pwc-archive/papers-with-abstracts huggingface dataset:
Developing a Part-Of-Speech tagger for te reo Māori
Authors: Aoife Finn, Peter-Lucas Jones, Keoni Mahelona, Suzanne Duncan, Gianna Leoni
Date: 2022-05-01 00:00:00
Proceeding: ComputEL (ACL) 2022 5
Tasks: POS, TAG

This paper discusses the development of a Part-of-Speech tagger for te reo Māori which is the Indigenous language of Aotearoa, also known as New Zealand, see Morrison. Henceforth, Part-of-Speech will be referred to as POS throughout this paper and te reo Māori will be referred to as Māori, while Universal Dependencies will be referred to as UD. Prior to the development of this tagger, there was no POS tagger for Māori from Aotearoa. POS taggers tag words according to their syntactic or grammatical category. However, many traditional syntactic categories, and by consequence POS labels, do not “work for” Māori. By this we mean that, for some of the traditional categories, The definition of, or guidelines for, an existing category is not suitable for Māori. They do not have an existing category for certain word classes of Māori. They do not reflect a Māori worldview of the Māori language. We wanted a tagset that is usable with industry-wide tools, but we also needed a tagset that would meet the needs of Māori. Therefore, we based our tagset and guidelines on the UD tagset and tagging conventions, however the categorization of words has been significantly altered to be appropriate for Māori. This is because at the time of development of our POS tagger, the UD conventions had still not been used to tag a Polyneisan language such as Māori, nor did it provide any guidelines about how to tag them. To that end, we worked with highly-proficient, specially-selected Māori speakers and linguists who are specialists in Māori. This has ensured that our POS labels and guidelines conventions faithfully reflect a Māori speaker’s conceptualization of their language.
Challenges and Perspectives for Innu-Aimun within Indigenous Language Technologies
Authors: Antoine Cadotte, Tan Le Ngoc, Mathieu Boivin, Fatiha Sadat
Date: 2022-05-01 00:00:00
Proceeding: ComputEL (ACL) 2022 5
Tasks: Machine Translation

Innu-Aimun is an Algonquian language spoken in Eastern Canada. It is the language of the Innu, an Indigenous people that now lives for the most part in a dozen communities across Quebec and Labrador. Although it is alive, Innu-Aimun sees important preservation and revitalization challenges and issues. The state of its technology is still nascent, with very few existing applications. This paper proposes a first survey of the available linguistic resources and existing technology for Innu-Aimun. Considering the existing linguistic and textual resources, we argue that developing language technology is feasible and propose first steps towards NLP applications like machine translation. The goal of developing such technologies is first and foremost to help efforts in improving language transmission and cultural safety and preservation for Innu-Aimun speakers, as those are considered urgent and vital issues. Finally, we discuss the importance of close collaboration and consultation with the Innu community in order to ensure that language technologies are developed respectfully and in accordance with that goal.
Using Speech and NLP Resources to build an iCALL platform for a minority language, the story of An Scéalaí, the Irish experience to date
Authors: Neasa Ní Chiaráin, Oisín Nolan, Madeleine Comtois, Neimhin Robinson Gunning, Harald Berthelsen, Ailbhe Ni Chasaide
Date: 2022-05-01 00:00:00
Proceeding: ComputEL (ACL) 2022 5
Tasks: Language Acquisition

This paper describes how emerging linguistic resources and technologies can be used to build a language learning platform for Irish, an endangered language. This platform, An Scéalaí, harvests learner corpora - a vital resource both to study the stages of learners’ language acquisition and to guide future platform development. A technical description of the platform is provided, including details of how different speech technologies and linguistic resources are fused to provide a holistic learner experience. The active continuous participation of the community, and platform evaluations by learners and teachers, are discussed.
Closing the NLP Gap Documentary Linguistics and NLP Need a Shared Software Infrastructure
Authors: Luke Gessler
Date: 2022-05-01 00:00:00
Proceeding: ComputEL (ACL) 2022 5

For decades, researchers in natural language processing and computational linguistics have been developing models and algorithms that aim to serve the needs of language documentation projects. However, these models have seen little use in language documentation despite their great potential for making documentary linguistic artefacts better and easier to produce. In this work, we argue that a major reason for this NLP gap is the lack of a strong foundation of application software which can on the one hand serve the complex needs of language documentation and on the other hand provide effortless integration with NLP models. We further present and describe a work-in-progress system we have developed to serve this need, Glam.
Can We Use Word Embeddings for Enhancing Guarani-Spanish Machine Translation?
Authors: Santiago Góngora, Nicolás Giossa, Luis Chiruzzo
Date: 2022-05-01 00:00:00
Proceeding: ComputEL (ACL) 2022 5
Tasks: Machine Translation, Translation, Word Embeddings

Machine translation for low-resource languages, such as Guarani, is a challenging task due to the lack of data. One way of tackling it is using pretrained word embeddings for model initialization. In this work we try to check if currently available data is enough to train rich embeddings for enhancing MT for Guarani and Spanish, by building a set of word embedding collections and training MT systems using them. We found that the trained vectors are strong enough to slightly improve the performance of some of the translation models and also to speed up the training convergence.
Faoi Gheasa an adaptive game for Irish language learning
Authors: Liang Xu, Elaine Uí Dhonnchadha, Monica Ward
Date: 2022-05-01 00:00:00
Proceeding: ComputEL (ACL) 2022 5

In this paper, we present a game with a purpose (GWAP) (Von Ahn 2006). The aim of the game is to promote language learning and ‘noticing’ (Skehan, 2013). The game has been designed for Irish, but the framework could be used for other languages. Irish is a minority language which means that L2 learners have limited opportunities for exposure to the language, and additionally, there are also limited (digital) learning resources available. This research incorporates game development, language pedagogy and ICALL language materials development. This paper will focus on the language materials development as this is a bottleneck in the teaching and learning of minority and endangered languages.
Using Graph-Based Methods to Augment Online Dictionaries of Endangered Languages
Authors: Khalid Alnajjar, Mika Hämäläinen, Niko Tapio Partanen, Jack Rueter
Date: 2022-05-01 00:00:00
Proceeding: ComputEL (ACL) 2022 5

Many endangered Uralic languages have multilingual machine readable dictionaries saved in an XML format. However, the dictionaries cover translations very inconsistently between language pairs, for instance, the Livonian dictionary has some translations to Finnish, Latvian and Estonian, and the Komi-Zyrian dictionary has some translations to Finnish, English and Russian. We utilize graph-based approaches to augment such dictionaries by predicting new translations to existing and new languages based on different dictionaries for endangered languages and Wiktionaries. Our study focuses on the lexical resources for Komi-Zyrian (kpv), Erzya (myv) and Livonian (liv). We evaluate our approach by human judges fluent in the three endangered languages in question. Based on the evaluation, the method predicted good or acceptable translations 77% of the time. Furthermore, we train a neural prediction model to predict the quality of the automatically predicted translations with an 81% accuracy. The resulting extensions to the dictionaries are made available on the online dictionary platform used by the speakers of these languages.
Reusing a Multi-lingual Setup to Bootstrap a Grammar Checker for a Very Low Resource Language without Data
Authors: Inga Lill Sigga Mikkelsen, Linda Wiechetek, Flammie A Pirinen
Date: 2022-05-01 00:00:00
Proceeding: ComputEL (ACL) 2022 5

Grammar checkers (GEC) are needed for digital language survival. Very low resource languages like Lule Sámi with less than 3,000 speakers need to hurry to build these tools, but do not have the big corpus data that are required for the construction of machine learning tools. We present a rule-based tool and a workflow where the work done for a related language can speed up the process. We use an existing grammar to infer rules for the new language, and we do not need a large gold corpus of annotated grammar errors, but a smaller corpus of regression tests is built while developing the tool. We present a test case for Lule Sámi reusing resources from North Sámi, show how we achieve a categorisation of the most frequent errors, and present a preliminary evaluation of the system. We hope this serves as an inspiration for small languages that need advanced tools in a limited amount of time, but do not have big data.
A Word-and-Paradigm Workflow for Fieldwork Annotation
Authors: Maria Copot, Sara Court, Noah Diewald, Stephanie Antetomaso, Micha Elsner
Date: 2022-05-01 00:00:00
Proceeding: ComputEL (ACL) 2022 5
Tasks: Active Learning, Segmentation

There are many challenges in morphological fieldwork annotation, it heavily relies on segmentation and feature labeling (which have both practical and theoretical drawbacks), it’s time-intensive, and the annotator needs to be linguistically trained and may still annotate things inconsistently. We propose a workflow that relies on unsupervised and active learning grounded in Word-and-Paradigm morphology (WP). Machine learning has the potential to greatly accelerate the annotation process and allow a human annotator to focus on problematic cases, while the WP approach makes for an annotation system that is word-based and relational, removing the need to make decisions about feature labeling and segmentation early in the process and allowing speakers of the language of interest to participate more actively, since linguistic training is not necessary. We present a proof-of-concept for the first step of the workflow, in a realistic fieldwork setting, annotators can process hundreds of forms per hour.
Fine-tuning pre-trained models for Automatic Speech Recognition, experiments on a fieldwork corpus of Japhug (Trans-Himalayan family)
Authors: Séverine Guillaume, Guillaume Wisniewski, Cécile Macaire, Guillaume Jacques, Alexis Michaud, Benjamin Galliot, Maximin Coavoux, Solange Rossato, Minh-Châu Nguyên, Maxime Fily
Date: 2022-05-01 00:00:00
Proceeding: ComputEL (ACL) 2022 5
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition

This is a report on results obtained in the development of speech recognition tools intended to support linguistic documentation efforts. The test case is an extensive fieldwork corpus of Japhug, an endangered language of the Trans-Himalayan (Sino-Tibetan) family. The goal is to reduce the transcription workload of field linguists. The method used is a deep learning approach based on the language-specific tuning of a generic pre-trained representation model, XLS-R, using a Transformer architecture. We note difficulties in implementation, in terms of learning stability. But this approach brings significant improvements nonetheless. The quality of phonemic transcription is improved over earlier experiments; and most significantly, the new approach allows for reaching the stage of automatic word recognition. Subjective evaluation of the tool by the author of the training data confirms the usefulness of this approach.
Morphologically annotated corpora of Pomak
Authors: Ritván Jusúf Karahóǧa, Panagiotis G. Krimpas, Vivian Stamou, Vasileios Arampatzakis, Dimitrios Karamatskos, Vasileios Sevetlidis, Nikolaos Constantinides, Nikolaos Kokkas, George Pavlidis, Stella Markantonatou
Date: 2022-05-01 00:00:00
Proceeding: ComputEL (ACL) 2022 5

The project XXXX is developing a platform to enable researchers of living languages to easily create and make available state-of-the-art spoken and textual annotated resources. As a case study we use Greek and Pomak, the latter being an endangered oral Slavic language of the Balkans (including Thrace/Greece). The linguistic documentation of Pomak is an ongoing work by an interdisciplinary team in close cooperation with the Pomak community of Greece. We describe our experience in the development of a Latin-based orthography and morphologically annotated text corpora of Pomak with state-of-the-art NLP technology. These resources will be made openly available on the XXXX site and the gold annotated corpora of Pomak will be made available on the Universal Dependencies treebank repository.
Enhancing Documentation of Hupa with Automatic Speech Recognition
Authors: Zoey Liu, Justin Spence, Emily Tucker Prud’hommeaux
Date: 2022-05-01 00:00:00
Proceeding: ComputEL (ACL) 2022 5
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition

This study investigates applications of automatic speech recognition (ASR) techniques to Hupa, a critically endangered Native American language from the Dene (Athabaskan) language family. Using around 9h12m of spoken data produced by one elder who is a first-language Hupa speaker, we experimented with different evaluation schemes and training settings. On average a fully connected deep neural network reached a word error rate of 35.26%. Our overall results illustrate the utility of ASR for making Hupa language documentation more accessible and usable. In addition, we found that when training acoustic models, using recordings with transcripts that were not carefully verified did not necessarily have a negative effect on model performance. This shows promise for speech corpora of indigenous languages that commonly include transcriptions produced by second-language speakers or linguists who have advanced knowledge in the language of interest.
Joint Entity and Relation Extraction Based on Table Labeling Using Convolutional Neural Networks
Authors: Youmi Ma, Tatsuya Hiraoka, Naoaki Okazaki
Date: 2022-05-01 00:00:00
Proceeding: spnlp (ACL) 2022 5
Tasks: Joint Entity and Relation Extraction, Language Modeling, Language Modelling, Relation, Relation Extraction

This study introduces a novel approach to the joint extraction of entities and relations by stacking convolutional neural networks (CNNs) on pretrained language models. We adopt table representations to model the entities and relations, casting the entity and relation extraction as a table-labeling problem. Regarding each table as an image and each cell in a table as an image pixel, we apply two-dimensional CNNs to the tables to capture local dependencies and predict the cell labels. The experimental results showed that the performance of the proposed method is comparable to those of current state-of-art systems on the CoNLL04, ACE05, and ADE datasets.Even when freezing pretrained language model parameters, the proposed method showed a stable performance, whereas the compared methods suffered from significant decreases in performance. This observation indicates that the parameters of the pretrained encoder may incorporate dependencies among the entity and relation labels during fine-tuning.
TempCaps: A Capsule Network-based Embedding Model for Temporal Knowledge Graph Completion
Authors: Guirong Fu, Zhao Meng, Zhen Han, Zifeng Ding, Yunpu Ma, Matthias Schubert, Volker Tresp, Roger Wattenhofer
Date: 2022-05-01 00:00:00
Proceeding: spnlp (ACL) 2022 5
Tasks: Entity Embeddings, Knowledge Graph Completion, Knowledge Graphs, Temporal Knowledge Graph Completion

Temporal knowledge graphs store the dynamics of entities and relations during a time period. However, typical temporal knowledge graphs often suffer from incomplete dynamics with missing facts in real-world scenarios. Hence, modeling temporal knowledge graphs to complete the missing facts is important. In this paper, we tackle the temporal knowledge graph completion task by proposing TempCaps, which is a Capsule network-based embedding model for Temporal knowledge graph completion. TempCaps models temporal knowledge graphs by introducing a novel dynamic routing aggregator inspired by Capsule Networks. Specifically, TempCaps builds entity embeddings by dynamically routing retrieved temporal relation and neighbor information. Experimental results demonstrate that TempCaps reaches state-of-the-art performance for temporal knowledge graph completion. Additional analysis also shows that TempCaps is efficient.
SlotGAN: Detecting Mentions in Text via Adversarial Distant Learning
Authors: Daniel Daza, Michael Cochez, Paul Groth
Date: 2022-05-01 00:00:00
Proceeding: spnlp (ACL) 2022 5
Tasks: Sentence, valid

We present SlotGAN, a framework for training a mention detection model that only requires unlabeled text and a gazetteer. It consists of a generator trained to extract spans from an input sentence, and a discriminator trained to determine whether a span comes from the generator, or from the gazetteer.We evaluate the method on English newswire data and compare it against supervised, weakly-supervised, and unsupervised methods. We find that the performance of the method is lower than these baselines, because it tends to generate more and longer spans, and in some cases it relies only on capitalization. In other cases, it generates spans that are valid but differ from the benchmark. When evaluated with metrics based on overlap, we find that SlotGAN performs within 95% of the precision of a supervised method, and 84% of its recall. Our results suggest that the model can generate spans that overlap well, but an additional filtering mechanism is required.
Multiplex Anti-Asian Sentiment before and during the Pandemic: Introducing New Datasets from Twitter Mining
Authors: Hao Lin, Pradeep Nalluri, Lantian Li, Yifan Sun, Yongjun Zhang
Date: 2022-05-01 00:00:00
Proceeding: WASSA (ACL) 2022 5
Tasks: Sentiment Analysis

COVID-19 has disproportionately threatened minority communities in the U.S, not only in health but also in societal impact. However, social scientists and policymakers lack critical data to capture the dynamics of the anti-Asian hate trend and to evaluate its scale and scope. We introduce new datasets from Twitter related to anti-Asian hate sentiment before and during the pandemic. Relying on Twitter’s academic API, we retrieve hateful and counter-hate tweets from the Twitter Historical Database. To build contextual understanding and collect related racial cues, we also collect instances of heated arguments, often political, but not necessarily hateful, discussing Chinese issues. We then use the state-of-the-art hate speech classifiers to discern whether these tweets express hatred. These datasets can be used to study hate speech, general anti-Asian or Chinese sentiment, and hate linguistics by social scientists as well as to evaluate and build hate speech or sentiment analysis classifiers by computational scholars.
“splink” is happy and “phrouth” is scary: Emotion Intensity Analysis for Nonsense Words
Authors: Valentino Sabbatino, Enrica Troiano, Antje Schweitzer, Roman Klinger
Date: 2022-05-01 00:00:00
Proceeding: WASSA (ACL) 2022 5

People associate affective meanings to words - “death” is scary and sad while “party” is connotated with surprise and joy. This raises the question if the association is purely a product of the learned affective imports inherent to semantic meanings, or is also an effect of other features of words, e.g., morphological and phonological patterns. We approach this question with an annotation-based analysis leveraging nonsense words. Specifically, we conduct a best-worst scaling crowdsourcing study in which participants assign intensity scores for joy, sadness, anger, disgust, fear, and surprise to 272 non-sense words and, for comparison of the results to previous work, to 68 real words. Based on this resource, we develop character-level and phonology-based intensity regressors. We evaluate them on both nonsense words and real words (making use of the NRC emotion intensity lexicon of 7493 words), across six emotion categories. The analysis of our data reveals that some phonetic patterns show clear differences between emotion intensities. For instance, s as a first phoneme contributes to joy, sh to surprise, p as last phoneme more to disgust than to anger and fear. In the modelling experiments, a regressor trained on real words from the NRC emotion intensity lexicon shows a higher performance (r = 0.17) than regressors that aim at learning the emotion connotation purely from nonsense words. We conclude that humans do associate affective meaning to words based on surface patterns, but also based on similarities to existing words (“juy” to “joy”, or “flike” to “like”).
SentEMO: A Multilingual Adaptive Platform for Aspect-based Sentiment and Emotion Analysis
Authors: Ellen De Geyndt, Orphee De Clercq, Cynthia Van Hee, Els Lefever, Pranaydeep Singh, Olivier Parent, Veronique Hoste
Date: 2022-05-01 00:00:00
Proceeding: WASSA (ACL) 2022 5
Tasks: Aspect-Based Sentiment Analysis, Aspect-Based Sentiment Analysis (ABSA), Emotion Recognition, Sentiment Analysis

In this paper, we present the SentEMO platform, a tool that provides aspect-based sentiment analysis and emotion detection of unstructured text data such as reviews, emails and customer care conversations. Currently, models have been trained for five domains and one general domain and are implemented in a pipeline approach, where the output of one model serves as the input for the next. The results are presented in three dashboards, allowing companies to gain more insights into what stakeholders think of their products and services. The SentEMO platform is available at https://sentemo.ugent.be
Can Emotion Carriers Explain Automatic Sentiment Prediction? A Study on Personal Narratives
Authors: Seyed Mahed Mousavi, Gabriel Roccabruna, Aniruddha Tammewar, Steve Azzolin, Giuseppe Riccardi
Date: 2022-05-01 00:00:00
Proceeding: WASSA (ACL) 2022 5

Deep Neural Networks (DNN) models have achieved acceptable performance in sentiment prediction of written text. However, the output of these machine learning (ML) models cannot be natively interpreted. In this paper, we study how the sentiment polarity predictions by DNNs can be explained and compare them to humans’ explanations. We crowdsource a corpus of Personal Narratives and ask human judges to annotate them with polarity and select the corresponding token chunks - the Emotion Carriers (EC) - that convey narrators’ emotions in the text. The interpretations of ML neural models are carried out through Integrated Gradients method and we compare them with human annotators’ interpretations. The results of our comparative analysis indicate that while the ML model mostly focuses on the explicit appearance of emotions-laden words (e.g. happy, frustrated), the human annotator predominantly focuses the attention on the manifestation of emotions through ECs that denote events, persons, and objects which activate narrator’s emotional state.