pwc-archive/papers-with-abstracts huggingface dataset:
Railway Stations Announcement System for Deaf
Authors: Rakesh Kumar, Vishal Goyal, Lalit Goyal
Date: 2020-12-01 00:00:00
Proceeding: ICON 2020 12
Tasks: Machine Translation, Translation

People belonging to hearing-impaired community feels very uncomfortable while travelling or visiting at Railway Stations without the help of human interpreter. Hearing-impaired people are not able to hear any announcements made at Railway Stations like which train heading to which destination. They remain ignorant about the choosing of platform number or counter number without the help of interpreter. Even they cannot find whether train is on time, delayed or cancelled. The Railway Stations Announcement System for Deaf is a rule-based MT developed. It is the first system developed in the domain of public places to translate all the announcements used at Railway Stations into Indian Sign Language (ISL) synthetic animations. The system is developed using Python and Flask Framework. This Machine Translation system accepts announcements in the form of English text as input and produces Indian Sign Language (ISL) synthetic animations as output.
Automatic Translation of Complex English Sentences to Indian Sign Language Synthetic Video Animations
Authors: Deepali Goyal, Vishal Goyal, Lalit Goyal
Date: 2020-12-01 00:00:00
Proceeding: ICON 2020 12
Tasks: LEMMA, Sentence, Translation

Sign Language is the natural way of expressing thoughts and feelings for the deaf community. Sign language is a diagrammatic and non-verbal language used by the impaired community to communicate their feeling to their lookalike one. Today we live in the era of technological development, owing to which instant communication is quite easy but even then, a lot of work needs to be done in the field of Sign language automation to improve the quality of life among the deaf community. The traditional approaches used for representing the signs are in the form of videos or text that are expensive, time-consuming, and are not easy to use. In this research work, an attempt is made for the conversion of Complex and Compound English sentences to Indian Sign Language (ISL) using synthetic video animations. The translation architecture includes a parsing module that parses the input complex or compound English sentences to their simplified versions by using complex to simple and compound to simple English grammar rules respectively. The simplified sentence is then forwarded to the conversion segment that rearranges the words of the English language into its corresponding ISL using the devised grammar rules. The next segment constitutes the removal of unwanted words or stop words. This segment gets an input sentence generated by ISL grammar rules. Unwanted or unnecessary words are eliminated by this segment. This removal is important because ISL needs only a meaningful sentence rather than unnecessary usage of linking verbs, helping verbs, and so on. After parsing through the eliminator segment, the sentence is sent to the concordance segment. This segment checks each word in the sentence and translates them into their respective lemma. Lemma is the basic requiring node of each word because sign language makes use of basic words irrespective of other languages that make use of gerund, suffixes, three forms of verbs, different kinds of nouns, adjectives, pronouns in their sentence theory. All the words of the sentence are checked in the lexicon which contains the English word with its HamNoSys notation and the words that are not in the lexicon are replaced by their synonym. The words of the sentence are replaced by their counter HamNoSys code. In case the word is not present in the lexicon, the HamNoSys code will be taken for each alphabet of the word in sequence. The HamNoSys code is converted into the SiGML tags (a form of XML tags) and these SiGML tags are then sent to the animation module which converts the SiGML code into the synthetic animation using avatar (computer-generated animation character).
Plagiarism Detection Tool for Indian Languages with Special focus on Hindi and Punjabi
Authors: Vishal Goyal, Rajeev Puri, Jitesh Pubreja, Jaswinder Singh
Date: 2020-12-01 00:00:00
Proceeding: ICON 2020 12
Tasks: Sentence

Plagiarism is closely linked with Intellectual Property Rights and Copyrights laws, both of which have been formed to protect the ownership of the concept. Most of the available tools for detecting plagiarism when tested with sample Punjabi text, failed to recognise the Punjabi text and the ones, which supported Punjabi text, did a simple string comparison for detecting the suspected copy-paste plagiarism, ignoring the other forms of plagiarism such as word switching, synonym replacement and sentence switching etc.
Neighbor Contextual Information Learners for Joint Intent and Slot Prediction
Authors: Bharatram Natarajan, Gaurav Mathur, Sameer Jain
Date: 2020-12-01 00:00:00
Proceeding: ICON 2020 12
Tasks: Natural Language Understanding

Intent Identification and Slot Identification aretwo important task for Natural Language Understanding(NLU). Exploration in this areahave gained significance using networks likeRNN, LSTM and GRU. However, modelscontaining the above modules are sequentialin nature, which consumes lot of resourceslike memory to train the model in cloud itself.With the advent of many voice assistantsdelivering offline solutions for manyapplications, there is a need for finding replacementfor such sequential networks. Explorationin self-attention, CNN modules hasgained pace in the recent times. Here we exploreCNN based models like Trellis and modifiedthe architecture to make it bi-directionalwith fusion techniques. In addition, we proposeCNN with Self Attention network calledNeighbor Contextual Information Projector usingMulti Head Attention (NCIPMA) architecture.These architectures beat state of the art inopen source datasets like ATIS, SNIPS.
Unified Multi Intent Order and Slot Prediction using Selective Learning Propagation
Authors: Bharatram Natarajan, Priyank Chhipa, Kritika Yadav, Divya Verma Gogoi
Date: 2020-12-01 00:00:00
Proceeding: ICON 2020 12
Tasks: Intent Detection, Natural Language Understanding, slot-filling, Slot Filling

Natural Language Understanding (NLU) involves two important task namely Intent Determination(ID) and Slot Filling (SF). With recent advancements in Intent Determination and Slot Filling tasks, explorations on handling of multiple intent information in a single utterance is increasing to make the NLU more conversation-based rather than command execution-based. Many have proven this task with huge multi-intent training data. In addition, lots of research have addressed multi intent problem only. The problem of multi intent also poses the challenge of addressing the order of execution of intents found. Hence, we are proposing a unified architecture to address multi-intent detection, associated slotsdetection and order of execution of found intents using low proportion multi-intent corpusin the training data. This architecture consists of Multi Word Importance relation propagator using Multi-Head GRU and Importance learner propagator module using self-attention. This architecture has beaten state-of-the-art by 2.58% on the MultiIntentData dataset.
Named Entity Popularity Determination using Ensemble Learning
Authors: Vikram Karthikeyan, B Shrikara Varna, Amogha Hegde, Govind Satwani, Shambhavi B R, Jayarekha P, Ranjan Samal
Date: 2020-12-01 00:00:00
Proceeding: ICON 2020 12
Tasks: Ensemble Learning, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER

Determining the popularity of a Named Entity after completion of Named Entity Recognition (NER) task finds many applications. This work studies Named Entities of Music and Movie domains and solves the problem considering relevant 11 features. Decision Trees and Random Forests approaches were applied on the dataset and the latter algorithm resulted in acceptable accuracy.
Optimized Web-Crawling of Conversational Data from Social Media and Context-Based Filtering
Authors: Annapurna P Patil, Rajarajeswari Subramanian, Gaurav Karkal, Keerthana Purushotham, Jugal Wadhwa, K Dhanush Reddy, Meer Sawood
Date: 2020-12-01 00:00:00
Proceeding: ICON 2020 12

Building Chabot’s requires a large amount of conversational data. In this paper, a web crawler is designed to fetch multi-turn dialogues from websites such as Twitter, YouTube and Reddit in the form of a JavaScript Object Notation (JSON) file. Tools like Twitter Application Programming Interface (API), LXML Library, and JSON library are used to crawl Twitter, YouTube and Reddit to collect conversational chat data. The data obtained in a raw form cannot be used directly as it will have only text metadata such as author or name, time to provide more information on the chat data being scraped. The data collected has to be formatted for proper use case and the JSON library of python allows us to format the data easily. The scraped dialogues are further filtered based on the context of a search keyword without introducing bias and with flexible strictness of classification.
Findings of the VarDial Evaluation Campaign 2021
Authors: Bharathi Raja Chakravarthi, Gaman Mihaela, Radu Tudor Ionescu, Heidi Jauhiainen, Tommi Jauhiainen, Krister Lindén, Nikola Ljubešić, Niko Partanen, Ruba Priyadharshini, Christoph Purschke, Eswari Rajagopal, Yves Scherrer, Marcos Zampieri
Date: 2021-04-01 00:00:00
Proceeding: EACL (VarDial) 2021 4
Tasks: Dialect Identification, Language Identification

This paper describes the results of the shared tasks organized as part of the VarDial Evaluation Campaign 2021. The campaign was part of the eighth workshop on Natural Language Processing (NLP) for Similar Languages, Varieties and Dialects (VarDial), co-located with EACL 2021. Four separate shared tasks were included this year: Dravidian Language Identification (DLI), Romanian Dialect Identification (RDI), Social Media Variety Geolocation (SMG), and Uralic Language Identification (ULI). DLI was organized for the first time and the other three continued a series of tasks from previous evaluation campaigns.
Regression Analysis of Lexical and Morpho-Syntactic Properties of Kiezdeutsch
Authors: Diego Frassinelli, Gabriella Lapesa, Reem Alatrash, Dominik Schlechtweg, Sabine Schulte im Walde
Date: 2021-04-01 00:00:00
Proceeding: EACL (VarDial) 2021 4
Tasks: feature selection, regression

Kiezdeutsch is a variety of German predominantly spoken by teenagers from multi-ethnic urban neighborhoods in casual conversations with their peers. In recent years, the popularity of Kiezdeutsch has increased among young people, independently of their socio-economic origin, and has spread in social media, too. While previous studies have extensively investigated this language variety from a linguistic and qualitative perspective, not much has been done from a quantitative point of view. We perform the first large-scale data-driven analysis of the lexical and morpho-syntactic properties of Kiezdeutsch in comparison with standard German. At the level of results, we confirm predictions of previous qualitative analyses and integrate them with further observations on specific linguistic phenomena such as slang and self-centered speaker attitude. At the methodological level, we provide logistic regression as a framework to perform bottom-up feature selection in order to quantify differences across language varieties.
Whit’s the Richt Pairt o Speech: PoS tagging for Scots
Authors: Harm Lameris, Sara Stymne
Date: 2021-04-01 00:00:00
Proceeding: EACL (VarDial) 2021 4
Tasks: POS, POS Tagging, Transfer Learning

In this paper we explore PoS tagging for the Scots language. Scots is spoken in Scotland and Northern Ireland, and is closely related to English. As no linguistically annotated Scots data were available, we manually PoS tagged a small set that is used for evaluation and training. We use English as a transfer language to examine zero-shot transfer and transfer learning methods. We find that training on a very small amount of Scots data was superior to zero-shot transfer from English. Combining the Scots and English data led to further improvements, with a concatenation method giving the best results. We also compared the use of two different English treebanks and found that a treebank containing web data was superior in the zero-shot setting, while it was outperformed by a treebank containing a mix of genres when combined with Scots data.
Efficient Unsupervised NMT for Related Languages with Cross-Lingual Language Models and Fidelity Objectives
Authors: Rami Aly, Andrew Caines, Paula Buttery
Date: 2021-04-01 00:00:00
Proceeding: EACL (VarDial) 2021 4
Tasks: Denoising, Language Modeling, Language Modelling, Machine Translation, NMT, Translation, Unsupervised Machine Translation

The most successful approach to Neural Machine Translation (NMT) when only monolingual training data is available, called unsupervised machine translation, is based on back-translation where noisy translations are generated to turn the task into a supervised one. However, back-translation is computationally very expensive and inefficient. This work explores a novel, efficient approach to unsupervised NMT. A transformer, initialized with cross-lingual language model weights, is fine-tuned exclusively on monolingual data of the target language by jointly learning on a paraphrasing and denoising autoencoder objective. Experiments are conducted on WMT datasets for German-English, French-English, and Romanian-English. Results are competitive to strong baseline unsupervised NMT models, especially for closely related source languages (German) compared to more distant ones (Romanian, French), while requiring about a magnitude less training time.
Fine-tuning Distributional Semantic Models for Closely-Related Languages
Authors: Kushagra Bhatia, Divyanshu Aggarwal, Ashwini Vaidya
Date: 2021-04-01 00:00:00
Proceeding: EACL (VarDial) 2021 4
Tasks: Word Similarity

In this paper we compare the performance of three models: SGNS (skip-gram negative sampling) and augmented versions of SVD (singular value decomposition) and PPMI (Positive Pointwise Mutual Information) on a word similarity task. We particularly focus on the role of hyperparameter tuning for Hindi based on recommendations made in previous work (on English). Our results show that there are language specific preferences for these hyperparameters. We extend the best settings for Hindi to a set of related languages: Punjabi, Gujarati and Marathi with favourable results. We also find that a suitably tuned SVD model outperforms SGNS for most of our languages and is also more robust in a low-resource setting.
Naive Bayes-based Experiments in Romanian Dialect Identification
Authors: Tommi Jauhiainen, Heidi Jauhiainen, Krister Lindén
Date: 2021-04-01 00:00:00
Proceeding: EACL (VarDial) 2021 4
Tasks: Dialect Identification

This article describes the experiments and systems developed by the SUKI team for the second edition of the Romanian Dialect Identification (RDI) shared task which was organized as part of the 2021 VarDial Evaluation Campaign. We submitted two runs to the shared task and our second submission was the overall best submission by a noticeable margin. Our best submission used a character n-gram based naive Bayes classifier with adaptive language models. We describe our experiments on the development set leading to both submissions.
Optimizing a Supervised Classifier for a Difficult Language Identification Problem
Authors: Yves Bestgen
Date: 2021-04-01 00:00:00
Proceeding: EACL (VarDial) 2021 4
Tasks: Language Identification, regression

This paper describes the system developed by the Laboratoire d’analyse statistique des textes for the Dravidian Language Identification (DLI) shared task of VarDial 2021. This task is particularly difficult because the materials consists of short YouTube comments, written in Roman script, from three closely related Dravidian languages, and a fourth category consisting of several other languages in varying proportions, all mixed with English. The proposed system is made up of a logistic regression model which uses as only features n-grams of characters with a maximum length of 5. After its optimization both in terms of the feature weighting and the classifier parameters, it ranked first in the challenge. The additional analyses carried out underline the importance of optimization, especially when the measure of effectiveness is the Macro-F1.
Comparing the Performance of CNNs and Shallow Models for Language Identification
Authors: Andrea Ceolin
Date: 2021-04-01 00:00:00
Proceeding: EACL (VarDial) 2021 4
Tasks: Dialect Identification, Language Identification

In this work we compare the performance of convolutional neural networks and shallow models on three out of the four language identification shared tasks proposed in the VarDial Evaluation Campaign 2021. In our experiments, convolutional neural networks and shallow models yielded comparable performance in the Romanian Dialect Identification (RDI) and the Dravidian Language Identification (DLI) shared tasks, after the training data was augmented, while an ensemble of support vector machines and Naïve Bayes models was the best performing model in the Uralic Language Identification (ULI) task. While the deep learning models did not achieve state-of-the-art performance at the tasks and tended to overfit the data, the ensemble method was one of two methods that beat the existing baseline for the first track of the ULI shared task.
Dialect Identification through Adversarial Learning and Knowledge Distillation on Romanian BERT
Authors: George-Eduard Zaharia, Andrei-Marius Avram, Dumitru-Clementin Cercel, Traian Rebedea
Date: 2021-04-01 00:00:00
Proceeding: EACL (VarDial) 2021 4
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Dialect Identification, Knowledge Distillation, Opinion Mining, speech-recognition, Speech Recognition

Dialect identification is a task with applicability in a vast array of domains, ranging from automatic speech recognition to opinion mining. This work presents our architectures used for the VarDial 2021 Romanian Dialect Identification subtask. We introduced a series of solutions based on Romanian or multilingual Transformers, as well as adversarial training techniques. At the same time, we experimented with a knowledge distillation tool in order to check whether a smaller model can maintain the performance of our best approach. Our best solution managed to obtain a weighted F1-score of 0.7324, allowing us to obtain the 2nd place on the leaderboard.
N-gram and Neural Models for Uralic Language Identification: NRC at VarDial 2021
Authors: Gabriel Bernier-Colborne, Serge Leger, Cyril Goutte
Date: 2021-04-01 00:00:00
Proceeding: EACL (VarDial) 2021 4
Tasks: Language Identification

We describe the systems developed by the National Research Council Canada for the Uralic language identification shared task at the 2021 VarDial evaluation campaign. We evaluated two different approaches to this task: a probabilistic classifier exploiting only character 5-grams as features, and a character-based neural network pre-trained through self-supervision, then fine-tuned on the language identification task. The former method turned out to perform better, which casts doubt on the usefulness of deep learning methods for language identification, where they have yet to convincingly and consistently outperform simpler and less costly classification algorithms exploiting n-gram features.
Social Media Variety Geolocation with geoBERT
Authors: Yves Scherrer, Nikola Ljubešić
Date: 2021-04-01 00:00:00
Proceeding: EACL (VarDial) 2021 4
Tasks: regression

This paper describes the Helsinki–Ljubljana contribution to the VarDial 2021 shared task on social media variety geolocation. Following our successful participation at VarDial 2020, we again propose constrained and unconstrained systems based on the BERT architecture. In this paper, we report experiments with different tokenization settings and different pre-trained models, and we contrast our parameter-free regression approach with various classification schemes proposed by other participants at VarDial 2020. Both the code and the best-performing pre-trained models are made freely available.
FINDINGS OF THE IWSLT 2021 EVALUATION CAMPAIGN
Authors: Antonios Anastasopoulos, Ondřej Bojar, Jacob Bremerman, Roldano Cattoni, Maha Elbayad, Marcello Federico, Xutai Ma, Satoshi Nakamura, Matteo Negri, Jan Niehues, Juan Pino, Elizabeth Salesky, Sebastian Stüker, Katsuhito Sudoh, Marco Turchi, Alexander Waibel, Changhan Wang, Matthew Wiesner
Date: 2021-08-01 00:00:00
Proceeding: ACL (IWSLT) 2021 8
Tasks: Translation

The evaluation campaign of the International Conference on Spoken Language Translation (IWSLT 2021) featured this year four shared tasks: (i) Simultaneous speech translation, (ii) Offline speech translation, (iii) Multilingual speech translation, (iv) Low-resource speech translation. A total of 22 teams participated in at least one of the tasks. This paper describes each shared task, data and evaluation metrics, and reports results of the received submissions.
NAIST English-to-Japanese Simultaneous Translation System for IWSLT 2021 Simultaneous Text-to-text Task
Authors: Ryo Fukuda, Yui Oka, Yasumasa Kano, Yuki Yano, Yuka Ko, Hirotaka Tokuyama, Kosuke Doi, Sakriani Sakti, Katsuhito Sudoh, Satoshi Nakamura
Date: 2021-08-01 00:00:00
Proceeding: ACL (IWSLT) 2021 8
Tasks: Knowledge Distillation, Machine Translation, Translation

This paper describes NAIST’s system for the English-to-Japanese Simultaneous Text-to-text Translation Task in IWSLT 2021 Evaluation Campaign. Our primary submission is based on wait-k neural machine translation with sequence-level knowledge distillation to encourage literal translation.
The University of Edinburgh’s Submission to the IWSLT21 Simultaneous Translation Task
Authors: Sukanta Sen, Ulrich Germann, Barry Haddow
Date: 2021-08-01 00:00:00
Proceeding: ACL (IWSLT) 2021 8
Tasks: Language Modeling, Language Modelling, Machine Translation, NMT, Sentence, Translation

We describe our submission to the IWSLT 2021 shared task on simultaneous text-to-text English-German translation. Our system is based on the re-translation approach where the agent re-translates the whole source prefix each time it receives a new source token. This approach has the advantage of being able to use a standard neural machine translation (NMT) inference engine with beam search, however, there is a risk that incompatibility between successive re-translations will degrade the output. To improve the quality of the translations, we experiment with various approaches: we use a fixed size wait at the beginning of the sentence, we use a language model score to detect translatable units, and we apply dynamic masking to determine when the translation is unstable. We find that a combination of dynamic masking and language model score obtains the best latency-quality trade-off.
Without Further Ado: Direct and Simultaneous Speech Translation by AppTek in 2021
Authors: Parnia Bahar, Patrick Wilken, Mattia A. Di Gangi, Evgeny Matusov
Date: 2021-08-01 00:00:00
Proceeding: ACL (IWSLT) 2021 8
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Data Augmentation, Machine Translation, speech-recognition, Speech Recognition, Translation

This paper describes the offline and simultaneous speech translation systems developed at AppTek for IWSLT 2021. Our offline ST submission includes the direct end-to-end system and the so-called posterior tight integrated model, which is akin to the cascade system but is trained in an end-to-end fashion, where all the cascaded modules are end-to-end models themselves. For simultaneous ST, we combine hybrid automatic speech recognition with a machine translation approach whose translation policy decisions are learned from statistical word alignments. Compared to last year, we improve general quality and provide a wider range of quality/latency trade-offs, both due to a data augmentation method making the MT model robust to varying chunk sizes. Finally, we present a method for ASR output segmentation into sentences that introduces a minimal additional delay.