pwc-archive/papers-with-abstracts huggingface dataset:
Multimodal Neural Machine Translation System for English to Bengali
Authors: Shantipriya Parida, Subhadarshi Panda, Satya Prakash Biswal, Ketan Kotwal, Arghyadeep Sen, Satya Ranjan Dash, Petr Motlicek
Date: 2021-09-01 00:00:00
Proceeding: MMTLRL (RANLP) 2021 9
Tasks: Machine Translation, Multimodal Machine Translation, Translation

Multimodal Machine Translation (MMT) systems utilize additional information from other modalities beyond text to improve the quality of machine translation (MT). The additional modality is typically in the form of images. Despite proven advantages, it is indeed difficult to develop an MMT system for various languages primarily due to the lack of a suitable multimodal dataset. In this work, we develop an MMT for English-> Bengali using a recently published Bengali Visual Genome (BVG) dataset that contains images with associated bilingual textual descriptions. Through a comparative study of the developed MMT system vis-a-vis a Text-to-text translation, we demonstrate that the use of multimodal data not only improves the translation performance improvement in BLEU score of +1.3 on the development set, +3.9 on the evaluation test, and +0.9 on the challenge test set but also helps to resolve ambiguities in the pure text description. As per best of our knowledge, our English-Bengali MMT system is the first attempt in this direction, and thus, can act as a baseline for the subsequent research in MMT for low resource languages.
Experiences of Adapting Multimodal Machine Translation Techniques for Hindi
Authors: Baban Gain, Dibyanayan Bandyopadhyay, Asif Ekbal
Date: 2021-09-01 00:00:00
Proceeding: MMTLRL (RANLP) 2021 9
Tasks: Machine Translation, Multimodal Machine Translation, Sentence, Translation

Multimodal Neural Machine Translation (MNMT) is an interesting task in natural language processing (NLP) where we use visual modalities along with a source sentence to aid the source to target translation process. Recently, there has been a lot of works in MNMT frameworks to boost the performance of standalone Machine Translation tasks. Most of the prior works in MNMT tried to perform translation between two widely known languages (e.g. English-to-German, English-to-French ). In this paper, We explore the effectiveness of different state-of-the-art MNMT methods, which use various data oriented techniques including multimodal pre-training, for low resource languages. Although the existing methods works well on high resource languages, usability of those methods on low-resource languages is unknown. In this paper, we evaluate the existing methods on Hindi and report our findings.
MultiLexNorm: A Shared Task on Multilingual Lexical Normalization
Authors: Rob van der Goot, Alan Ramponi, Arkaitz Zubiaga, Barbara Plank, Benjamin Muller, Iñaki San Vicente Roncal, Nikola Ljubešić, Özlem Çetinoğlu, Rahmad Mahendra, Talha Çolakoğlu, Timothy Baldwin, Tommaso Caselli, Wladimir Sidorenko
Date: 2021-11-01 00:00:00
Proceeding: EMNLP (WNUT) 2021 11
Tasks: Dependency Parsing, Lexical Normalization, Part-Of-Speech Tagging, POS

Lexical normalization is the task of transforming an utterance into its standardized form. This task is beneficial for downstream analysis, as it provides a way to harmonize (often spontaneous) linguistic variation. Such variation is typical for social media on which information is shared in a multitude of ways, including diverse languages and code-switching. Since the seminal work of Han and Baldwin (2011) a decade ago, lexical normalization has attracted attention in English and multiple other languages. However, there exists a lack of a common benchmark for comparison of systems across languages with a homogeneous data and evaluation setup. The MultiLexNorm shared task sets out to fill this gap. We provide the largest publicly available multilingual lexical normalization benchmark including 13 language variants. We propose a homogenized evaluation setup with both intrinsic and extrinsic evaluation. As extrinsic evaluation, we use dependency parsing and part-of-speech tagging with adapted evaluation metrics (a-LAS, a-UAS, and a-POS) to account for alignment discrepancies. The shared task hosted at W-NUT 2021 attracted 9 participants and 18 submissions. The results show that neural normalization systems outperform the previous state-of-the-art system by a large margin. Downstream parsing and part-of-speech tagging performance is positively affected but to varying degrees, with improvements of up to 1.72 a-LAS, 0.85 a-UAS, and 1.54 a-POS for the winning system.
CL-MoNoise: Cross-lingual Lexical Normalization
Authors: Rob van der Goot
Date: 2021-11-01 00:00:00
Proceeding: EMNLP (WNUT) 2021 11
Tasks: Lexical Normalization

Social media is notoriously difficult to process for existing natural language processing tools, because of spelling errors, non-standard words, shortenings, non-standard capitalization and punctuation. One method to circumvent these issues is to normalize input data before processing. Most previous work has focused on only one language, which is mostly English. In this paper, we are the first to propose a model for cross-lingual normalization, with which we participate in the WNUT 2021 shared task. To this end, we use MoNoise as a starting point, and make a simple adaptation for cross-lingual application. Our proposed model outperforms the leave-as-is baseline provided by the organizers which copies the input. Furthermore, we explore a completely different model which converts the task to a sequence labeling task. Performance of this second system is low, as it does not take capitalization into account in our implementation.
BPoMP: The Benchmark of Poetic Minimal Pairs – Limericks, Rhyme, and Narrative Coherence
Authors: Almas Abdibayev, Allen Riddell, Daniel Rockmore
Date: 2021-09-01 00:00:00
Proceeding: RANLP 2021 9
Tasks: Language Model Evaluation, Language Modelling

We adapt BLiMP (Benchmark of Linguistic Minimal Pairs) language model evaluation framework to the context of poetry, introducing the first of a series of tasks titled Benchmark of Poetic Minimal Pairs (BPoMP). The tasks presented herein use one genre of English-language poetry, the limerick (five-lines, rhyme scheme AABBA). Following the BLiMP schema, the BPoMP tasks use 10,000 minimal pairs of limerick/corrupted limerick. The latter is created by (1) shuffling two rhyming end-of-the-line words, (2) shuffling two rhyming lines, (3) replacing end-of-the-line word by a non-rhyming synonym. Our general task is detection of the original limerick, which we believe tests a language model’s capacity to utilize “end rhymes”, a common feature of poetry. We evaluate Transformer-based models by checking if they assign a higher probability to the non-corrupted limerick in each minimal pair. We find that the models identify the original limerick at rates better than chance, but with a nontrivial gap relative to human accuracy (average of 98.3% across tasks). The publicly available curated set of limericks accompanying this paper is an additional contribution. In general, we see this as a first step to create a community of NLP activity around the rigorous computational study of poetry.
Ontology Population Reusing Resources for Dialogue Intent Detection: Generic and Multilingual Approach
Authors: Cristina Aceta, Izaskun Fernández, Aitor Soroa
Date: 2021-09-01 00:00:00
Proceeding: RANLP 2021 9
Tasks: Intent Detection, Management

This work presents a generic semi-automatic strategy to populate the domain ontology of an ontology-driven task-oriented dialogue system, with the aim of performing successful intent detection in the dialogue process, reusing already existing multilingual resources. This semi-automatic approach allows ontology engineers to exploit available resources so as to associate the potential situations in the use case to FrameNet frames and obtain the relevant lexical units associated to them in the target language, following lexical and semantic criteria, without linguistic expert knowledge. This strategy has been validated and evaluated in two use cases, from industrial scenarios, for interaction in Spanish with a guide robot and with a Computerized Maintenance Management System (CMMS). In both cases, this method has allowed the ontology engineer to instantiate the domain ontology with the intent-relevant information with quality data in a simple and low-resource-consuming manner.
Efficient Multilingual Text Classification for Indian Languages
Authors: Salil Aggarwal, Sourav Kumar, Radhika Mamidi
Date: 2021-09-01 00:00:00
Proceeding: RANLP 2021 9
Tasks: Classification, Multilingual text classification, text-classification, Text Classification, Zero-Shot Text Classification

India is one of the richest language hubs on the earth and is very diverse and multilingual. But apart from a few Indian languages, most of them are still considered to be resource poor. Since most of the NLP techniques either require linguistic knowledge that can only be developed by experts and native speakers of that language or they require a lot of labelled data which is again expensive to generate, the task of text classification becomes challenging for most of the Indian languages. The main objective of this paper is to see how one can benefit from the lexical similarity found in Indian languages in a multilingual scenario. Can a classification model trained on one Indian language be reused for other Indian languages? So, we performed zero-shot text classification via exploiting lexical similarity and we observed that our model performs best in those cases where the vocabulary overlap between the language datasets is maximum. Our experiments also confirm that a single multilingual model trained via exploiting language relatedness outperforms the baselines by significant margins.
Domain Adaptation for Hindi-Telugu Machine Translation Using Domain Specific Back Translation
Authors: Hema Ala, Vandan Mujadia, Dipti Sharma
Date: 2021-09-01 00:00:00
Proceeding: RANLP 2021 9
Tasks: Domain Adaptation, Machine Translation, Translation

In this paper, we present a novel approachfor domain adaptation in Neural MachineTranslation which aims to improve thetranslation quality over a new domain.Adapting new domains is a highly challeng-ing task for Neural Machine Translation onlimited data, it becomes even more diffi-cult for technical domains such as Chem-istry and Artificial Intelligence due to spe-cific terminology, etc. We propose DomainSpecific Back Translation method whichuses available monolingual data and gen-erates synthetic data in a different way.This approach uses Out Of Domain words.The approach is very generic and can beapplied to any language pair for any do-main. We conduct our experiments onChemistry and Artificial Intelligence do-mains for Hindi and Telugu in both direc-tions. It has been observed that the usageof synthetic data created by the proposedalgorithm improves the BLEU scores significantly.
English-Arabic Cross-language Plagiarism Detection
Authors: Naif Alotaibi, Mike Joy
Date: 2021-09-01 00:00:00
Proceeding: RANLP 2021 9
Tasks: Machine Translation, Sentence, Translation, Word Alignment

The advancement of the web and information technology has contributed to the rapid growth of digital libraries and automatic machine translation tools which easily translate texts from one language into another. These have increased the content accessible in different languages, which results in easily performing translated plagiarism, which are referred to as “cross-language plagiarism”. Recognition of plagiarism among texts in different languages is more challenging than identifying plagiarism within a corpus written in the same language. This paper proposes a new technique for enhancing English-Arabic cross-language plagiarism detection at the sentence level. This technique is based on semantic and syntactic feature extraction using word order, word embedding and word alignment with multilingual encoders. Those features, and their combination with different machine learning (ML) algorithms, are then used in order to aid the task of classifying sentences as either plagiarized or non-plagiarized. The proposed approach has been deployed and assessed using datasets presented at SemEval-2017. Analysis of experimental data demonstrates that utilizing extracted features and their combinations with various ML classifiers achieves promising results.
Towards a Better Understanding of Noise in Natural Language Processing
Authors: Khetam Al Sharou, Zhenhao Li, Lucia Specia
Date: 2021-09-01 00:00:00
Proceeding: RANLP 2021 9

In this paper, we propose a definition and taxonomy of various types of non-standard textual content – generally referred to as “noise” – in Natural Language Processing (NLP). While data pre-processing is undoubtedly important in NLP, especially when dealing with user-generated content, a broader understanding of different sources of noise and how to deal with them is an aspect that has been largely neglected. We provide a comprehensive list of potential sources of noise, categorise and describe them, and show the impact of a subset of standard pre-processing strategies on different tasks. Our main goal is to raise awareness of non-standard content – which should not always be considered as “noise” – and of the need for careful, task-dependent pre-processing. This is an alternative to blanket, all-encompassing solutions generally applied by researchers through “standard” pre-processing pipelines. The intention is for this categorisation to serve as a point of reference to support NLP researchers in devising strategies to clean, normalise or embrace non-standard content.
Comparing Supervised Machine Learning Techniques for Genre Analysis in Software Engineering Research Articles
Authors: Felipe Araújo de Britto, Thiago castro Ferreira, Leonardo Pereira Nunes, Fernando Silva Parreiras
Date: 2021-09-01 00:00:00
Proceeding: RANLP 2021 9
Tasks: Articles, BIG-bench Machine Learning, regression

Written communication is of utmost importance to the progress of scientific research. The speed of such development, however, may be affected by the scarcity of reviewers to referee the quality of research articles. In this context, automatic approaches that are able to query linguistic segments in written contributions by detecting the presence or absence of common rhetorical patterns have become a necessity. This paper aims to compare supervised machine learning techniques tested to accomplish genre analysis in Introduction sections of software engineering articles. A semi-supervised approach was carried out to augment the number of annotated sentences in SciSents (Avaliable on: ANONYMOUS). Two supervised approaches using SVM and logistic regression were undertaken to assess the F-score for genre analysis in the corpus. A technique based on logistic regression and BERT has been found to perform genre analysis highly satisfactorily with an average of 88.25 on F-score when retrieving patterns at an overall level.
A Multi-Pass Sieve Coreference Resolution for Indonesian
Authors: Valentina Kania Prameswara Artari, Rahmad Mahendra, Meganingrum Arista Jiwanggi, Adityo Anggraito, Indra Budi
Date: 2021-09-01 00:00:00
Proceeding: RANLP 2021 9
Tasks: coreference-resolution, Coreference Resolution

Coreference resolution is an NLP task to find out whether the set of referring expressions belong to the same concept in discourse. A multi-pass sieve is a deterministic coreference model that implements several layers of sieves, where each sieve takes a pair of correlated mentions from a collection of non-coherent mentions. The multi-pass sieve is based on the principle of high precision, followed by increased recall in each sieve. In this work, we examine the portability of the multi-pass sieve coreference resolution model to the Indonesian language. We conduct the experiment on 201 Wikipedia documents and the multi-pass sieve system yields 72.74% of MUC F-measure and 52.18% of BCUBED F-measure.
Solving SCAN Tasks with Data Augmentation and Input Embeddings
Authors: Michal Auersperger, Pavel Pecina
Date: 2021-09-01 00:00:00
Proceeding: RANLP 2021 9
Tasks: Data Augmentation

We address the compositionality challenge presented by the SCAN benchmark. Using data augmentation and a modification of the standard seq2seq architecture with attention, we achieve SOTA results on all the relevant tasks from the benchmark, showing the models can generalize to words used in unseen contexts. We propose an extension of the benchmark by a harder task, which cannot be solved by the proposed method.
TEASER: Towards Efficient Aspect-based SEntiment Analysis and Recognition
Authors: Vaibhav Bajaj, Kartikey Pant, Ishan Upadhyay, Srinath Nair, Radhika Mamidi
Date: 2021-09-01 00:00:00
Proceeding: RANLP 2021 9
Tasks: Aspect-Based Sentiment Analysis, Aspect-Based Sentiment Analysis (ABSA), Sentence, Sentiment Analysis

Sentiment analysis aims to detect the overall sentiment, i.e., the polarity of a sentence, paragraph, or text span, without considering the entities mentioned and their aspects. Aspect-based sentiment analysis aims to extract the aspects of the given target entities and their respective sentiments. Prior works formulate this as a sequence tagging problem or solve this task using a span-based extract-then-classify framework where first all the opinion targets are extracted from the sentence, and then with the help of span representations, the targets are classified as positive, negative, or neutral. The sequence tagging problem suffers from issues like sentiment inconsistency and colossal search space. Whereas, Span-based extract-then-classify framework suffers from issues such as half-word coverage and overlapping spans. To overcome this, we propose a similar span-based extract-then-classify framework with a novel and improved heuristic. Experiments on the three benchmark datasets (Restaurant14, Laptop14, Restaurant15) show our model consistently outperforms the current state-of-the-art. Moreover, we also present a novel supervised movie reviews dataset (Movie20) and a pseudo-labeled movie reviews dataset (moviesLarge) made explicitly for this task and report the results on the novel Movie20 dataset as well.
Interactive Learning Approach for Arabic Target-Based Sentiment Analysis
Authors: Husamelddin Balla, Marisa Llorens Salvador, Sarah Jane Delany
Date: 2021-09-01 00:00:00
Proceeding: RANLP 2021 9
Tasks: Sentence, Sentiment Analysis

Recently, the majority of sentiment analysis researchers focus on target-based sentiment analysis because it delivers in-depth analysis with more accurate results as compared to traditional sentiment analysis. In this paper, we propose an interactive learning approach to tackle a target-based sentiment analysis task for the Arabic language. The proposed IA-LSTM model uses an interactive attention-based mechanism to force the model to focus on different parts (targets) of a sentence. We investigate the ability to use targets, right, and left context, and model them separately to learn their own representations via interactive modeling. We evaluated our model on two different datasets: Arabic hotel review and Arabic book review datasets. The results demonstrate the effectiveness of using this interactive modeling technique for the Arabic target-based task. The model obtained accuracy values of 83.10 compared to SOTA models such as AB-LSTM-PC which obtained 82.60 for the same dataset.
Litescale: A Lightweight Tool for Best-worst Scaling Annotation
Authors: Valerio Basile, Christian Cagnazzo
Date: 2021-09-01 00:00:00
Proceeding: RANLP 2021 9

Best-worst Scaling (BWS) is a methodology for annotation based on comparing and ranking instances, rather than classifying or scoring individual instances. Studies have shown the efficacy of this methodology applied to NLP tasks in terms of a higher quality of the datasets produced by following it. In this system demonstration paper, we present Litescale, a free software library to create and manage BWS annotation tasks. Litescale computes the tuples to annotate, manages the users and the annotation process, and creates the final gold standard. The functionalities of Litescale can be accessed programmatically through a Python module, or via two alternative user interfaces, a textual console-based one and a graphical Web-based one. We further developed and deployed a fully online version of Litescale complete with multi-user support.
Probabilistic Ensembles of Zero- and Few-Shot Learning Models for Emotion Classification
Authors: Angelo Basile, Guillermo Pérez-Torró, Marc Franco-Salvador
Date: 2021-09-01 00:00:00
Proceeding: RANLP 2021 9
Tasks: Emotion Classification, Few-Shot Learning

Emotion Classification is the task of automatically associating a text with a human emotion. State-of-the-art models are usually learned using annotated corpora or rely on hand-crafted affective lexicons. We present an emotion classification model that does not require a large annotated corpus to be competitive. We experiment with pretrained language models in both a zero-shot and few-shot configuration. We build several of such models and consider them as biased, noisy annotators, whose individual performance is poor. We aggregate the predictions of these models using a Bayesian method originally developed for modelling crowdsourced annotations. Next, we show that the resulting system performs better than the strongest individual model. Finally, we show that when trained on few labelled data, our systems outperform fully-supervised models.
Cross-Lingual Wolastoqey-English Definition Modelling
Authors: Diego Bear, Paul Cook
Date: 2021-09-01 00:00:00
Proceeding: RANLP 2021 9
Tasks: Definition Modelling

Definition modelling is the task of automatically generating a dictionary-style definition given a target word. In this paper, we consider cross-lingual definition generation. Specifically, we generate English definitions for Wolastoqey (Malecite-Passamaquoddy) words. Wolastoqey is an endangered, low-resource polysynthetic language. We hypothesize that sub-word representations based on byte pair encoding (Sennrich et al., 2016) can be leveraged to represent morphologically-complex Wolastoqey words and overcome the challenge of not having large corpora available for training. Our experimental results demonstrate that this approach outperforms baseline methods in terms of BLEU score. ﻿
Neural Network-Based Generation of Sport Summaries: A Preliminary Study
Authors: David Stéphane Belemkoabga, Aurélien Bossard, Abdallah Essa, Christophe Rodrigues, Kévin Sylla
Date: 2021-09-01 00:00:00
Proceeding: RANLP 2021 9

This paper presents a global summarization method for live sport commentaries for which we have a human-written summary available. This method is based on a neural generative summarizer. The amount of data available for training is limited compared to corpora commonly used by neural summarizers. We propose to help the summarizer to learn from a limited amount of data by limiting the entropy of the input texts. This step is performed by a classification into categories derived by a detailed analysis of the human-written summaries. We show that the filtering helps the summarization system to overcome the lack of resources. However, several improving points have emerged from this preliminary study, that we discuss and plan to implement in future work.