pwc-archive/papers-with-abstracts huggingface dataset:
Enhancing the Measurement of Social Effects by Capturing Morality
Authors: Rezvaneh Rezapour, Saumil H. Shah, Jana Diesner
Date: 2019-06-01 00:00:00
Proceeding: WS 2019 6
Tasks: Cultural Vocal Bursts Intensity Prediction

We investigate the relationship between basic principles of human morality and the expression of opinions in user-generated text data. We assume that people{'}s backgrounds, culture, and values are associated with their perceptions and expressions of everyday topics, and that people{'}s language use reflects these perceptions. While personal values and social effects are abstract and complex concepts, they have practical implications and are relevant for a wide range of NLP applications. To extract human values (in this paper, morality) and measure social effects (morality and stance), we empirically evaluate the usage of a morality lexicon that we expanded via a quality controlled, human in the loop process. As a result, we enhanced the Moral Foundations Dictionary in size (from 324 to 4,636 syntactically disambiguated entries) and scope. We used both lexica for feature-based and deep learning classification (SVM, RF, and LSTM) to test their usefulness for measuring social effects. We find that the enhancement of the original lexicon led to measurable improvements in prediction accuracy for the selected NLP tasks.
Deep Learning Techniques for Humor Detection in Hindi-English Code-Mixed Tweets
Authors: Sushmitha Reddy Sane, Suraj Tripathi, Koushik Reddy Sane, Radhika Mamidi
Date: 2019-06-01 00:00:00
Proceeding: WS 2019 6
Tasks: Deep Learning, Humor Detection, Word Embeddings

We propose bilingual word embeddings based on word2vec and fastText models (CBOW and Skip-gram) to address the problem of Humor detection in Hindi-English code-mixed tweets in combination with deep learning architectures. We focus on deep learning approaches which are not widely used on code-mixed data and analyzed their performance by experimenting with three different neural network models. We propose convolution neural network (CNN) and bidirectional long-short term memory (biLSTM) (with and without Attention) models which take the generated bilingual embeddings as input. We make use of Twitter data to create bilingual word embeddings. All our proposed architectures outperform the state-of-the-art results, and Attention-based bidirectional LSTM model achieved an accuracy of 73.6{\%} which is an increment of more than 4{\%} compared to the current state-of-the-art results.
How do we feel when a robot dies? Emotions expressed on Twitter before and after hitchBOT's destruction
Authors: Kathleen C. Fraser, Frauke Zeller, David Harris Smith, Saif Mohammad, Frank Rudzicz
Date: 2019-06-01 00:00:00
Proceeding: WS 2019 6
Tasks: Ethics

In 2014, a chatty but immobile robot called hitchBOT set out to hitchhike across Canada. It similarly made its way across Germany and the Netherlands, and had begun a trip across the USA when it was destroyed by vandals. In this work, we analyze the emotions and sentiments associated with words in tweets posted before and after hitchBOT{'}s destruction to answer two questions: Were there any differences in the emotions expressed across the different countries visited by hitchBOT? And how did the public react to the demise of hitchBOT? Our analyses indicate that while there were few cross-cultural differences in sentiment towards hitchBOT, there was a significant negative emotional reaction to its destruction, suggesting that people had formed an emotional connection with hitchBOT and perceived its destruction as morally wrong. We discuss potential implications of anthropomorphism and emotional attachment to robots from the perspective of robot ethics.
``When Numbers Matter!'': Detecting Sarcasm in Numerical Portions of Text
Authors: Abhijeet Dubey, Lakshya Kumar, Arpan Somani, Aditya Joshi, Pushpak Bhattacharyya
Date: 2019-06-01 00:00:00
Proceeding: WS 2019 6
Tasks: Sarcasm Detection, Sentence

Research in sarcasm detection spans almost a decade. However a particular form of sarcasm remains unexplored: sarcasm expressed through numbers, which we estimate, forms about 11{\%} of the sarcastic tweets in our dataset. The sentence {`}Love waking up at 3 am{'} is sarcastic because of the number. In this paper, we focus on detecting sarcasm in tweets arising out of numbers. Initially, to get an insight into the problem, we implement a rule-based and a statistical machine learning-based (ML) classifier. The rule-based classifier conveys the crux of the numerical sarcasm problem, namely, incongruity arising out of numbers. The statistical ML classifier uncovers the indicators i.e., features of such sarcasm. The actual system in place, however, are two deep learning (DL) models, CNN and attention network that obtains an F-score of 0.93 and 0.91 on our dataset of tweets containing numbers. To the best of our knowledge, this is the first line of research investigating the phenomenon of sarcasm arising out of numbers, culminating in a detector thereof.
Cross-lingual Subjectivity Detection for Resource Lean Languages
Authors: Ida Amini, Samane Karimi, Azadeh Shakery
Date: 2019-06-01 00:00:00
Proceeding: WS 2019 6
Tasks: Language Modeling, Language Modelling, Learning-To-Rank, Opinion Mining, Sentiment Analysis

Wide and universal changes in the web content due to the growth of web 2 applications increase the importance of user-generated content on the web. Therefore, the related research areas such as sentiment analysis, opinion mining and subjectivity detection receives much attention from the research community. Due to the diverse languages that web-users use to express their opinions and sentiments, research areas like subjectivity detection should present methods which are practicable on all languages. An important prerequisite to effectively achieve this aim is considering the limitations in resource-lean languages. In this paper, cross-lingual subjectivity detection on resource lean languages is investigated using two different approaches: a language-model based and a learning-to-rank approach. Experimental results show the impact of different factors on the performance of subjectivity detection methods using English resources to detect the subjectivity score of Persian documents. The experiments demonstrate that the proposed learning-to-rank method outperforms the baseline method in ranking documents based on their subjectivity degree.
Analyzing Incorporation of Emotion in Emoji Prediction
Authors: Shirley Anugrah Hayati, Aldrian Obaja Muis
Date: 2019-06-01 00:00:00
Proceeding: WS 2019 6
Tasks: Prediction

In this work, we investigate the impact of incorporating emotion classes on the task of predicting emojis from Twitter texts. More specifically, we first show that there is a correlation between the emotion expressed in the text and the emoji choice of Twitter users. Based on this insight we propose a few simple methods to incorporate emotion information in traditional classifiers. Through automatic metrics, human evaluation, and error analysis, we show that the improvement obtained by incorporating emotion is significant and correlate better with human preferences compared to the baseline models. Through the human ratings that we obtained, we also argue for preference metric to better evaluate the usefulness of an emoji prediction system.
A Report on the Third VarDial Evaluation Campaign
Authors: Marcos Zampieri, Shervin Malmasi, Yves Scherrer, Tanja Samard{\v{z}}i{\'c}, Francis Tyers, Miikka Silfverberg, Natalia Klyueva, Tung-Le Pan, Chu-Ren Huang, Radu Tudor Ionescu, Andrei M. Butnaru, Tommi Jauhiainen
Date: 2019-06-01 00:00:00
Proceeding: WS 2019 6
Tasks: Dialect Identification, Language Identification, Morphological Analysis

In this paper, we present the findings of the Third VarDial Evaluation Campaign organized as part of the sixth edition of the workshop on Natural Language Processing (NLP) for Similar Languages, Varieties and Dialects (VarDial), co-located with NAACL 2019. This year, the campaign included five shared tasks, including one task re-run {--} German Dialect Identification (GDI) {--} and four new tasks {--} Cross-lingual Morphological Analysis (CMA), Discriminating between Mainland and Taiwan variation of Mandarin Chinese (DMT), Moldavian vs. Romanian Cross-dialect Topic identification (MRC), and Cuneiform Language Identification (CLI). A total of 22 teams submitted runs across the five shared tasks. After the end of the competition, we received 14 system description papers, which are published in the VarDial workshop proceedings and referred to in this report.
Improving Cuneiform Language Identification with BERT
Authors: Gabriel Bernier-Colborne, Cyril Goutte, Serge L{\'e}ger
Date: 2019-06-01 00:00:00
Proceeding: WS 2019 6
Tasks: Language Identification

We describe the systems developed by the National Research Council Canada for the Cuneiform Language Identification (CLI) shared task at the 2019 VarDial evaluation campaign. We compare a state-of-the-art baseline relying on character n-grams and a traditional statistical classifier, a voting ensemble of classifiers, and a deep learning approach using a Transformer network. We describe how these systems were trained, and analyze the impact of some preprocessing and model estimation decisions. The deep neural network achieved 77{\%} accuracy on the test data, which turned out to be the best performance at the CLI evaluation, establishing a new state-of-the-art for cuneiform language identification.
Joint Approach to Deromanization of Code-mixed Texts
Authors: Rashed Rubby Riyadh, Grzegorz Kondrak
Date: 2019-06-01 00:00:00
Proceeding: WS 2019 6
Tasks: Language Identification, Transliteration

The conversion of romanized texts back to the native scripts is a challenging task because of the inconsistent romanization conventions and non-standard language use. This problem is compounded by code-mixing, i.e., using words from more than one language within the same discourse. In this paper, we propose a novel approach for handling these two problems together in a single system. Our approach combines three components: language identification, back-transliteration, and sequence prediction. The results of our experiments on Bengali and Hindi datasets establish the state of the art for the task of deromanization of code-mixed texts.
Language Discrimination and Transfer Learning for Similar Languages: Experiments with Feature Combinations and Adaptation
Authors: Nianheng Wu, Eric DeMattos, Kwok Him So, Pin-zhen Chen, {\c{C}}a{\u{g}}r{\i} {\c{C}}{\"o}ltekin
Date: 2019-06-01 00:00:00
Proceeding: WS 2019 6
Tasks: Dialect Identification, Language Identification, Transfer Learning

This paper describes the work done by team tearsofjoy participating in the VarDial 2019 Evaluation Campaign. We developed two systems based on Support Vector Machines: SVM with a flat combination of features and SVM ensembles. We participated in all language/dialect identification tasks, as well as the Moldavian vs. Romanian cross-dialect topic identification (MRC) task. Our team achieved first place in German Dialect identification (GDI) and MRC subtasks 2 and 3, second place in the simplified variant of Discriminating between Mainland and Taiwan variation of Mandarin Chinese (DMT) as well as Cuneiform Language Identification (CLI), and third and fifth place in DMT traditional and MRC subtask 1 respectively. In most cases, the SVM with a flat combination of features performed better than SVM ensembles. Besides describing the systems and the results obtained by them, we provide a tentative comparison between the feature combination methods, and present additional experiments with a method of adaptation to the test set, which may indicate potential pitfalls with some of the data sets.
Variation between Different Discourse Types: Literate vs. Oral
Authors: Katrin Ortmann, Stefanie Dipper
Date: 2019-06-01 00:00:00
Proceeding: WS 2019 6
Tasks: Sentence

This paper deals with the automatic identification of literate and oral discourse in German texts. A range of linguistic features is selected and their role in distinguishing between literate- and oral-oriented registers is investigated, using a decision-tree classifier. It turns out that all of the investigated features are related in some way to oral conceptuality. Especially simple measures of complexity (average sentence and word length) are prominent indicators of oral and literate discourse. In addition, features of reference and deixis (realized by different types of pronouns) also prove to be very useful in determining the degree of orality of different registers.
Neural Machine Translation between Myanmar (Burmese) and Rakhine (Arakanese)
Authors: Thazin Myint Oo, Ye Kyaw Thu, Khin Mar Soe
Date: 2019-06-01 00:00:00
Proceeding: WS 2019 6
Tasks: Machine Translation, NMT, Segmentation, Translation, Word Embeddings

This work explores neural machine translation between Myanmar (Burmese) and Rakhine (Arakanese). Rakhine is a language closely related to Myanmar, often considered a dialect. We implemented three prominent neural machine translation (NMT) systems: recurrent neural networks (RNN), transformer, and convolutional neural networks (CNN). The systems were evaluated on a Myanmar-Rakhine parallel text corpus developed by us. In addition, two types of word segmentation schemes for word embeddings were studied: Word-BPE and Syllable-BPE segmentation. Our experimental results clearly show that the highest quality NMT and statistical machine translation (SMT) performances are obtained with Syllable-BPE segmentation for both types of translations. If we focus on NMT, we find that the transformer with Word-BPE segmentation outperforms CNN and RNN for both Myanmar-Rakhine and Rakhine-Myanmar translation. However, CNN with Syllable-BPE segmentation obtains a higher score than the RNN and transformer.
Toward a deep dialectological representation of Indo-Aryan
Authors: Chundra Cathcart
Date: 2019-06-01 00:00:00
Proceeding: WS 2019 6

This paper presents a new approach to disentangling inter-dialectal and intra-dialectal relationships within one such group, the Indo-Aryan subgroup of Indo-European. We draw upon admixture models and deep generative models to tease apart historic language contact and language-specific behavior in the overall patterns of sound change displayed by Indo-Aryan languages. We show that a {``}deep{''} model of Indo-Aryan dialectology sheds some light on questions regarding inter-relationships among the Indo-Aryan languages, and performs better than a {``}shallow{''} model in terms of certain qualities of the posterior distribution (e.g., entropy of posterior distributions), and outline future pathways for model development.
Naive Bayes and BiLSTM Ensemble for Discriminating between Mainland and Taiwan Variation of Mandarin Chinese
Authors: Li Yang, Yang Xiang
Date: 2019-06-01 00:00:00
Proceeding: WS 2019 6
Tasks: Dialect Identification, Language Identification, Word Embeddings

Automatic dialect identification is a more challengingctask than language identification, as it requires the ability to discriminate between varieties of one language. In this paper, we propose an ensemble based system, which combines traditional machine learning models trained on bag of n-gram fetures, with deep learning models trained on word embeddings, to solve the Discriminating between Mainland and Taiwan Variation of Mandarin Chinese (DMT) shared task at VarDial 2019. Our experiments show that a character bigram-trigram combination based Naive Bayes is a very strong model for identifying varieties of Mandarin Chinense. Through further ensemble of Navie Bayes and BiLSTM, our system (team: itsalexyang) achived an macro-averaged F1 score of 0.8530 and 0.8687 in two tracks.
BAM: A combination of deep and shallow models for German Dialect Identification.
Authors: Andrei M. Butnaru
Date: 2019-06-01 00:00:00
Proceeding: WS 2019 6
Tasks: Dialect Identification

*This is a submission for the Third VarDial Evaluation Campaign* In this paper, we present a machine learning approach for the German Dialect Identification (GDI) Closed Shared Task of the DSL 2019 Challenge. The proposed approach combines deep and shallow models, by applying a voting scheme on the outputs resulted from a Character-level Convolutional Neural Networks (Char-CNN), a Long Short-Term Memory (LSTM) network, and a model based on String Kernels. The first model used is the Char-CNN model that merges multiple convolutions computed with kernels of different sizes. The second model is the LSTM network which applies a global max pooling over the returned sequences over time. Both models pass the activation maps to two fully-connected layers. The final model is based on String Kernels, computed on character p-grams extracted from speech transcripts. The model combines two blended kernel functions, one is the presence bits kernel, and the other is the intersection kernel. The empirical results obtained in the shared task prove that the approach can achieve good results. The system proposed in this paper obtained the fourth place with a macro-F1 score of 62.55{\%}
The R2I\_LIS Team Proposes Majority Vote for VarDial's MRC Task
Authors: Adrian-Gabriel Chifu
Date: 2019-06-01 00:00:00
Proceeding: WS 2019 6
Tasks: BIG-bench Machine Learning, Binary Classification, Classification, General Classification

This article presents the model that generated the runs submitted by the R2I{\_}LIS team to the VarDial2019 evaluation campaign, more particularly, to the binary classification by dialect sub-task of the Moldavian vs. Romanian Cross-dialect Topic identification (MRC) task. The team proposed a majority vote-based model, between five supervised machine learning models, trained on forty manually-crafted features. One of the three submitted runs was ranked second at the binary classification sub-task, with a performance of 0.7963, in terms of macro-F1 measure. The other two runs were ranked third and fourth, respectively.
Initial Experiments In Cross-Lingual Morphological Analysis Using Morpheme Segmentation
Authors: Vladislav Mikhailov, Lorenzo Tosi, Anastasia Khorosheva, Oleg Serikov
Date: 2019-06-01 00:00:00
Proceeding: WS 2019 6
Tasks: Decoder, Morphological Analysis

The paper describes initial experiments in data-driven cross-lingual morphological analysis of open-category words using a combination of unsupervised morpheme segmentation, annotation projection and an LSTM encoder-decoder model with attention. Our algorithm provides lemmatisation and morphological analysis generation for previously unseen low-resource language surface forms with only annotated data on the related languages given. Despite the inherently lossy annotation projection, we achieved the best lemmatisation F1-score in the VarDial 2019 Shared Task on Cross-Lingual Morphological Analysis for both Karachay-Balkar (Turkic languages, agglutinative morphology) and Sardinian (Romance languages, fusional morphology).
Neural and Linear Pipeline Approaches to Cross-lingual Morphological Analysis
Authors: {\c{C}}a{\u{g}}r{\i} {\c{C}}{\"o}ltekin, Jeremy Barnes
Date: 2019-06-01 00:00:00
Proceeding: WS 2019 6
Tasks: Morphological Analysis

This paper describes T{\"u}bingen-Oslo team{'}s participation in the cross-lingual morphological analysis task in the VarDial 2019 evaluation campaign. We participated in the shared task with a standard neural network model. Our model achieved analysis F1-scores of 31.48 and 23.67 on test languages Karachay-Balkar (Turkic) and Sardinian (Romance) respectively. The scores are comparable to the scores obtained by the other participants in both language families, and the analysis score on the Romance data set was also the best result obtained in the shared task. Besides describing the system used in our shared task participation, we describe another, simpler, model based on linear classifiers, and present further analyses using both models. Our analyses, besides revealing some of the difficult cases, also confirm that the usefulness of a source language in this task is highly correlated with the similarity of source and target languages.
Ensemble Methods to Distinguish Mainland and Taiwan Chinese
Authors: Hai Hu, Wen Li, He Zhou, Zuoyu Tian, Yiwen Zhang, Liang Zou
Date: 2019-06-01 00:00:00
Proceeding: WS 2019 6
Tasks: Word Embeddings

This paper describes the IUCL system at VarDial 2019 evaluation campaign for the task of discriminating between Mainland and Taiwan variation of mandarin Chinese. We first build several base classifiers, including a Naive Bayes classifier with word n-gram as features, SVMs with both character and syntactic features, and neural networks with pre-trained character/word embeddings. Then we adopt ensemble methods to combine output from base classifiers to make final predictions. Our ensemble models achieve the highest F1 score (0.893) in simplified Chinese track and the second highest (0.901) in traditional Chinese track. Our results demonstrate the effectiveness and robustness of the ensemble methods.
SC-UPB at the VarDial 2019 Evaluation Campaign: Moldavian vs. Romanian Cross-Dialect Topic Identification
Authors: Cristian Onose, Dumitru-Clementin Cercel, Stefan Trausan-Matu
Date: 2019-06-01 00:00:00
Proceeding: WS 2019 6
Tasks: Binary Classification, General Classification, Multi-class Classification

This paper describes our models for the Moldavian vs. Romanian Cross-Topic Identification (MRC) evaluation campaign, part of the VarDial 2019 workshop. We focus on the three subtasks for MRC: binary classification between the Moldavian (MD) and the Romanian (RO) dialects and two cross-dialect multi-class classification between six news topics, MD to RO and RO to MD. We propose several deep learning models based on long short-term memory cells, Bidirectional Gated Recurrent Unit (BiGRU) and Hierarchical Attention Networks (HAN). We also employ three word embedding models to represent the text as a low dimensional vector. Our official submission includes two runs of the BiGRU and HAN models for each of the three subtasks. The best submitted model obtained the following macro-averaged F1 scores: 0.708 for subtask 1, 0.481 for subtask 2 and 0.480 for the last one. Due to a read error caused by the quoting behaviour over the test file, our final submissions contained a smaller number of items than expected. More than 50{\%} of the submission files were corrupted. Thus, we also present the results obtained with the corrected labels for which the HAN model achieves the following results: 0.930 for subtask 1, 0.590 for subtask 2 and 0.687 for the third one.
Discriminating between Mandarin Chinese and Swiss-German varieties using adaptive language models
Authors: Tommi Jauhiainen, Krister Lind{\'e}n, Heidi Jauhiainen
Date: 2019-06-01 00:00:00
Proceeding: WS 2019 6
Tasks: Dialect Identification, Language Identification, Language Modeling, Language Modelling

This paper describes the language identification systems used by the SUKI team in the Discriminating between the Mainland and Taiwan variation of Mandarin Chinese (DMT) and the German Dialect Identification (GDI) shared tasks which were held as part of the third VarDial Evaluation Campaign. The DMT shared task included two separate tracks, one for the simplified Chinese script and one for the traditional Chinese script. We submitted three runs on both tracks of the DMT task as well as on the GDI task. We won the traditional Chinese track using Naive Bayes with language model adaptation, came second on GDI with an adaptive version of the HeLI 2.0 method, and third on the simplified Chinese track using again the adaptive Naive Bayes.