pwc-archive/papers-with-abstracts huggingface dataset:
Overcoming Resistance: The Normalization of an Amazonian Tribal Language
Authors: John E Ortega, Richard Alexander Castro-Mamani, Jaime Rafael Montoya Samame
Date: 2020-12-01 00:00:00
Proceeding: loresmt (AACL) 2020 12
Tasks: Articles, Machine Translation, Segmentation, Translation

Languages can be considered endangered for many reasons. One of the principal reasons for endangerment is the disappearance of its speakers. Another, more identifiable reason, is the lack of written resources. We present an automated sub-segmentation system called AshMorph that deals with the morphology of an Amazonian tribal language called Ashaninka which is at risk of being endangered due to the lack of availability (or resistance) of native speakers and the absence of written resources. We show that by the use of a cross-lingual lexicon and finite state transducers we can increase accuracy by more than 30% when compared to other modern sub-segmentation tools. Our results, made freely available on-line, are verified by an Ashaninka speaker and perform well in two distinct domains, everyday literary articles and the bible. This research serves as a first step in helping to preserve Ashaninka by offering a sub-segmentation process that can be used to normalize any Ashaninka text which will serve as input to a machine translation system for translation into other high-resource languages spoken by higher populated locations like Spanish and Portuguese in the case of Peru and Brazil where Ashaninka is mostly spoken.
Bridging Philippine Languages With Multilingual Neural Machine Translation
Authors: Renz Iver Baliber, Charibeth Cheng, Kristine Mae Adlaon, Virgion Mamonong
Date: 2020-12-01 00:00:00
Proceeding: loresmt (AACL) 2020 12
Tasks: Machine Translation, NMT, Translation

The Philippines is home to more than 150 languages that is considered to be low-resourced even on its major languages. This results into a lack of pursuit in developing a translation system for the underrepresented languages. To simplify the process of developing translation system for multiple languages, and to aid in improving the translation quality of zero to low-resource languages, multilingual NMT became an active area of research. However, existing works in multilingual NMT disregards the analysis of a multilingual model on a closely related and low-resource language group in the context of pivot-based translation and zero-shot translation. In this paper, we benchmarked translation for several Philippine Languages, provided an analysis of a multilingual NMT system for morphologically rich and low-resource languages in terms of its effectiveness in translating zero-resource languages with zero-shot translations. To further evaluate the capability of the multilingual NMT model in translating unseen language pairs in training, we tested the model to translate between Tagalog and Cebuano and compared its performance with a simple NMT model that is directly trained on a parallel Tagalog and Cebuano data in which we showed that zero-shot translation outperforms a directly trained model in some instances, while utilizing English as a pivot language in translating outperform both approaches.
Findings of the LoResMT 2020 Shared Task on Zero-Shot for Low-Resource languages
Authors: Atul Kr. Ojha, Valentin Malykh, Alina Karakanta, Chao-Hong Liu
Date: 2020-12-01 00:00:00
Proceeding: loresmt (AACL) 2020 12
Tasks: Domain Adaptation, Machine Translation, Translation

This paper presents the findings of the LoResMT 2020 Shared Task on zero-shot translation for low resource languages. This task was organised as part of the 3rd Workshop on Technologies for MT of Low Resource Languages (LoResMT) at AACL-IJCNLP 2020. The focus was on the zero-shot approach as a notable development in Neural Machine Translation to build MT systems for language pairs where parallel corpora are small or even non-existent. The shared task experience suggests that back-translation and domain adaptation methods result in better accuracy for small-size datasets. We further noted that, although translation between similar languages is no cakewalk, linguistically distinct languages require more data to give better results.
Zero-Shot Neural Machine Translation: Russian-Hindi @LoResMT 2020
Authors: Sahinur Rahman Laskar, Abdullah Faiz Ur Rahman Khilji, Partha Pakray, Sivaji Bandyopadhyay
Date: 2020-12-01 00:00:00
Proceeding: loresmt (AACL) 2020 12
Tasks: Machine Translation, NMT, Text Generation, Translation

Neural machine translation (NMT) is a widely accepted approach in the machine translation (MT) community, translating from one natural language to another natural language. Although, NMT shows remarkable performance in both high and low resource languages, it needs sufficient training corpus. The availability of a parallel corpus in low resource language pairs is one of the challenging tasks in MT. To mitigate this issue, NMT attempts to utilize a monolingual corpus to get better at translation for low resource language pairs. Workshop on Technologies for MT of Low Resource Languages (LoResMT 2020) organized shared tasks of low resource language pair translation using zero-shot NMT. Here, the parallel corpus is not used and only monolingual corpora is allowed. We have participated in the same shared task with our team name CNLP-NITS for the Russian-Hindi language pair. We have used masked sequence to sequence pre-training for language generation (MASS) with only monolingual corpus following the unsupervised NMT architecture. The evaluated results are declared at the LoResMT 2020 shared task, which reports that our system achieves the bilingual evaluation understudy (BLEU) score of 0.59, precision score of 3.43, recall score of 5.48, F-measure score of 4.22, and rank-based intuitive bilingual evaluation score (RIBES) of 0.180147 in Russian to Hindi translation. And for Hindi to Russian translation, we have achieved BLEU, precision, recall, F-measure, and RIBES score of 1.11, 4.72, 4.41, 4.56, and 0.026842 respectively.
Unsupervised Approach for Zero-Shot Experiments: Bhojpuri–Hindi and Magahi–Hindi@LoResMT 2020
Authors: Amit Kumar, Rajesh Kumar Mundotiya, Anil Kumar Singh
Date: 2020-12-01 00:00:00
Proceeding: loresmt (AACL) 2020 12
Tasks: Domain Adaptation, Machine Translation, Translation, Unsupervised Domain Adaptation

This paper reports a Machine Translation (MT) system submitted by the NLPRL team for the Bhojpuri–Hindi and Magahi–Hindi language pairs at LoResMT 2020 shared task. We used an unsupervised domain adaptation approach that gives promising results for zero or extremely low resource languages. Task organizers provide the development and the test sets for evaluation and the monolingual data for training. Our approach is a hybrid approach of domain adaptation and back-translation. Metrics used to evaluate the trained model are BLEU, RIBES, Precision, Recall and F-measure. Our approach gives relatively promising results, with a wide range, of 19.5, 13.71, 2.54, and 3.16 BLEU points for Bhojpuri to Hindi, Magahi to Hindi, Hindi to Bhojpuri and Hindi to Magahi language pairs, respectively.
Zero-shot translation among Indian languages
Authors: Rudali Huidrom, Yves Lepage
Date: 2020-12-01 00:00:00
Proceeding: loresmt (AACL) 2020 12
Tasks: Machine Translation, NMT, Sentence, Translation

Standard neural machine translation (NMT) allows a model to perform translation between a pair of languages. Multilingual neural machine translation (NMT), on the other hand, allows a model to perform translation between several language pairs, even between language pairs for which no sentences pair has been seen during training (zero-shot translation). This paper presents experiments with zero-shot translation on low resource Indian languages with a very small amount of data for each language pair. We first report results on balanced data over all considered language pairs. We then expand our experiments for additional three rounds by increasing the training data with 2,000 sentence pairs in each round for some of the language pairs. We obtain an increase in translation accuracy with its balanced data settings score multiplied by 7 for Manipuri to Hindi during Round-III of zero-shot translation.
Improving Multilingual Neural Machine Translation For Low-Resource Languages: French, English - Vietnamese
Authors: Thi-Vinh Ngo, Phuong-Thai Nguyen, Thanh-Le Ha, Khac-Quy Dinh, Le-Minh Nguyen
Date: 2020-12-01 00:00:00
Proceeding: loresmt (AACL) 2020 12
Tasks: Machine Translation, Translation, Word Similarity

Prior works have demonstrated that a low-resource language pair can benefit from multilingual machine translation (MT) systems, which rely on many language pairs’ joint training. This paper proposes two simple strategies to address the rare word issue in multilingual MT systems for two low-resource language pairs: French-Vietnamese and English-Vietnamese. The first strategy is about dynamical learning word similarity of tokens in the shared space among source languages while another one attempts to augment the translation ability of rare words through updating their embeddings during the training. Besides, we leverage monolingual data for multilingual MT systems to increase the amount of synthetic parallel corpora while dealing with the data sparsity problem. We have shown significant improvements of up to +1.62 and +2.54 BLEU points over the bilingual baseline systems for both language pairs and released our datasets for the research community.
EnAsCorp1.0: English-Assamese Corpus
Authors: Sahinur Rahman Laskar, Abdullah Faiz Ur Rahman Khilji, Partha Pakray, Sivaji Bandyopadhyay
Date: 2020-12-01 00:00:00
Proceeding: loresmt (AACL) 2020 12
Tasks: Machine Translation, Translation

The corpus preparation is one of the important challenging task for the domain of machine translation especially in low resource language scenarios. Country like India where multiple languages exists, machine translation attempts to minimize the communication gap among people with different linguistic backgrounds. Although Google Translation covers automatic translation of various languages all over the world but it lags in some languages including Assamese. In this paper, we have developed EnAsCorp1.0, corpus of English-Assamese low resource pair where parallel and monolingual data are collected from various online sources. We have also implemented baseline systems with statistical machine translation and neural machine translation approaches for the same corpus.
Unsupervised Neural Machine Translation for English and Manipuri
Authors: Salam Michael Singh, Thoudam Doren Singh
Date: 2020-12-01 00:00:00
Proceeding: loresmt (AACL) 2020 12
Tasks: Denoising, Machine Translation, Translation

Availability of bitext dataset has been a key challenge in the conventional machine translation system which requires surplus amount of parallel data. In this work, we devise an unsupervised neural machine translation (UNMT) system consisting of a transformer based shared encoder and language specific decoders using denoising autoencoder and backtranslation with an additional Manipuri side multiple test reference. We report our work on low resource setting for English (en) - Manipuri (mni) language pair and attain a BLEU score of 3.1 for en-mni and 2.7 for mni-en respectively. Subjective evaluation on translated output gives encouraging findings.
Effective Architectures for Low Resource Multilingual Named Entity Transliteration
Authors: Molly Moran, Constantine Lignos
Date: 2020-12-01 00:00:00
Proceeding: loresmt (AACL) 2020 12
Tasks: Decoder, Transliteration

In this paper, we evaluate LSTM, biLSTM, GRU, and Transformer architectures for the task of name transliteration in a many-to-one multilingual paradigm, transliterating from 590 languages to English. We experiment with different encoder-decoder combinations and evaluate them using accuracy, character error rate, and an F-measure based on longest continuous subsequences. We find that using a Transformer for the encoder and decoder performs best, improving accuracy by over 4 points compared to previous work. We explore whether manipulating the source text by adding macrolanguage flag tokens or pre-romanizing source strings can improve performance and find that neither manipulation has a positive effect. Finally, we analyze performance differences between the LSTM and Transformer encoders when using a Transformer decoder and find that the Transformer encoder is better able to handle insertions and substitutions when transliterating.
Investigating Low-resource Machine Translation for English-to-Tamil
Authors: Akshai Ramesh, Venkatesh Balavadhani parthasa, Rejwanul Haque, Andy Way
Date: 2020-12-01 00:00:00
Proceeding: loresmt (AACL) 2020 12
Tasks: Machine Translation, NMT, Translation

Statistical machine translation (SMT) which was the dominant paradigm in machine translation (MT) research for nearly three decades has recently been superseded by the end-to-end deep learning approaches to MT. Although deep neural models produce state-of-the-art results in many translation tasks, they are found to under-perform on resource-poor scenarios. Despite some success, none of the present-day benchmarks that have tried to overcome this problem can be regarded as a universal solution to the problem of translation of many low-resource languages. In this work, we investigate the performance of phrase-based SMT (PB-SMT) and neural MT (NMT) on a rarely-tested low-resource language-pair, English-to-Tamil, taking a specialised data domain (software localisation) into consideration. In particular, we produce rankings of our MT systems via a social media platform-based human evaluation scheme, and demonstrate our findings in the low-resource domain-specific text translation task.
On Universal Colexifications
Authors: Hongchang Bao, Bradley Hauer, Grzegorz Kondrak
Date: 2021-01-01 00:00:00
Proceeding: EACL (GWC) 2021 1

Colexification occurs when two distinct concepts are lexified by the same word. The term covers both polysemy and homonymy. We posit and investigate the hypothesis that no pair of concepts are colexified in every language. We test our hypothesis by analyzing colexification data from BabelNet, Open Multilingual WordNet, and CLICS. The results show that our hypothesis is supported by over 99.9% of colexified concept pairs in these three lexical resources.
UZWORDNET: A Lexical-Semantic Database for the Uzbek Language
Authors: Alessandro Agostini, Timur Usmanov, Ulugbek Khamdamov, Nilufar Abdurakhmonova, Mukhammadsaid Mamasaidov
Date: 2021-01-01 00:00:00
Proceeding: EACL (GWC) 2021 1
Tasks: Lexical Analysis

The results reported in this paper aim to increase the presence of the Uzbek language in the Internet and its usability within IT applications. We describe the initial development of a “word-net” for the Uzbek language compatible to Princeton WordNet. We called it UZWORDNET. In the current version, UZWORDNET contains 28140 synsets, 64389 sense and 20683 words; its estimated accuracy is 75.98%. To the best of our knowledge, it is the largest wordnet for Uzbek existing to date, and the second wordnet developed overall.
Practical Approach on Implementation of WordNets for South African Languages
Authors: Tshephisho Joseph Sefara, Tumisho Billson Mokgonyane, Vukosi Marivate
Date: 2021-01-01 00:00:00
Proceeding: EACL (GWC) 2021 1

This paper proposes the implementation of WordNets for five South African languages, namely, Sepedi, Setswana, Tshivenda, isiZulu and isiXhosa to be added to open multilingual WordNets (OMW) on natural language toolkit (NLTK). The African WordNets are converted from Princeton WordNet (PWN) 2.0 to 3.0 to match the synsets in PWN 3.0. After conversion, there were 7157, 11972, 1288, 6380, and 9460 lemmas for Sepedi, Setswana, Tshivenda, isiZulu and isiX- hosa respectively. Setswana, isiXhosa, Sepedi contains more lemmas compared to 8 languages in OMW and isiZulu contains more lemmas compared to 7 languages in OMW. A library has been published for continuous development of African WordNets in OMW using NLTK.
Homonymy and Polysemy Detection with Multilingual Information
Authors: Amir Ahmad Habibi, Bradley Hauer, Grzegorz Kondrak
Date: 2021-01-01 00:00:00
Proceeding: EACL (GWC) 2021 1
Tasks: Translation

Deciding whether a semantically ambiguous word is homonymous or polysemous is equivalent to establishing whether it has any pair of senses that are semantically unrelated. We present novel methods for this task that leverage information from multilingual lexical resources. We formally prove the theoretical properties that provide the foundation for our methods. In particular, we show how the One Homonym Per Translation hypothesis of Hauer and Kondrak (2020a) follows from the synset properties formulated by Hauer and Kondrak (2020b). Experimental evaluation shows that our approach sets a new state of the art for homonymy detection.
Taboo Wordnet
Authors: Francis Bond, Merrick Yeu Herng Choo
Date: 2021-01-01 00:00:00
Proceeding: EACL (GWC) 2021 1

This paper describes the development of an online lexical resource to help detection systems regulate and curb the use of offensive words online. With the growing prevalence of social media platforms, many conversations are now conducted on- line. The increase of online conversations for leisure, work and socializing has led to an increase in harassment. In particular, we create a specialized sense-based vocabulary of Japanese offensive words for the Open Multilingual Wordnet. This vocabulary expands on an existing list of Japanese offen- sive words and provides categorization and proper linking to synsets within the multilingual wordnet. This paper then discusses the evaluation of the vocabulary as a resource for representing and classifying offensive words and as a possible resource for offensive word use detection in social media.
Ask2Transformers: Zero-Shot Domain labelling with Pretrained Language Models
Authors: Oscar Sainz, German Rigau
Date: 2021-01-01 00:00:00
Proceeding: EACL (GWC) 2021 1
Tasks: Domain Labelling

In this paper we present a system that exploits different pre-trained Language Models for assigning domain labels to WordNet synsets without any kind of supervision. Furthermore, the system is not restricted to use a particular set of domain labels. We exploit the knowledge encoded within different off-the-shelf pre-trained Language Models and task formulations to infer the domain label of a particular WordNet definition. The proposed zero-shot system achieves a new state-of-the-art on the English dataset used in the evaluation.
Discriminating Homonymy from Polysemy in Wordnets: English, Spanish and Polish Nouns
Authors: Arkadiusz Janz, Marek Maziarz
Date: 2021-01-01 00:00:00
Proceeding: EACL (GWC) 2021 1
Tasks: LEMMA, regression

We propose a novel method of homonymy-polysemy discrimination for three Indo-European Languages (English, Spanish and Polish). Support vector machines and LASSO logistic regression were successfully used in this task, outperforming baselines. The feature set utilised lemma properties, gloss similarities, graph distances and polysemy patterns. The proposed ML models performed equally well for English and the other two languages (constituting testing data sets). The algorithms not only ruled out most cases of homonymy but also were efficacious in distinguishing between closer and indirect semantic relatedness.
Implementing ASLNet V1.0: Progress and Plans
Authors: Colin Lualdi, Elaine Wright, Jack Hudson, Naomi Caselli, Christiane Fellbaum
Date: 2021-01-01 00:00:00
Proceeding: EACL (GWC) 2021 1

We report on the development of ASLNet, a wordnet for American Sign Language (ASL). ASLNet V1.0 is currently under construction by mapping easy-to-translate ASL lexical nouns to Princeton WordNet synsets. We describe our data model and mapping approach, which can be extended to any sign language. Analysis of the 390 synsets processed to date indicates the success of our procedure yet also highlights the need to supplement our mapping with the “merge” method. We outline our plans for upcoming work to remedy this, which include use of ASL free-association data.
Monolingual Word Sense Alignment as a Classification Problem
Authors: Sina Ahmadi, John P. McCrae
Date: 2021-01-01 00:00:00
Proceeding: EACL (GWC) 2021 1
Tasks: Classification, Relationship Detection, Representation Learning

Words are defined based on their meanings in various ways in different resources. Aligning word senses across monolingual lexicographic resources increases domain coverage and enables integration and incorporation of data. In this paper, we explore the application of classification methods using manually-extracted features along with representation learning techniques in the task of word sense alignment and semantic relationship detection. We demonstrate that the performance of classification methods dramatically varies based on the type of semantic relationships due to the nature of the task but outperforms the previous experiments.
Extraction of Common-Sense Relations from Procedural Task Instructions using BERT
Authors: Viktor Losing, Lydia Fischer, Jörg Deigmöller
Date: 2021-01-01 00:00:00
Proceeding: EACL (GWC) 2021 1
Tasks: Common Sense Reasoning

Manipulation-relevant common-sense knowledge is crucial to support action-planning for complex tasks. In particular, instrumentality information of what can be done with certain tools can be used to limit the search space which is growing exponentially with the number of viable options. Typical sources for such knowledge, structured common-sense knowledge bases such as ConceptNet or WebChild, provide a limited amount of information which also varies drastically across different domains. Considering the recent success of pre-trained language models such as BERT, we investigate whether common-sense information can directly be extracted from semi-structured text with an acceptable annotation effort. Concretely, we compare the common-sense relations obtained from ConceptNet versus those extracted with BERT from large recipe databases. In this context, we propose a scoring function, based on the WordNet taxonomy to match specific terms to more general ones, enabling a rich evaluation against a set of ground-truth relations.
The GlobalWordNet Formats: Updates for 2020
Authors: John P. McCrae, Michael Wayne Goodman, Francis Bond, Alexandre Rademaker, Ewa Rudnicka, Luis Morgado Da Costa
Date: 2021-01-01 00:00:00
Proceeding: EACL (GWC) 2021 1

The Global Wordnet Formats have been introduced to enable wordnets to have a common representation that can be integrated through the Global WordNet Grid. As a result of their adoption, a number of shortcomings of the format were identified, and in this paper we describe the extensions to the formats that address these issues. These include: ordering of senses, dependencies between wordnets, pronunciation, syntactic modelling, relations, sense keys, metadata and RDF support. Furthermore, we provide some perspectives on how these changes help in the integration of wordnets.
Intrinsically Interlingual: The Wn Python Library for Wordnets
Authors: Michael Wayne Goodman, Francis Bond
Date: 2021-01-01 00:00:00
Proceeding: EACL (GWC) 2021 1

This paper introduces Wn, a new Python library for working with wordnets. Unlike previous libraries, Wn is built from the beginning to accommodate multiple wordnets — for multiple languages or multiple versions of the same wordnet — while retaining the ability to query and traverse them independently. It is also able to download and incorporate wordnets published online. These features are made possible through Wn’s adoption of standard formats and methods for interoperability, namely the WN-LMF schema (Vossen et al., 2013; Bond et al., 2020) and the Collaborative Interlingual Index (Bond et al., 2016). Wn is open-source, easily available, and well-documented.