pwc-archive/papers-with-abstracts huggingface dataset:
The Tembusu Treebank: An English Learner Treebank
Authors: Luís Morgado da Costa, Francis Bond, Roger V. P. Winder
Date: 2022-06-01 00:00:00
Proceeding: LREC 2022 6
Tasks: Grammatical Error Detection

This paper reports on the creation and development of the Tembusu Learner Treebank — an open treebank created from the NTU Corpus of Learner English, unique for incorporating mal-rules in the annotation of ungrammatical sentences. It describes the motivation and development of the treebank, as well as its exploitation to build a new parse-ranking model for the English Resource Grammar, designed to help improve the parse selection of ungrammatical sentences and diagnose these sentences through mal-rules. The corpus contains 25,000 sentences, of which 4,900 are treebanked. The paper concludes with an evaluation experiment that shows the usefulness of this new treebank in the tasks of grammatical error detection and diagnosis.
The Norwegian Dialect Corpus Treebank
Authors: Andre Kåsen, Kristin Hagen, Anders Nøklestad, Joel Priestly, Per Erik Solberg, Dag Trygve Truslew Haug
Date: 2022-06-01 00:00:00
Proceeding: LREC 2022 6

This paper presents the NDC Treebank of spoken Norwegian dialects in the Bokmål variety of Norwegian. It consists of dialect recordings made between 2006 and 2012 which have been digitised, segmented, transcribed and subsequently annotated with morphological and syntactic analysis. The nature of the spoken data gives rise to various challenges both in segmentation and annotation. We follow earlier efforts for Norwegian, in particular the LIA Treebank of spoken dialects transcribed in the Nynorsk variety of Norwegian, in the annotation principles to ensure interusability of the resources. We have developed a spoken language parser on the basis of the annotated material and report on its accuracy both on a test set across the dialects and by holding out single dialects.
RRGparbank: A Parallel Role and Reference Grammar Treebank
Authors: Tatiana Bladier, Kilian Evang, Valeria Generalova, Zahra Ghane, Laura Kallmeyer, Robin Möllemann, Natalia Moors, Rainer Osswald, Simon Petitjean
Date: 2022-06-01 00:00:00
Proceeding: LREC 2022 6

This paper describes the first release of RRGparbank, a multilingual parallel treebank for Role and Reference Grammar (RRG) containing annotations of George Orwell’s novel 1984 and its translations. The release comprises the entire novel for English and a constructionally diverse and highly parallel sample (“seed”) for German, French and Russian. The paper gives an overview of annotation decisions that have been taken and describes the adopted treebanking methodology. Finally, as a possible application, a multilingual parser is trained on the treebank data. RRGparbank is one of the first resources to apply RRG to large amounts of real-world data. Furthermore, it enables comparative and typological corpus studies in RRG. And, finally, it creates new possibilities of data-driven NLP applications based on RRG.
Unifying Morphology Resources with OntoLex-Morph. A Case Study in German
Authors: Christian Chiarcos, Christian Fäth, Maxim Ionov
Date: 2022-06-01 00:00:00
Proceeding: LREC 2022 6
Tasks: MORPH

The OntoLex vocabulary has become a widely used community standard for machine-readable lexical resources on the web. The primary motivation to use OntoLex in favor of tool- or application-specific formalisms is to facilitate interoperability and information integration across different resources. One of its extension that is currently being developed is a module for representing morphology, OntoLex-Morph. In this paper, we show how OntoLex-Morph can be used for the encoding and integration of different types of morphological resources on a unified basis. With German as the example, we demonstrate it for (a) a full-form dictionary with inflection information (Unimorph), (b) a dictionary of base forms and their derivations (UDer), (c) a dictionary of compounds (from GermaNet), and (d) lexicon and inflection rules of a finite-state parser/generator (SMOR/Morphisto). These data are converted to OntoLex-Morph, their linguistic information is consolidated and corresponding lexical entries are linked with each other.
Building Dataset for Grounding of Formulae — Annotating Coreference Relations Among Math Identifiers
Authors: Takuto Asakura, Yusuke Miyao, Akiko Aizawa
Date: 2022-06-01 00:00:00
Proceeding: LREC 2022 6
Tasks: Math

Grounding the meaning of each symbol in math formulae is important for automated understanding of scientific documents. Generally speaking, the meanings of math symbols are not necessarily constant, and the same symbol is used in multiple meanings. Therefore, coreference relations between symbols need to be identified for grounding, and the task has aspects of both description alignment and coreference analysis. In this study, we annotated 15 papers selected from arXiv.org with the grounding information. In total, 12,352 occurrences of math identifiers in these papers were annotated, and all coreference relations between them were made explicit in each paper. The constructed dataset shows that regardless of the ambiguity of symbols in math formulae, coreference relations can be labeled with a high inter-annotator agreement. The constructed dataset enables us to achieve automation of formula grounding, and in turn, make deeper use of the knowledge in scientific documents using techniques such as math information extraction. The built grounding dataset is available at https://sigmathling.kwarc.info/resources/grounding- dataset/.
CorefUD 1.0: Coreference Meets Universal Dependencies
Authors: Anna Nedoluzhko, Michal Novák, Martin Popel, Zdeněk Žabokrtský, Amir Zeldes, Daniel Zeman
Date: 2022-06-01 00:00:00
Proceeding: LREC 2022 6
Tasks: coreference-resolution, Coreference Resolution, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER)

Recent advances in standardization for annotated language resources have led to successful large scale efforts, such as the Universal Dependencies (UD) project for multilingual syntactically annotated data. By comparison, the important task of coreference resolution, which clusters multiple mentions of entities in a text, has yet to be standardized in terms of data formats or annotation guidelines. In this paper we present CorefUD, a multilingual collection of corpora and a standardized format for coreference resolution, compatible with morphosyntactic annotations in the UD framework and including facilities for related tasks such as named entity recognition, which forms a first step in the direction of convergence for coreference resolution across languages.
The Universal Anaphora Scorer
Authors: Juntao Yu, Sopan Khosla, Nafise Sadat Moosavi, Silviu Paun, Sameer Pradhan, Massimo Poesio
Date: 2022-06-01 00:00:00
Proceeding: LREC 2022 6

The aim of the Universal Anaphora initiative is to push forward the state of the art in anaphora and anaphora resolution by expanding the aspects of anaphoric interpretation which are or can be reliably annotated in anaphoric corpora, producing unified standards to annotate and encode these annotations, deliver datasets encoded according to these standards, and developing methods for evaluating models carrying out this type of interpretation. Such expansion of the scope of anaphora resolution requires a comparable expansion of the scope of the scorers used to evaluate this work. In this paper, we introduce an extended version of the Reference Coreference Scorer (Pradhan et al., 2014) that can be used to evaluate the extended range of anaphoric interpretation included in the current Universal Anaphora proposal. The UA scorer supports the evaluation of identity anaphora resolution and of bridging reference resolution, for which scorers already existed but not integrated in a single package. It also supports the evaluation of split antecedent anaphora and discourse deixis, for which no tools existed. The proposed approach to the evaluation of split antecedent anaphora is entirely novel; the proposed approach to the evaluation of discourse deixis leverages the encoding of discourse deixis proposed in Universal Anaphora to enable the use for discourse deixis of the same metrics already used for identity anaphora. The scorer was tested in the recent CODI-CRAC 2021 Shared Task on Anaphora Resolution in Dialogues.
Enhancing Deep Learning with Embedded Features for Arabic Named Entity Recognition
Authors: Ali L. Hatab, Caroline Sabty, Slim Abdennadher
Date: 2022-06-01 00:00:00
Proceeding: LREC 2022 6
Tasks: named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER, Word Embeddings

The introduction of word embedding models has remarkably changed many Natural Language Processing tasks. Word embeddings can automatically capture the semantics of words and other hidden features. Nonetheless, the Arabic language is highly complex, which results in the loss of important information. This paper uses Madamira, an external knowledge source, to generate additional word features. We evaluate the utility of adding these features to conventional word and character embeddings to perform the Named Entity Recognition (NER) task on Modern Standard Arabic (MSA). Our NER model is implemented using Bidirectional Long Short Term Memory and Conditional Random Fields (BiLSTM-CRF). We add morphological and syntactical features to different word embeddings to train the model. The added features improve the performance by different values depending on the used embedding model. The best performance is achieved by using Bert embeddings. Moreover, our best model outperforms the previous systems to the best of our knowledge.
Semantic Relations between Text Segments for Semantic Storytelling: Annotation Tool - Dataset - Evaluation
Authors: Michael Raring, Malte Ostendorff, Georg Rehm
Date: 2022-06-01 00:00:00
Proceeding: LREC 2022 6
Tasks: Articles, Sentence

Semantic Storytelling describes the goal to automatically and semi-automatically generate stories based on extracted, processed, classified and annotated information from large content resources. Essential is the automated processing of text segments extracted from different content resources by identifying the relevance of a text segment to a topic and its semantic relation to other text segments. In this paper we present an approach to create an automatic classifier for semantic relations between extracted text segments from different news articles. We devise custom annotation guidelines based on various discourse structure theories and annotate a dataset of 2,501 sentence pairs extracted from 2,638 Wikinews articles. For the annotation, we developed a dedicated annotation tool. Based on the constructed dataset, we perform initial experiments with Transformer language models that are trained for the automatic classification of semantic relations. Our results with promising high accuracy scores suggest the validity and applicability of our approach for future Semantic Storytelling solutions.
Evaluating Pre-training Objectives for Low-Resource Translation into Morphologically Rich Languages
Authors: Prajit Dhar, Arianna Bisazza, Gertjan van Noord
Date: 2022-06-01 00:00:00
Proceeding: LREC 2022 6
Tasks: Machine Translation, NMT, Translation

The scarcity of parallel data is a major limitation for Neural Machine Translation (NMT) systems, in particular for translation into morphologically rich languages (MRLs). An important way to overcome the lack of parallel data is to leverage target monolingual data, which is typically more abundant and easier to collect. We evaluate a number of techniques to achieve this, ranging from back-translation to random token masking, on the challenging task of translating English into four typologically diverse MRLs, under low-resource settings. Additionally, we introduce Inflection Pre-Training (or PT-Inflect), a novel pre-training objective whereby the NMT system is pre-trained on the task of re-inflecting lemmatized target sentences before being trained on standard source-to-target language translation. We conduct our evaluation on four typologically diverse target MRLs, and find that PT-Inflect surpasses NMT systems trained only on parallel data. While PT-Inflect is outperformed by back-translation overall, combining the two techniques leads to gains in some of the evaluated language pairs.
Aligning Images and Text with Semantic Role Labels for Fine-Grained Cross-Modal Understanding
Authors: Abhidip Bhattacharyya, Cecilia Mauceri, Martha Palmer, Christoffer Heckman
Date: 2022-06-01 00:00:00
Proceeding: LREC 2022 6
Tasks: Caption Generation, Image Retrieval, Retrieval, Sentence

As vision processing and natural language processing continue to advance, there is increasing interest in multimodal applications, such as image retrieval, caption generation, and human-robot interaction. These tasks require close alignment between the information in the images and text. In this paper, we present a new multimodal dataset that combines state of the art semantic annotation for language with the bounding boxes of corresponding images. This richer multimodal labeling supports cross-modal inference for applications in which such alignment is useful. Our semantic representations, developed in the natural language processing community, abstract away from the surface structure of the sentence, focusing on specific actions and the roles of their participants, a level that is equally relevant to images. We then utilize these representations in the form of semantic role labels in the captions and the images and demonstrate improvements in standard tasks such as image retrieval. The potential contributions of these additional labels is evaluated using a role-aware retrieval system based on graph convolutional and recurrent neural networks. The addition of semantic roles into this system provides a significant increase in capability and greater flexibility for these tasks, and could be extended to state-of-the-art techniques relying on transformers with larger amounts of annotated data.
Rosetta-LSF: an Aligned Corpus of French Sign Language and French for Text-to-Sign Translation
Authors: Elise Bertin-Lemée, Annelies Braffort, Camille Challant, Claire Danet, Boris Dauriac, Michael Filhol, Emmanuella Martinod, Jérémie Segouat
Date: 2022-06-01 00:00:00
Proceeding: LREC 2022 6

This article presents a new French Sign Language (LSF) corpus called “Rosetta-LSF”. It was created to support future studies on the automatic translation of written French into LSF, rendered through the animation of a virtual signer. An overview of the field highlights the importance of a quality representation of LSF. In order to obtain quality animations understandable by signers, it must surpass the simple “gloss transcription” of the LSF lexical units to use in the discourse. To achieve this, we designed a corpus composed of four types of aligned data, and evaluated its usability. These are: news headlines in French, translations of these headlines into LSF in the form of videos showing animations of a virtual signer, gloss annotations of the “traditional” type—although including additional information on the context in which each gestural unit is performed as well as their potential for adaptation to another context—and AZee representations of the videos, i.e. formal expressions capturing the necessary and sufficient linguistic information. This article describes this data, exhibiting an example from the corpus. It is available online for public research.
OpenKorPOS: Democratizing Korean Tokenization with Voting-Based Open Corpus Annotation
Authors: Sangwhan Moon, Won Ik Cho, Hye Joo Han, Naoaki Okazaki, Nam Soo Kim
Date: 2022-06-01 00:00:00
Proceeding: LREC 2022 6
Tasks: POS, POS Tagging, Text Generation

Korean is a language with complex morphology that uses spaces at larger-than-word boundaries, unlike other East-Asian languages. While morpheme-based text generation can provide significant semantic advantages compared to commonly used character-level approaches, Korean morphological analyzers only provide a sequence of morpheme-level tokens, losing information in the tokenization process. Two crucial issues are the loss of spacing information and subcharacter level morpheme normalization, both of which make the tokenization result challenging to reconstruct the original input string, deterring the application to generative tasks. As this problem originates from the conventional scheme used when creating a POS tagging corpus, we propose an improvement to the existing scheme, which makes it friendlier to generative tasks. On top of that, we suggest a fully-automatic annotation of a corpus by leveraging public analyzers. We vote the surface and POS from the outcome and fill the sequence with the selected morphemes, yielding tokenization with a decent quality that incorporates space information. Our scheme is verified via an evaluation done on an external corpus, and subsequently, it is adapted to Korean Wikipedia to construct an open, permissive resource. We compare morphological analyzer performance trained on our corpus with existing methods, then perform an extrinsic evaluation on a downstream task.
Enriching Grammatical Error Correction Resources for Modern Greek
Authors: Katerina Korre, John Pavlopoulos
Date: 2022-06-01 00:00:00
Proceeding: LREC 2022 6
Tasks: Grammatical Error Correction

Grammatical Error Correction (GEC), a task of Natural Language Processing (NLP), is challenging for underepresented languages. This issue is most prominent in languages other than English. This paper addresses the issue of data and system sparsity for GEC purposes in the modern Greek Language. Following the most popular current approaches in GEC, we develop and test an MT5 multilingual text-to-text transformer for Greek. To our knowledge this the first attempt to create a fully-fledged GEC model for Greek. Our evaluation shows that our system reaches up to 52.63% F0.5 score on part of the Greek Native Corpus (GNC), which is 16% below the winning system of the BEA-19 shared task on English GEC. In addition, we provide an extended version of the Greek Learner Corpus (GLC), on which our model reaches up to 22.76% F0.5. Previous versions did not include corrections with the annotations which hindered the potential development of efficient GEC systems. For that reason we provide a new set of corrections. This new dataset facilitates an exploration of the generalisation abilities and robustness of our system, given that the assessment is conducted on learner data while the training on native data.
A Hmong Corpus with Elaborate Expression Annotations
Authors: David R. Mortensen, Xinyu Zhang, Chenxuan Cui, Katherine Zhang
Date: 2022-06-01 00:00:00
Proceeding: LREC 2022 6
Tasks: Word Embeddings

This paper describes the first publicly available corpus of Hmong, a minority language of China, Vietnam, Laos, Thailand, and various countries in Europe and the Americas. The corpus has been scraped from a long-running Usenet newsgroup called soc.culture.hmong and consists of approximately 12 million tokens. This corpus (called SCH) is also the first substantial corpus to be annotated for elaborate expressions, a kind of four-part coordinate construction that is common and important in the languages of mainland Southeast Asia. We show that word embeddings trained on SCH can benefit tasks in Hmong (solving analogies) and that a model trained on it can label previously unseen elaborate expressions, in context, with an F1 of 90.79 (precision: 87.36, recall: 94.52). [ISO 639-3: mww, hmj]
ELAL: An Emotion Lexicon for the Analysis of Alsatian Theatre Plays
Authors: Delphine Bernhard, Pablo Ruiz Fabo
Date: 2022-06-01 00:00:00
Proceeding: LREC 2022 6
Tasks: Emotion Recognition

In this work, we present a novel and manually corrected emotion lexicon for the Alsatian dialects, including graphical variants of Alsatian lexical items. These High German dialects are spoken in the North-East of France. They are used mainly orally, and thus lack a stable and consensual spelling convention. There has nevertheless been a continuous literary production since the middle of the 17th century and, in particular, theatre plays. A large sample of Alsatian theatre plays is currently being encoded according to the Text Encoding Initiative (TEI) Guidelines. The emotion lexicon will be used to perform automatic emotion analysis in this corpus of theatre plays. We used a graph-based approach to deriving emotion scores and translations, relying only on bilingual lexicons, cognates and spelling variants. The source lexicons for emotion scores are the NRC Valence Arousal and Dominance and NRC Emotion Intensity lexicons.
Universal Dependencies for Western Sierra Puebla Nahuatl
Authors: Robert Pugh, Marivel Huerta Mendez, Mitsuya Sasaki, Francis Tyers
Date: 2022-06-01 00:00:00
Proceeding: LREC 2022 6

We present a morpho-syntactically-annotated corpus of Western Sierra Puebla Nahuatl that conforms to the annotation guidelines of the Universal Dependencies project. We describe the sources of the texts that make up the corpus, the annotation process, and important annotation decisions made throughout the development of the corpus. As the first indigenous language of Mexico to be added to the Universal Dependencies project, this corpus offers a good opportunity to test and more clearly define annotation guidelines for the Meso-american linguistic area, spontaneous and elicited spoken data, and code-switching.
The Construction and Evaluation of the LEAFTOP Dataset of Automatically Extracted Nouns in 1480 Languages
Authors: Gregory Baker, Diego Molla
Date: 2022-06-01 00:00:00
Proceeding: LREC 2022 6

The LEAFTOP (language extracted automatically from thousands of passages) dataset consists of nouns that appear in multiple places in the four gospels of the New Testament. We use a naive approach — probabilistic inference — to identify likely translations in 1480 other languages. We evaluate this process and find that it provides lexiconaries with accuracy from 42% (Korafe) to 99% (Runyankole), averaging 72% correct across evaluated languages. The process translates up to 161 distinct lemmas from Koine Greek (average 159). We identify nouns which appear to be easy and hard to translate, language families where this technique works, and future possible improvements and extensions. The claims to novelty are: the use of a Koine Greek New Testament as the source language; using a fully-annotated manually-created grammatically parse of the source text; a custom scraper for texts in the target languages; a new metric for language similarity; a novel strategy for evaluation on low-resource languages.
Huqariq: A Multilingual Speech Corpus of Native Languages of Peru forSpeech Recognition
Authors: Rodolfo Zevallos, Luis Camacho, Nelsi Melgarejo
Date: 2022-06-01 00:00:00
Proceeding: LREC 2022 6
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Language Identification, speech-recognition, Speech Recognition, text-to-speech, Text to Speech

The Huqariq corpus is a multilingual collection of speech from native Peruvian languages. The transcribed corpus is intended for the research and development of speech technologies to preserve endangered languages in Peru. Huqariq is primarily designed for the development of automatic speech recognition, language identification and text-to-speech tools. In order to achieve corpus collection sustainably, we employs the crowdsourcing methodology. Huqariq includes four native languages of Peru, and it is expected that by the year 2022, it can reach up to 20 native languages out of the 48 native languages in Peru. The corpus has 220 hours of transcribed audio recorded by more than 500 volunteers, making it the largest speech corpus for native languages in Peru. In order to verify the quality of the corpus, we present speech recognition experiments using 220 hours of fully transcribed audio.