pwc-archive/papers-with-abstracts huggingface dataset:
Annotating “Particles” in Multiword Expressions in te reo Māori for a Part-of-Speech Tagger
Authors: Aoife Finn, Suzanne Duncan, Peter-Lucas Jones, Gianna Leoni, Keoni Mahelona
Date: 2022-06-01 00:00:00
Proceeding: LREC (MWE) 2022 6
Tasks: Part-Of-Speech Tagging

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. Te reo Māori is a particularly analytical and polysemic language. A word class called “particles” is introduced, they are small multi-functional words with many meanings, for example ē, ai, noa, rawa, mai, anō and koa. These “particles” are reflective of the analytical and polysemous nature of te reo Māori. They frequently occur both singularly and also in multiword expressions, including time adverbial phrases. The paper illustrates the challenges that they presented to part-of-speech tagging. It also discusses how we overcome these challenges in a way that is appropriate for te reo Māori, given its status an Indigenous language and history of colonisation. This includes a discussion of the importance of accurately reflecting the conceptualization of te reo Māori. And how this involved making no linguistic presumptions, and of eliciting faithful judgements from speakers, in a way that is uninfluenced by linguistic terminology.
Metaphor Detection for Low Resource Languages: From Zero-Shot to Few-Shot Learning in Middle High German
Authors: Felix Schneider, Sven Sickert, Phillip Brandes, Sophie Marshall, Joachim Denzler
Date: 2022-06-01 00:00:00
Proceeding: LREC (MWE) 2022 6
Tasks: Few-Shot Learning, Word Embeddings

In this work, we present a novel unsupervised method for adjective-noun metaphor detection on low resource languages. We propose two new approaches: First, a way of artificially generating metaphor training examples and second, a novel way to find metaphors relying only on word embeddings. The latter enables application for low resource languages. Our method is based on a transformation of word embedding vectors into another vector space, in which the distance between the adjective word vector and the noun word vector represents the metaphoricity of the word pair. We train this method in a zero-shot pseudo-supervised manner by generating artificial metaphor examples and show that our approach can be used to generate a metaphor dataset with low annotation cost. It can then be used to finetune the system in a few-shot manner. In our experiments we show the capabilities of the method in its unsupervised and in its supervised version. Additionally, we test it against a comparable unsupervised baseline method and a supervised variation of it.
Automatic Bilingual Phrase Dictionary Construction from GIZA++ Output
Authors: Albina Khusainova, Vitaly Romanov, Adil Khan
Date: 2022-06-01 00:00:00
Proceeding: LREC (MWE) 2022 6
Tasks: Decoder, Machine Translation, NMT, Sentence, Translation

Modern encoder-decoder based neural machine translation (NMT) models are normally trained on parallel sentences. Hence, they give best results when translating full sentences rather than sentence parts. Thereby, the task of translating commonly used phrases, which often arises for language learners, is not addressed by NMT models. While for high-resourced language pairs human-built phrase dictionaries exist, less-resourced pairs do not have them. We suggest an approach for building such dictionary automatically based on the GIZA++ output and show that it works significantly better than translating phrases with a sentences-trained NMT system.
A BERT’s Eye View: Identification of Irish Multiword Expressions Using Pre-trained Language Models
Authors: Abigail Walsh, Teresa Lynn, Jennifer Foster
Date: 2022-06-01 00:00:00
Proceeding: LREC (MWE) 2022 6

This paper reports on the investigation of using pre-trained language models for the identification of Irish verbal multiword expressions (vMWEs), comparing the results with the systems submitted for the PARSEME shared task edition 1.2. We compare the use of a monolingual BERT model for Irish (gaBERT) with multilingual BERT (mBERT), fine-tuned to perform MWE identification, presenting a series of experiments to explore the impact of hyperparameter tuning and dataset optimisation steps on these models. We compare the results of our optimised systems to those achieved by other systems submitted to the shared task, and present some best practices for minority languages addressing this task.
Enhancing the PARSEME Turkish Corpus of Verbal Multiword Expressions
Authors: Yagmur Ozturk, Najet Hadj Mohamed, Adam Lion-Bouton, Agata Savary
Date: 2022-06-01 00:00:00
Proceeding: LREC (MWE) 2022 6

The PARSEME (Parsing and Multiword Expressions) project proposes multilingual corpora annotated for multiword expressions (MWEs). In this case study, we focus on the Turkish corpus of PARSEME. Turkish is an agglutinative language and shows high inflection and derivation in word forms. This can cause some issues in terms of automatic morphosyntactic annotation. We provide an overview of the problems observed in the morphosyntactic annotation of the Turkish PARSEME corpus. These issues are mostly observed on the lemmas, which is important for the approximation of a type of an MWE. We propose modifications of the original corpus with some enhancements on the lemmas and parts of speech. The enhancements are then evaluated with an identification system from the PARSEME Shared Task 1.2 to detect MWEs, namely Seen2Seen. Results show increase in the F-measure for MWE identification, emphasizing the necessity of robust morphosyntactic annotation for MWE processing, especially for languages that show high surface variability.
mwetoolkit-lib: Adaptation of the mwetoolkit as a Python Library and an Application to MWE-based Document Clustering
Authors: Fernando Zagatti, Paulo Augusto de Lima Medeiros, Esther da Cunha Soares, Lucas Nildaimon dos Santos Silva, Carlos Ramisch, Livy Real
Date: 2022-06-01 00:00:00
Proceeding: LREC (MWE) 2022 6

This paper introduces the mwetoolkit-lib, an adaptation of the mwetoolkit as a python library. The original toolkit performs the extraction and identification of multiword expressions (MWEs) in large text bases through the command line. One of the contributions of our work is the adaptation of the MWE extraction pipeline from the mwetoolkit, allowing its usage in python development environments and integration in larger pipelines. The other contribution is the execution of a pilot experiment aiming to show the impact of MWE discovery in data professionals’ work. This experiment found that the addition of MWE knowledge to the Term Frequency-Inverse Document Frequency (TF-IDF) vectorization altered the word relevance order, improving the linguistic quality of the clusters returned by k-means method.
Handling Idioms in Symbolic Multilingual Natural Language Generation
Authors: Michaelle Dubé, François Lareau
Date: 2022-06-01 00:00:00
Proceeding: LREC (MWE) 2022 6
Tasks: Text Generation

While idioms are usually very rigid in their expression, they sometimes allow a certain level of freedom in their usage, with modifiers or complements splitting them or being syntactically attached to internal nodes rather than to the root (e.g., “take something with a big grain of salt”). This means that they cannot always be handled as ready-made strings in rule-based natural language generation systems. Having access to the internal syntactic structure of an idiom allows for more subtle processing. We propose a way to enumerate all possible language-independent n-node trees and to map particular idioms of a language onto these generic syntactic patterns. Using this method, we integrate the idioms from the LN-fr into GenDR, a multilingual realizer. Our implementation covers nearly 98% of LN-fr’s idioms with high precision, and can easily be extended or ported to other languages.
Casteism in India, but Not Racism - a Study of Bias in Word Embeddings of Indian Languages
Authors: Senthil Kumar B, Pranav Tiwari, Aman Chandra Kumar, Aravindan Chandrabose
Date: 2022-06-01 00:00:00
Proceeding: LATERAISSE (LREC) 2022 6
Tasks: Cultural Vocal Bursts Intensity Prediction, Word Embeddings

In this paper, we studied the gender bias in monolingual word embeddings of two Indian languages Hindi and Tamil. Tamil is one of the classical languages of India from the Dravidian language family. In Indian society and culture, instead of racism, a similar type of discrimination called casteism is against the subgroup of peoples representing lower class or Dalits. The word embeddings measurement to evaluate bias using the WEAT score reveals that the embeddings are biased with gender and casteism which is in line with the common stereotypical human biases.
Objectifying Women? A Syntactic Bias in French and English Corpora.
Authors: Yanis da Cunha, Anne Abeillé
Date: 2022-06-01 00:00:00
Proceeding: LATERAISSE (LREC) 2022 6

Gender biases in syntax have been documented for languages with grammatical gender for cases where mixed-gender coordination structures take masculine agreement, or with male-first preference in the ordering of pairs (Adam and Eve). On the basis of various annotated corpora spanning different genres (fiction, newspapers, speech and web), we show another syntactic gender bias: masculine pronouns are more often subjects than feminine pronouns, in both English and French. We find the same bias towards masculine subjects for French human nouns, which then refer to males and females. Comparing the subject of passive verbs and the object of active verbs, we show that this syntactic function bias is not reducible to a bias in semantic role assignment since it is also found with non-agentive subjects. For French fiction, we also found that the masculine syntactic function bias is larger in text written by male authors – female authors seem to be unbiased. We finally discuss two principles as possible explanations, ‘Like Me’ and ‘Easy first’, and examine the effect of the discourse tendency for men being agents and topics. We conclude by addressing the impact of such biases in language technologies.
A Cancel Culture Corpus through the Lens of Natural Language Processing
Authors: Justus-Jonas Erker, Catalina Goanta, Gerasimos Spanakis
Date: 2022-06-01 00:00:00
Proceeding: LATERAISSE (LREC) 2022 6
Tasks: Cultural Vocal Bursts Intensity Prediction, Sentiment Analysis

Cancel Culture as an Internet phenomenon has been previously explored from a social and legal science perspective. This paper demonstrates how Natural Language Processing tasks can be derived from this previous work, underlying techniques on how cancel culture can be measured, identified and evaluated. As part of this paper, we introduce a first cancel culture data set with of over 2.3 million tweets and a framework to enlarge it further. We provide a detailed analysis of this data set and propose a set of features, based on various models including sentiment analysis and emotion detection that can help characterizing cancel culture.
Benchmarking Language Models for Cyberbullying Identification and Classification from Social-media Texts
Authors: Kanishk Verma, Tijana Milosevic, Keith Cortis, Brian Davis
Date: 2022-06-01 00:00:00
Proceeding: LATERAISSE (LREC) 2022 6
Tasks: Benchmarking, Binary Classification, Classification

Cyberbullying is bullying perpetrated via the medium of modern communication technologies like social media networks and gaming platforms. Unfortunately, most existing datasets focusing on cyberbullying detection or classification are i) limited in number ii) usually targeted to one specific online social networking (OSN) platform, or iii) often contain low-quality annotations. In this study, we fine-tune and benchmark state of the art neural transformers for the binary classification of cyberbullying in social media texts, which is of high value to Natural Language Processing (NLP) researchers and computational social scientists. Furthermore, this work represents the first step toward building neural language models for cross OSN platform cyberbullying classification to make them as OSN platform agnostic as possible.
Identifying Hate Speech Using Neural Networks and Discourse Analysis Techniques
Authors: Zehra Melce Hüsünbeyi, Didar Akar, Arzucan Özgür
Date: 2022-06-01 00:00:00
Proceeding: LATERAISSE (LREC) 2022 6
Tasks: Hate Speech Detection

Discriminatory language, in particular hate speech, is a global problem posing a grave threat to democracy and human rights. Yet, it is not always easy to identify, as it is rarely explicit. In order to detect hate speech, we developed Hierarchical Attention Network (HAN) based and Bidirectional Encoder Representations from Transformer (BERT) based deep learning models to capture the changing discursive cues and understand the context around the discourse. In addition, we designed linguistic features using critical discourse analysis techniques and integrated them into these neural network models. We studied the compatibility of our model with the hate speech detection problem by comparing it with traditional machine learning models, as well as a Convolution Neural Network (CNN) based model, a Convolutional Neural Network-Gated Recurrent Unit (CNN-GRU) based model which reached significant performance results for hate speech detection. Our results on a manually annotated corpus of print media in Turkish show that the proposed approach is effective for hate speech detection. We believe that the feature sets created for the Turkish language will encourage new studies in the quantitative analysis of hate speech.
An Open Source Contractual Language Understanding Application Using Machine Learning
Authors: Afra Nawar, Mohammed Rakib, Salma Abdul Hai, Sanaulla Haq
Date: 2022-06-01 00:00:00
Proceeding: LATERAISSE (LREC) 2022 6
Tasks: Document Text Classification, Information Retrieval, Optical Character Recognition (OCR), Question Answering

Legal field is characterized by its exclusivity and non-transparency. Despite the frequency and relevance of legal dealings, legal documents like contracts remains elusive to non-legal professionals for the copious usage of legal jargon. There has been little advancement in making legal contracts more comprehensible. This paper presents how Machine Learning and NLP can be applied to solve this problem, further considering the challenges of applying ML to the high length of contract documents and training in a low resource environment. The largest open-source contract dataset so far, the Contract Understanding Atticus Dataset (CUAD) is utilized. Various pre-processing experiments and hyperparameter tuning have been carried out and we successfully managed to eclipse SOTA results presented for models in the CUAD dataset trained on RoBERTa-base. Our model, A-type-RoBERTa-base achieved an AUPR score of 46.6% compared to 42.6% on the original RoBERT-base. This model is utilized in our end to end contract understanding application which is able to take a contract and highlight the clauses a user is looking to find along with it’s descriptions to aid due diligence before signing. Alongside digital, i.e. searchable, contracts the system is capable of processing scanned, i.e. non-searchable, contracts using tesseract OCR. This application is aimed to not only make contract review a comprehensible process to non-legal professionals, but also to help lawyers and attorneys more efficiently review contracts.
PeruSIL: A Framework to Build a Continuous Peruvian Sign Language Interpretation Dataset
Authors: Gissella Bejarano, Joe Huamani-Malca, Francisco Cerna-Herrera, Fernando Alva-Manchego, Pablo Rivas
Date: 2022-06-01 00:00:00
Proceeding: SignLang (LREC) 2022 6
Tasks: Sign Language Recognition

Video-based datasets for Continuous Sign Language are scarce due to the challenging task of recording videos from native signers and the reduced number of people who can annotate sign language. COVID-19 has evidenced the key role of sign language interpreters in delivering nationwide health messages to deaf communities. In this paper, we present a framework for creating a multi-modal sign language interpretation dataset based on videos and we use it to create the first dataset for Peruvian Sign Language (LSP) interpretation annotated by hearing volunteers who have intermediate knowledge of PSL guided by the video audio. We rely on hearing people to produce a first version of the annotations, which should be reviewed by native signers in the future. Our contributions: i) we design a framework to annotate a sign Language dataset; ii) we release the first annotated LSP multi-modal interpretation dataset (AEC); iii) we evaluate the annotation done by hearing people by training a sign language recognition model. Our model reaches up to 80.3% of accuracy among a minimum of five classes (signs) AEC dataset, and 52.4% in a second dataset. Nevertheless, analysis by subject in the second dataset show variations worth to discuss.
Introducing Sign Languages to a Multilingual Wordnet: Bootstrapping Corpora and Lexical Resources of Greek Sign Language and German Sign Language
Authors: Sam Bigeard, Marc Schulder, Maria Kopf, Thomas Hanke, Kyriaki Vasilaki, Anna Vacalopoulou, Theodore Goulas, Athanasia-Lida Dimou, Stavroula-Evita Fotinea, Eleni Efthimiou
Date: 2022-06-01 00:00:00
Proceeding: SignLang (LREC) 2022 6

Wordnets have been a popular lexical resource type for many years. Their sense-based representation of lexical items and numerous relation structures have been used for a variety of computational and linguistic applications. The inclusion of different wordnets into multilingual wordnet networks has further extended their use into the realm of cross-lingual research. Wordnets have been released for many spoken languages. Research has also been carried out into the creation of wordnets for several sign languages, but none have yet resulted in publicly available datasets. This article presents our own efforts towards an inclusion of sign languages in a multilingual wordnet, starting with Greek Sign Language (GSL) and German Sign Language (DGS). Based on differences in available language resources between GSL and DGS, we trial two workflows with different coverage priorities. We also explore how synergies between both workflows can be leveraged and how future work on additional sign languages could profit from building on existing sign language wordnet data. The results of our work are made publicly available.
Introducing the signglossR Package
Authors: Carl Börstell
Date: 2022-06-01 00:00:00
Proceeding: SignLang (LREC) 2022 6

The signglossR package is a library written in the programming language R, intended as an easy-to-use resource for those who work with signed language data and are familiar with R. The package contains a variety of functions designed specifically towards signed language research, facilitating a single-pipeline workflow with R when accessing public language resources remotely (online) or a user’s own files and data. The package specifically targets processing of image and video files, but also features some interaction with software commonly used by researchers working on signed language and gesture, such as ELAN and OpenPose. The signglossR package combines features and functionality from many other libraries and tools in order to simplify and collect existing resources in one place, as well as adding some new functionality, and adapt everything to the needs of researchers working with visual language data. In this paper, the main features of this package are introduced.
Moving towards a Functional Approach in the Flemish Sign Language Dictionary Making Process
Authors: Caro Brosens, Margot Janssens, Sam Verstraete, Thijs Vandamme, Hannes De Durpel
Date: 2022-06-01 00:00:00
Proceeding: SignLang (LREC) 2022 6
Tasks: Instance Search

This presentation will outline the dictionary making process of the new online Flemish Sign Language dictionary launched in 2019. First some necessary background information is provided, consisting of a brief history of Flemish Sign Language (VGT) lexicography. Then three phases in the development of the renewed dictionary of VGT will be explored: (i) user research, (ii) data-cleaning and modeling, and (iii) innovations. More than wanting to project a report of lexicographic research on a website, the goal was to make the new dictionary a practical, user-friendly reference tool that meets the needs, expectations, and skills of the dictionary users. To gain a better understanding of who the users were, several sources were consulted: the user research by Joni Oyserman (2013), the quantitative data from Google Analytics and VGTC’s own user profiles. Since 2017, VGTC has been using Signbank, an electronic database specifically developed to compile and manage lexicographic data for sign languages. Bringing together all this raw data inadvertently led to inconsistencies and small mistakes, therefore the data had to be manually revised and complemented. The VGT dictionary was mainly formally modernized, but there are also several substantive differences regarding the previous dictionary: for instance, search options were expanded, and semantic categories were added as well as a new feedback feature. In addition, the new website is also structurally different, it is now responsive to all screen sizes. Lastly, possible future innovations will briefly be discussed. VGTC aims to continuously improve both the user-based interface and the content of the current dictionary. Future goals include, but are not limited to, adding definitions and sample sentences (preferably extracted from the corpus), as well as information on the etymology and common use of signs.
Phonetics of Negative Headshake in Russian Sign Language: A Small-Scale Corpus Study
Authors: Anastasia Chizhikova, Vadim Kimmelman
Date: 2022-06-01 00:00:00
Proceeding: SignLang (LREC) 2022 6

We analyzed negative headshake found in the online corpus of Russian Sign Language. We found that negative headshake can co-occur with negative manual signs, although most of these signs are not accompanied by it. We applied OpenFace, a Computer Vision toolkit, to extract head rotation measurements from video recordings, and analyzed the headshake in terms of the number of peaks (turns), the amplitude of the turns, and their frequency. We find that such basic phonetic measurements of headshake can be extracted using a combination of manual annotation and Computer Vision, and can be further used in comparative research across constructions and sign languages.
Documenting the Use of Iranian Sign Language (ZEI) in Kermanshah
Authors: Yassaman Choubsaz, Onno Crasborn, Sara Siyavoshi, Farzaneh Soleimanbeigi
Date: 2022-06-01 00:00:00
Proceeding: SignLang (LREC) 2022 6

We describe a sign language documentation project funded by the Endangered Languages Documentation Project (ELDP) in the province of Kermanshah, a city in west of Iran. The deposit at ELDP archive (elararchive.org) includes recording of 38 native signers of Zaban Eshareh Irani living in Kermanshah. The recordings start with an elicitation of the signs of the Farsi alphabet along with fingerspelling of some words as well as vocabulary elicitation of some basic concepts. Subsequently, the participants are asked to watch short movies and then they are asked to retell the story. Later, the participants have natural conversations in pairs guided by a deaf moderator. Initial annotations of ID-glosses and translations to Persian and English were also archived. ID-glosses are stored as a dataset in Global Signbank, along with a citation form of signs and their phonological description. The resulting datasets and one-hour annotation of the conversations are available to other researchers in ELDP archive.