without clustering, and second methodology with clustering, the used data set is NADI shared task data set.
Arabic Dialect Identification Using BERT Fine-Tuning
Authors: Moataz Mansour, Moustafa Tohamy, Zeyad Ezzat, Marwan Torki
Date: 2020-12-01 00:00:00
Proceeding: COLING (WANLP) 2020 12
Tasks: Deep Learning, Dialect Identification, Transfer Learning

In the last few years, deep learning has proved to be a very effective paradigm to discover patterns in large data sets. Unfortunately, deep learning training on small data sets is not the best option because most of the time traditional machine learning algorithms could get better scores. Now, we can train the neural network on a large data set then fine-tune on a smaller data set using the transfer learning technique. In this paper, we present our system for NADI shared Task: Country-level Dialect Identification, Our system is based on fine-tuning of BERT and it achieves 22.85 F1-score on Test Set and our rank is 5th out of 18 teams.
LTG-ST at NADI Shared Task 1: Arabic Dialect Identification using a Stacking Classifier
Authors: Samia Touileb
Date: 2020-12-01 00:00:00
Proceeding: COLING (WANLP) 2020 12
Tasks: Dialect Identification, regression

This paper presents our results for the Nuanced Arabic Dialect Identification (NADI) shared task of the Fifth Workshop for Arabic Natural Language Processing (WANLP 2020). We participated in the first sub-task for country-level Arabic dialect identification covering 21 Arab countries. Our contribution is based on a stacking classifier using Multinomial Naive Bayes, Linear SVC, and Logistic Regression classifiers as estimators; followed by a Logistic Regression as final estimator. Despite the fact that the results on the test set were low, with a macro F1 of 17.71, we were able to show that a simple approach can achieve comparable results to more sophisticated solutions. Moreover, the insights of our error analysis, and of the corpus content in general, can be used to develop and improve future systems.
Robust Text Classification using Sub-Word Information in Input Word Representations.
Authors: Bhanu Prakash Mahanti, Priyank Chhipa, Vivek Sridhar, Vinuthkumar Prasan
Date: 2019-12-01 00:00:00
Proceeding: ICON 2019 12
Tasks: Language Modelling, Machine Translation, text-classification, Text Classification

Word based deep learning approaches have been used with increasing success recently to solve Natural Language Processing problems like Machine Translation, Language Modelling and Text Classification. However, performance of these word based models is limited by the vocabulary of the training corpus. Alternate approaches using character based models have been proposed to overcome the unseen word problems arising for a variety of reasons. However, character based models fail to capture the sequential relationship of words inherently present in texts. Hence, there is scope for improvement by addressing the unseen word problem while also maintaining the sequential context through word based models. In this work, we propose a method where the input embedding vector incorporates sub-word information but is also suitable for use with models which successfully capture the sequential nature of text. We further attempt to establish that using such a word representation as input makes the model robust to unseen words, particularly arising due to tokenization and spelling errors, which is a common problem in systems where a typing interface is one of the input modalities.
A Deep Ensemble Framework for Fake News Detection and Multi-Class Classification of Short Political Statements
Authors: Arjun Roy, Kingshuk Basak, Asif Ekbal, Pushpak Bhattacharyya
Date: 2019-12-01 00:00:00
Proceeding: ICON 2019 12
Tasks: Fake News Detection, Misinformation, Multi-class Classification

Fake news, rumor, incorrect information, and misinformation detection are nowadays crucial issues as these might have serious consequences for our social fabrics. Such information is increasing rapidly due to the availability of enormous web information sources including social media feeds, news blogs, online newspapers etc. In this paper, we develop various deep learning models for detecting fake news and classifying them into the pre-defined fine-grained categories. At first, we develop individual models based on Convolutional Neural Network (CNN), and Bi-directional Long Short Term Memory (Bi-LSTM) networks. The representations obtained from these two models are fed into a Multi-layer Perceptron Model (MLP) for the final classification. Our experiments on a benchmark dataset show promising results with an overall accuracy of 44.87%, which outperforms the current state of the arts.
Building Discourse Parser for Thirukkural
Authors: Anita R, Subalalitha C N
Date: 2019-12-01 00:00:00
Proceeding: ICON 2019 12
Tasks: Ethics, Information Retrieval, Question Answering, Retrieval

Thirukkural is one of the famous Tamil Literatures in the world. It was written by Thiruvalluvar, and focuses on ethics and morality. It provides all possible solutions to lead a successful and a peaceful life fitting any generation. It has been translated into 82 global languages, which necessitate the access of Thirukkural in any language on the World Wide Web (WWW) and processing the Thirukkural computationally. This paper aims at constructing the Thirukkural Discourse Parser which finds the semantic relations in the Thirukkurals which can extract the hidden meaning in it and help in utilizing the same in various Natural Language Processing (NLP) applications, such as, Summary Generation Systems, Information Retrieval (IR) Systems and Question Answering (QA) Systems. Rhetorical Structure Theory (RST) is one of the discourse theories, which is used in NLP to find the coherence between texts. This paper finds the relation within the Thriukkurals and the discourse structure is created using the Thirukkural Discourse Parser. The resultant discourse structure of Thirukkural can be indexed and further be used by Summary Generation Systems, IR Systems and QA Systems. This facilitates the end user to access Thirukkural on WWW and get benefited. This Thirukkural Discourse Parser has been tested with all 1330 Thirukurals using precision and recall.
Incorporating Sub-Word Level Information in Language Invariant Neural Event Detection
Authors: Suhan Prabhu, Pranav Goel, Alok Debnath, Manish Shrivastava
Date: 2019-12-01 00:00:00
Proceeding: ICON 2019 12
Tasks: Event Detection, Word Embeddings

Detection of TimeML events in text have traditionally been done on corpora such as TimeBanks. However, deep learning methods have not been applied to these corpora, because these datasets seldom contain more than 10,000 event mentions. Traditional architectures revolve around highly feature engineered, language specific statistical models. In this paper, we present a Language Invariant Neural Event Detection (ALINED) architecture. ALINED uses an aggregation of both sub-word level features as well as lexical and structural information. This is achieved by combining convolution over character embeddings, with recurrent layers over contextual word embeddings. We find that our model extracts relevant features for event span identification without relying on language specific features. We compare the performance of our language invariant model to the current state-of-the-art in English, Spanish, Italian and French. We outperform the F1-score of the state of the art in English by 1.65 points. We achieve F1-scores of 84.96, 80.87 and 74.81 on Spanish, Italian and French respectively which is comparable to the current states of the art for these languages. We also introduce the automatic annotation of events in Hindi, a low resource language, with an F1-Score of 77.13.
Event Centric Entity Linking for Hindi News Articles: A Knowledge Graph Based Approach
Authors: Pranav Goel, Suhan Prabhu, Alok Debnath, Manish Shrivastava
Date: 2019-12-01 00:00:00
Proceeding: ICON 2019 12
Tasks: Articles, Entity Linking

We describe the development of a knowledge graph from an event annotated corpus by presenting a pipeline that identifies and extracts the relations between entities and events from Hindi news articles. Due to the semantic implications of argument identification for events in Hindi, we use a combined syntactic argument and semantic role identification methodology. To the best of our knowledge, no other architecture exists for this purpose. The extracted combined role information is incorporated in a knowledge graph that can be queried via subgraph extraction for basic questions. The architectures presented in this paper can be used for participant extraction and event-entity linking in most Indo-Aryan languages, due to similar syntactic and semantic properties of event arguments.
Language Modelling with NMT Query Translation for Amharic-Arabic Cross-Language Information Retrieval
Authors: Ibrahim Gashaw, H.l Shashirekha
Date: 2019-12-01 00:00:00
Proceeding: ICON 2019 12
Tasks: Information Retrieval, Language Modeling, Language Modelling, Machine Translation, NMT, Retrieval, Translation

This paper describes our first experiment on Neural Machine Translation (NMT) based query translation for Amharic-Arabic Cross-Language Information Retrieval (CLIR) task to retrieve relevant documents from Amharic and Arabic text collections in response to a query expressed in the Amharic language. We used a pre-trained NMT model to map a query in the source language into an equivalent query in the target language. The relevant documents are then retrieved using a Language Modeling (LM) based retrieval algorithm. Experiments are conducted on four conventional IR models, namely Uni-gram and Bi-gram LM, Probabilistic model, and Vector Space Model (VSM). The results obtained illustrate that the proposed Uni-gram LM outperforms all other models for both Amharic and Arabic language document collections.
Non-native Accent Partitioning for Speakers of Indian Regional Languages
Authors: Radha Krishna Guntur, Krishnan Ramakrishnan, Vinay Kumar Mittal
Date: 2019-12-01 00:00:00
Proceeding: ICON 2019 12

Acoustic features extracted from the speech signal can help in identifying speaker related multiple information such as geographical origin, regional accent and nativity. In this paper, classification of native speakers of South Indian languages is carried out based upon the accent of their non-native language, i.e., English. Four South Indian languages: Kannada, Malayalam, Tamil, and Telugu are examined. A database of English speech from the native speakers of these languages, along with the native language speech data was collected, from a non-overlapping set of speakers. Segment level acoustic features F0 and Mel-frequency cepstral coefficients (MFCCs) are used. Accent partitioning of non-native English speech data is carried out using multiple classifiers: k-nearest neighbour (KNN), linear discriminant analysis (LDA) and support vector machine (SVM), for validation and comparison of results. Classification accuracies of 86.6% are observed using KNN, and 89.2% or more than 90% using SVM classifier. A study of acoustic feature F0 contour, related to L2 intonation, showed that native speakers of Kannada language are quite distinct as compared to those of Tamil or Telugu languages. It is also observed that identification of Malayalam and Kannada speakers from their English speech accent is relatively easier than Telugu or Tamil speakers.
A little perturbation makes a difference: Treebank augmentation by perturbation improves transfer parsing
Authors: Ayan Das, Sudeshna Sarkar
Date: 2019-12-01 00:00:00
Proceeding: ICON 2019 12
Tasks: Cross-Lingual Transfer

We present an approach for cross-lingual transfer of dependency parser so that the parser trained on a single source language can more effectively cater to diverse target languages. In this work, we show that the cross-lingual performance of the parsers can be enhanced by over-generating the source language treebank. For this, the source language treebank is augmented with its perturbed version in which controlled perturbation is introduced in the parse trees by stochastically reordering the positions of the dependents with respect to their heads while keeping the structure of the parse trees unchanged. This enables the parser to capture diverse syntactic patterns in addition to those that are found in the source language. The resulting parser is found to more effectively parse target languages with different syntactic structures. With English as the source language, our system shows an average improvement of 6.7% and 7.7% in terms of UAS and LAS over 29 target languages compared to the baseline single source parser trained using unperturbed source language treebank. This also results in significant improvement over the transfer parser proposed by (CITATION) that involves an “order-free” parser algorithm.
Autism Speech Analysis using Acoustic Features
Authors: Abhijit Mohanta, Vinay Kumar Mittal
Date: 2019-12-01 00:00:00
Proceeding: ICON 2019 12

Autism speech has distinct acoustic patterns, different from normal speech. Analyzing acoustic features derived from the speech of children affected with autism spectrum disorder (ASD) can help its early detection. In this study, a comparative analysis of the discriminating acoustic characteristics is carried out between ASD affected and normal children speech, from speech production point of view. Datasets of English speech of children affected with ASD and normal children were recorded. Changes in the speech production characteristics are examined using the excitation source features F0 and strength of excitation (SoE), the vocal tract filter features formants (F1 to F5) and dominant frequencies (FD1, FD2), and the combined source-filter features signal energy and zero-crossing rate. Changes in the acoustic features are compared in the five vowels’ regions of the English language. Significant changes in few acoustic features are observed for ASD affected speech as compared to normal speech. The differences between the mean values of the formants and dominant frequencies, for ASD affected and normal children, are highest for vowel /i/. It indicates that ASD affected children have possibly more difficulty in speaking the words with vowel /i/. This study can be helpful towards developing systems for automatic detection of ASD.
A Survey on Ontology Enrichment from Text
Authors: Vivek Iyer, Lalit Mohan, Mehar Bhatia, Y. Raghu Reddy
Date: 2019-12-01 00:00:00
Proceeding: ICON 2019 12
Tasks: Survey

Increased internet bandwidth at low cost is leading to the creation of large volumes of unstructured data. This data explosion opens up opportunities for the creation of a variety of data-driven intelligent systems, such as the Semantic Web. Ontologies form one of the most crucial layers of semantic web, and the extraction and enrichment of ontologies given this data explosion becomes an inevitable research problem. In this paper, we survey the literature on semi-automatic and automatic ontology extraction and enrichment and classify them into four broad categories based on the approach. Then, we proceed to narrow down four algorithms from each of these categories, implement and analytically compare them based on parameters like context relevance, efficiency and precision. Lastly, we propose a Long Short Term Memory Networks (LSTM) based deep learning approach to try and overcome the gaps identified in these approaches.
Robust Deep Learning Based Sentiment Classification of Code-Mixed Text
Authors: Siddhartha Mukherjee, Vinuthkumar Prasan, Anish Nediyanchath, Manan Shah, Nikhil Kumar
Date: 2019-12-01 00:00:00
Proceeding: ICON 2019 12
Tasks: Classification, Deep Learning, Diversity, Robust classification, Sentiment Analysis, Sentiment Classification

India is one of unique countries in the world that has the legacy of diversity of languages. Most of these languages are influenced by English. This causes a large presence of code-mixed text in Social Media. Enormous presence of this code-mixed text provides an important research area for Natural Language Processing (NLP). This paper proposes a novel Attention based deep learning technique for Sentiment Classification on Code-Mixed Text (ACCMT) of Hindi-English. The proposed architecture uses fusion of character and word features. Non availability of suitable Word Embedding to represent these Code-Mixed texts is another important hurdle for this league of NLP tasks. This paper also proposes a novel technique for preparing Word Embedding of Code-Mixed text. This embedding is prepared with two separately trained word-embedding on Romanized Hindi and English respectively. This embedding is further used in the proposed deep learning based architecture for robust classification. The Proposed technique achieves 71.97% accuracy, which exceeds the baseline accuracy.
Dataset for Aspect Detection on Mobile reviews in Hindi
Authors: Pruthwik Mishra, Ayush Joshi, Dipti Sharma
Date: 2019-12-01 00:00:00
Proceeding: ICON 2019 12
Tasks: Opinion Mining, Sentence

In recent years Opinion Mining has become one of the very interesting fields of Language Processing. To extract the gist of a sentence in a shorter and efficient manner is what opinion mining provides. In this paper we focus on detecting aspects for a particular domain. While relevant research work has been done in aspect detection in resource rich languages like English, we are trying to do the same in a relatively resource poor Hindi language. Here we present a corpus of mobile reviews which are labelled with carefully curated aspects. The motivation behind Aspect detection is to get information on a finer level about the data. In this paper we identify all aspects related to the gadget which are present on the reviews given online on various websites. We also propose baseline models to detect aspects in Hindi text after conducting various experiments.
Multi-linguality helps: Event-Argument Extraction for Disaster Domain in Cross-lingual and Multi-lingual setting
Authors: Zishan Ahmad, Deeksha Varshney, Asif Ekbal, Pushpak Bhattacharyya
Date: 2019-12-01 00:00:00
Proceeding: ICON 2019 12
Tasks: Event Argument Extraction, Event Extraction, Management

Automatic extraction of disaster-related events and their arguments from natural language text is vital for building a decision support system for crisis management. Event extraction from various news sources is a well-explored area for this objective. However, extracting events alone, without any context, provides only partial help for this purpose. Extracting related arguments like Time, Place, Casualties, etc., provides a complete picture of the disaster event. In this paper, we create a disaster domain dataset in Hindi by annotating disaster-related event and arguments. We also obtain equivalent datasets for Bengali and English from a collaboration. We build a multi-lingual deep learning model for argument extraction in all the three languages. We also compare our multi-lingual system with a similar baseline mono-lingual system trained for each language separately. It is observed that a single multi-lingual system is able to compensate for lack of training data, by using joint training of dataset from different languages in shared space, thus giving a better overall result.
Towards Handling Verb Phrase Ellipsis in English-Hindi Machine Translation
Authors: Niyati Bafna, Dipti Sharma
Date: 2019-12-01 00:00:00
Proceeding: ICON 2019 12
Tasks: Machine Translation, Sentence, Translation

English-Hindi machine translation systems have difficulty interpreting verb phrase ellipsis (VPE) in English, and commit errors in translating sentences with VPE. We present a solution and theoretical backing for the treatment of English VPE, with the specific scope of enabling English-Hindi MT, based on an understanding of the syntactical phenomenon of verb-stranding verb phrase ellipsis in Hindi (VVPE). We implement a rule-based system to perform the following sub-tasks: 1) Verb ellipsis identification in the English source sentence, 2) Elided verb phrase head identification 3) Identification of verb segment which needs to be induced at the site of ellipsis 4) Modify input sentence; i.e. resolving VPE and inducing the required verb segment. This system obtains 94.83 percent precision and 83.04 percent recall on subtask (1), tested on 3900 sentences from the BNC corpus. This is competitive with state-of-the-art results. We measure accuracy of subtasks (2) and (3) together, and obtain a 91 percent accuracy on 200 sentences taken from the WSJ corpus. Finally, in order to indicate the relevance of ellipsis handling to MT, we carried out a manual analysis of the English-Hindi MT outputs of 100 sentences after passing it through our system. We set up a basic metric (1-5) for this evaluation, where 5 indicates drastic improvement, and obtained an average of 3.55. As far as we know, this is the first attempt to target ellipsis resolution in the context of improving English-Hindi machine translation.