pwc-archive/papers-with-abstracts huggingface dataset:
Negation in Norwegian: an annotated dataset
Authors: Petter Mæhlum, Jeremy Barnes, Robin Kurtz, Lilja Øvrelid, Erik Velldal
Date: 2021-05-01 00:00:00
Proceeding: NoDaLiDa 2021 5
Tasks: Negation, Sentence

This paper introduces NorecNeg – the first annotated dataset of negation for Norwegian. Negation cues and their in-sentence scopes have been annotated across more than 11K sentences spanning more than 400 documents for a subset of the Norwegian Review Corpus (NoReC). In addition to providing in-depth discussion of the annotation guidelines, we also present a first set of benchmark results based on a graph-parsing approach.
Investigation of Transfer Languages for Parsing Latin: Italic Branch vs. Hellenic Branch
Authors: Antonia Karamolegkou, Sara Stymne
Date: 2021-05-01 00:00:00
Proceeding: NoDaLiDa 2021 5
Tasks: Dependency Parsing, Transfer Learning

Choosing a transfer language is a crucial step in transfer learning. In much previous research on dependency parsing, related languages have successfully been used. However, when parsing Latin, it has been suggested that languages such as ancient Greek could be helpful. In this work we parse Latin in a low-resource scenario, with the main goal to investigate if Greek languages are more helpful for parsing Latin than related Italic languages, and show that this is indeed the case. We further investigate the influence of other factors including training set size and content as well as linguistic distances. We find that one explanatory factor seems to be the syntactic similarity between Latin and Ancient Greek. The influence of genres or shared annotation projects seems to have a smaller impact.
Towards cross-lingual application of language-specific PoS tagging schemes
Authors: Hinrik Hafsteinsson, Anton Karl Ingason
Date: 2021-05-01 00:00:00
Proceeding: NoDaLiDa 2021 5
Tasks: POS, POS Tagging, Transfer Learning

We describe the process of conversion between the PoS tagging schemes of two languages, the Icelandic MIM-GOLD tagging scheme and the Faroese Sosialurin tagging scheme. These tagging schemes are functionally similar but use separate ways to encode fine-grained morphological information on tokenised text. As Faroese and Icelandic are lexically and grammatically similar, having a systematic method to convert between these two tagging schemes would be beneficial in the field of language technology, specifically in research on transfer learning between the two languages. As a product of our work, we present a provisional version of Icelandic corpora, prepared in the Faroese PoS tagging scheme, ready for use in cross-lingual NLP applications.
Exploring the Importance of Source Text in Automatic Post-Editing for Context-Aware Machine Translation
Authors: Chaojun Wang, Christian Hardmeier, Rico Sennrich
Date: 2021-05-01 00:00:00
Proceeding: NoDaLiDa 2021 5
Tasks: Automatic Post-Editing, Machine Translation, Sentence, Translation

Accurate translation requires document-level information, which is ignored by sentence-level machine translation. Recent work has demonstrated that document-level consistency can be improved with automatic post-editing (APE) using only target-language (TL) information. We study an extended APE model that additionally integrates source context. A human evaluation of fluency and adequacy in English–Russian translation reveals that the model with access to source context significantly outperforms monolingual APE in terms of adequacy, an effect largely ignored by automatic evaluation metrics. Our results show that TL-only modelling increases fluency without improving adequacy, demonstrating the need for conditioning on source text for automatic post-editing. They also highlight blind spots in automatic methods for targeted evaluation and demonstrate the need for human assessment to evaluate document-level translation quality reliably.
Grapheme-Based Cross-Language Forced Alignment: Results with Uralic Languages
Authors: Juho Leinonen, Sami Virpioja, Mikko Kurimo
Date: 2021-05-01 00:00:00
Proceeding: NoDaLiDa 2021 5

Forced alignment is an effective process to speed up linguistic research. However, most forced aligners are language-dependent, and under-resourced languages rarely have enough resources to train an acoustic model for an aligner. We present a new Finnish grapheme-based forced aligner and demonstrate its performance by aligning multiple Uralic languages and English as an unrelated language. We show that even a simple non-expert created grapheme-to-phoneme mapping can result in useful word alignments.
Boosting Neural Machine Translation from Finnish to Northern Sámi with Rule-Based Backtranslation
Authors: Mikko Aulamo, Sami Virpioja, Yves Scherrer, Jörg Tiedemann
Date: 2021-05-01 00:00:00
Proceeding: NoDaLiDa 2021 5
Tasks: Machine Translation, NMT, Sentence, Translation

We consider a low-resource translation task from Finnish into Northern Sámi. Collecting all available parallel data between the languages, we obtain around 30,000 sentence pairs. However, there exists a significantly larger monolingual Northern Sámi corpus, as well as a rule-based machine translation (RBMT) system between the languages. To make the best use of the monolingual data in a neural machine translation (NMT) system, we use the backtranslation approach to create synthetic parallel data from it using both NMT and RBMT systems. Evaluating the results on an in-domain test set and a small out-of-domain set, we find that the RBMT backtranslation outperforms NMT backtranslation clearly for the out-of-domain test set, but also slightly for the in-domain data, for which the NMT backtranslation model provided clearly better BLEU scores than the RBMT. In addition, combining both backtranslated data sets improves the RBMT approach only for the in-domain test set. This suggests that the RBMT system provides general-domain knowledge that cannot be found from the relative small parallel training data.
It’s Basically the Same Language Anyway: the Case for a Nordic Language Model
Authors: Magnus Sahlgren, Fredrik Carlsson, Fredrik Olsson, Love Börjeson
Date: 2021-05-01 00:00:00
Proceeding: NoDaLiDa 2021 5
Tasks: Language Modeling, Language Modelling

When is it beneficial for a research community to organize a broader collaborative effort on a topic, and when should we instead promote individual efforts? In this opinion piece, we argue that we are at a stage in the development of large-scale language models where a collaborative effort is desirable, despite the fact that the preconditions for making individual contributions have never been better. We consider a number of arguments for collaboratively developing a large-scale Nordic language model, include environmental considerations, cost, data availability, language typology, cultural similarity, and transparency. Our primary goal is to raise awareness and foster a discussion about our potential impact and responsibility as NLP community.
Decentralized Word2Vec Using Gossip Learning
Authors: Abdul Aziz Alkathiri, Lodovico Giaretta, Sarunas Girdzijauskas, Magnus Sahlgren
Date: 2021-05-01 00:00:00
Proceeding: NoDaLiDa 2021 5

Advanced NLP models require huge amounts of data from various domains to produce high-quality representations. It is useful then for a few large public and private organizations to join their corpora during training. However, factors such as legislation and user emphasis on data privacy may prevent centralized orchestration and data sharing among these organizations. Therefore, for this specific scenario, we investigate how gossip learning, a massively-parallel, data-private, decentralized protocol, compares to a shared-dataset solution. We find that the application of Word2Vec in a gossip learning framework is viable. Without any tuning, the results are comparable to a traditional centralized setting, with a loss of quality as low as 4.3%. Furthermore, the results are up to 54.8% better than independent local training.
Multilingual ELMo and the Effects of Corpus Sampling
Authors: Vinit Ravishankar, Andrey Kutuzov, Lilja Øvrelid, Erik Velldal
Date: 2021-05-01 00:00:00
Proceeding: NoDaLiDa 2021 5

Multilingual pretrained language models are rapidly gaining popularity in NLP systems for non-English languages. Most of these models feature an important corpus sampling step in the process of accumulating training data in different languages, to ensure that the signal from better resourced languages does not drown out poorly resourced ones. In this study, we train multiple multilingual recurrent language models, based on the ELMo architecture, and analyse both the effect of varying corpus size ratios on downstream performance, as well as the performance difference between monolingual models for each language, and broader multilingual language models. As part of this effort, we also make these trained models available for public use.
Error Analysis of using BART for Multi-Document Summarization: A Study for English and German Language
Authors: Timo Johner, Abhik Jana, Chris Biemann
Date: 2021-05-01 00:00:00
Proceeding: NoDaLiDa 2021 5
Tasks: Document Summarization, Language Modeling, Language Modelling, Multi-Document Summarization

Recent research using pre-trained language models for multi-document summarization task lacks deep investigation of potential erroneous cases and their possible application on other languages. In this work, we apply a pre-trained language model (BART) for multi-document summarization (MDS) task using both fine-tuning and without fine-tuning. We use two English datasets and one German dataset for this study. First, we reproduce the multi-document summaries for English language by following one of the recent studies. Next, we show the applicability of the model to German language by achieving state-of-the-art performance on German MDS. We perform an in-depth error analysis of the followed approach for both languages, which leads us to identifying most notable errors, from made-up facts and topic delimitation, and quantifying the amount of extractiveness.
Creating Data in Icelandic for Text Normalization
Authors: Helga Svala Sigurðardóttir, Anna Björk Nikulásdóttir, Jón Guðnason
Date: 2021-05-01 00:00:00
Proceeding: NoDaLiDa 2021 5
Tasks: Text Normalization

There is no natural way to acquire normalized data so we try to create good enough data to attempt more advanced methods for text normalization. We manually annotated the first normalized corpus in Icelandic, 40,000 sentences, and developed Regína, a rule-based system for text normalization. Regína gets 90.83% accuracy compared to the manually annotated corpus on non-standard words. Regína showed a significant improvement in accuracy when compared to an older normalization system for Icelandic. The normalized corpus and Regína will be released as open source.
The Danish Gigaword Corpus
Authors: Leon Strømberg-Derczynski, Manuel Ciosici, Rebekah Baglini, Morten H. Christiansen, Jacob Aarup Dalsgaard, Riccardo Fusaroli, Peter Juel Henrichsen, Rasmus Hvingelby, Andreas Kirkedal, Alex Speed Kjeldsen, Claus Ladefoged, Finn Årup Nielsen, Jens Madsen, Malte Lau Petersen, Jonathan Hvithamar Rystrøm, Daniel Varab
Date: 2021-05-01 00:00:00
Proceeding: NoDaLiDa 2021 5

Danish language technology has been hindered by a lack of broad-coverage corpora at the scale modern NLP prefers. This paper describes the Danish Gigaword Corpus, the result of a focused effort to provide a diverse and freely-available one billion word corpus of Danish text. The Danish Gigaword corpus covers a wide array of time periods, domains, speakers’ socio-economic status, and Danish dialects.
DanFEVER: claim verification dataset for Danish
Authors: Jeppe Nørregaard, Leon Derczynski
Date: 2021-05-01 00:00:00
Proceeding: NoDaLiDa 2021 5
Tasks: Claim Verification, Fact Verification, Misinformation

We present a dataset, DanFEVER, intended for multilingual misinformation research. The dataset is in Danish and has the same format as the well-known English FEVER dataset. It can be used for testing methods in multilingual settings, as well as for creating models in production for the Danish language.
The Icelandic Word Web: A language technology-focused redesign of a lexicosemantic database
Authors: Hjalti Daníelsson, Jón Hilmar Jónsson, Þórður Arnar Árnason, Alec Shaw, Einar Freyr Sigurðsson, Steinþór Steingrímsson
Date: 2021-05-01 00:00:00
Proceeding: NoDaLiDa 2021 5

The new Icelandic Word Web (IW) is a language technology focused redesign of a lexicosemantic database of semantically related entries. The IW’s entities, relations, metadata and categorization scheme have all been implemented from scratch in two systems, OntoLex and SKOS. After certain adjustments were made to OntoLex and SKOS interoperability, it was also possible to implement specific IW features that, while potentially nonstandard, form an integral part of the Word Web’s lexicosemantic functionality. Also new in this implementation are access to a larger amount of linguistic data, a greater variety of search options, the possibility of automated processing, and the ability to conduct research through SPARQL without possessing a mastery of Icelandic.
Getting Hold of Villains and other Rogues
Authors: Manfred Klenner, Anne Göhring, Sophia Conrad
Date: 2021-05-01 00:00:00
Proceeding: NoDaLiDa 2021 5

In this paper, we introduce the first corpus specifying negative entities within sentences. We discuss indicators for their presence, namely particular verbs, but also the linguistic conditions when their prediction should be suppressed. We further show that a fine-tuned Bert-based baseline model outperforms an over-generating rule-based approach which is not aware of these further restrictions. If a perfect filter were applied, both would be on par.
Talrómur: A large Icelandic TTS corpus
Authors: Atli Sigurgeirsson, Þorsteinn Gunnarsson, Gunnar Örnólfsson, Eydís Magnúsdóttir, Ragnheiður Þórhallsdóttir, Stefán Jónsson, Jón Guðnason
Date: 2021-05-01 00:00:00
Proceeding: NoDaLiDa 2021 5
Tasks: text-to-speech, Text to Speech

We present Talrómur, a large high-quality Text-To-Speech (TTS) corpus for the Icelandic language. This multi-speaker corpus contains recordings from 4 male speakers and 4 female speakers of a wide range in age and speaking style. The corpus consists of 122,417 single utterance recordings equating to approximately 213 hours of voice data. All speakers read from the same script which has a high coverage of possible Icelandic diphones. Manual analysis of 15,956 utterances indicates that the corpus has a reading mistake rate no higher than 0.25%. We additionally present results from subjective evaluations of the different voices with regards to intelligibility, likeability and trustworthiness.
The Swedish Winogender Dataset
Authors: Saga Hansson, Konstantinos Mavromatakis, Yvonne Adesam, Gerlof Bouma, Dana Dannélls
Date: 2021-05-01 00:00:00
Proceeding: NoDaLiDa 2021 5
Tasks: coreference-resolution, Coreference Resolution, Diagnostic

We introduce the SweWinogender test set, a diagnostic dataset to measure gender bias in coreference resolution. It is modelled after the English Winogender benchmark, and is released with reference statistics on the distribution of men and women between occupations and the association between gender and occupation in modern corpus material. The paper discusses the design and creation of the dataset, and presents a small investigation of the supplementary statistics.
DaNLP: An open-source toolkit for Danish Natural Language Processing
Authors: Amalie Brogaard Pauli, Maria Barrett, Ophélie Lacroix, Rasmus Hvingelby
Date: 2021-05-01 00:00:00
Proceeding: NoDaLiDa 2021 5

We present an open-source toolkit for Danish Natural Language Processing, enabling easy access to Danish NLP’s latest advancements. The toolkit features wrapper-functions for loading models and datasets in a unified way using third-party NLP frameworks. The toolkit is developed to enhance community building, understanding the need from industry and knowledge sharing. As an example of this, we present Angry Tweets: An Annotation Game to create awareness of Danish NLP and create a new sentiment-annotated dataset.
HB Deid - HB De-identification tool demonstrator
Authors: Hanna Berg, Hercules Dalianis
Date: 2021-05-01 00:00:00
Proceeding: NoDaLiDa 2021 5
Tasks: De-identification

This paper describes a freely available web-based demonstrator called HB Deid. HB Deid identifies so-called protected health information, PHI, in a text written in Swedish and removes, masks, or replaces them with surrogates or pseudonyms. PHIs are named entities such as personal names, locations, ages, phone numbers, dates. HB Deid uses a CRF model trained on non-sensitive annotated text in Swedish, as well as a rule-based post-processing step for finding PHI. The final step in obscuring the PHI is then to either mask it, show only the class name or use a rule-based pseudonymisation system to replace it.
Zero-shot cross-lingual Meaning Representation Transfer: Annotation of Hungarian using the Prague Functional Generative Description
Authors: Attila Novák, Borbála Novák, Csilla Novák
Date: 2021-11-01 00:00:00
Proceeding: EMNLP (LAW, DMR) 2021 11
Tasks: Language Modeling, Language Modelling, Sentence

In this paper, we present the results of our experiments concerning the zero-shot cross-lingual performance of the PERIN sentence-to-graph semantic parser. We applied the PTG model trained using the PERIN parser on a 740k-token Czech newspaper corpus to Hungarian. We evaluated the performance of the parser using the official evaluation tool of the MRP 2020 shared task. The gold standard Hungarian annotation was created by manual correction of the output of the parser following the annotation manual of the tectogrammatical level of the Prague Dependency Treebank. An English model trained on a larger one-million-token English newspaper corpus is also available, however, we found that the Czech model performed significantly better on Hungarian input due to the fact that Hungarian is typologically more similar to Czech than to English. We have found that zero-shot transfer of the PTG meaning representation across typologically not-too-distant languages using a neural parser model based on a multilingual contextual language model followed by a manual correction by linguist experts seems to be a viable scenario.
Theoretical and Practical Issues in the Semantic Annotation of Four Indigenous Languages
Authors: Jens E. L. Van Gysel, Meagan Vigus, Lukas Denk, Andrew Cowell, Rosa Vallejos, Tim O’Gorman, William Croft
Date: 2021-11-01 00:00:00
Proceeding: EMNLP (LAW, DMR) 2021 11

Computational resources such as semantically annotated corpora can play an important role in enabling speakers of indigenous minority languages to participate in government, education, and other domains of public life in their own language. However, many languages – mainly those with small native speaker populations and without written traditions – have little to no digital support. One hurdle in creating such resources is that for many languages, few speakers would be capable of annotating texts – a task which requires literacy and some linguistic training – and that these experts’ time is typically in high demand for language planning work. This paper assesses whether typologically trained non-speakers of an indigenous language can feasibly perform semantic annotation using Uniform Meaning Representations, thus allowing for the creation of computational materials without putting further strain on community resources.
Representing Implicit Positive Meaning of Negated Statements in AMR
Authors: Katharina Stein, Lucia Donatelli
Date: 2021-11-01 00:00:00
Proceeding: EMNLP (LAW, DMR) 2021 11
Tasks: Abstract Meaning Representation, Negation

Abstract Meaning Representation (AMR) has become popular for representing the meaning of natural language in graph structures. However, AMR does not represent scope information, posing a problem for its overall expressivity and specifically for drawing inferences from negated statements. This is the case with so-called “positive interpretations” of negated statements, in which implicit positive meaning is identified by inferring the opposite of the negation’s focus. In this work, we investigate how potential positive interpretations (PPIs) can be represented in AMR. We propose a logically motivated AMR structure for PPIs that makes the focus of negation explicit and sketch an initial proposal for a systematic methodology to generate this more expressive structure.
AutoAspect: Automatic Annotation of Tense and Aspect for Uniform Meaning Representations
Authors: Daniel Chen, Martha Palmer, Meagan Vigus
Date: 2021-11-01 00:00:00
Proceeding: EMNLP (LAW, DMR) 2021 11
Tasks: Sentence

We present AutoAspect, a novel, rule-based annotation tool for labeling tense and aspect. The pilot version annotates English data. The aspect labels are designed specifically for Uniform Meaning Representations (UMR), an annotation schema that aims to encode crosslingual semantic information. The annotation tool combines syntactic and semantic cues to assign aspects on a sentence-by-sentence basis, following a sequence of rules that each output a UMR aspect. Identified events proceed through the sequence until they are assigned an aspect. We achieve a recall of 76.17% for identifying UMR events and an accuracy of 62.57% on all identified events, with high precision values for 2 of the aspect labels.
Can predicate-argument relationships be extracted from UD trees?
Authors: Adam Ek, Jean-Philippe Bernardy, Stergios Chatzikyriakidis
Date: 2021-11-01 00:00:00
Proceeding: EMNLP (LAW, DMR) 2021 11
Tasks: Question Answering, Semantic Role Labeling

In this paper we investigate the possibility of extracting predicate-argument relations from UD trees (and enhanced UD graphs). Con- cretely, we apply UD parsers on an En- glish question answering/semantic-role label- ing data set (FitzGerald et al., 2018) and check if the annotations reflect the relations in the resulting parse trees, using a small number of rules to extract this information. We find that 79.1% of the argument-predicate pairs can be found in this way, on the basis of Ud- ify (Kondratyuk and Straka, 2019). Error anal- ysis reveals that half of the error cases are at- tributable to shortcomings in the dataset. The remaining errors are mostly due to predicate- argument relations not being extractible algo- rithmically from the UD trees (requiring se- mantic reasoning to be resolved). The parser itself is only responsible for a small portion of errors. Our analysis suggests a number of improvements to the UD annotation schema: we propose to enhance the schema in four ways, in order to capture argument-predicate relations. Additionally, we propose improve- ments regarding data collection for question answering/semantic-role labeling data.
Classifying Divergences in Cross-lingual AMR Pairs
Authors: Shira Wein, Nathan Schneider
Date: 2021-11-01 00:00:00
Proceeding: EMNLP (LAW, DMR) 2021 11
Tasks: Abstract Meaning Representation, Sentence, Translation

Translation divergences are varied and widespread, challenging approaches that rely on parallel text. To annotate translation divergences, we propose a schema grounded in the Abstract Meaning Representation (AMR), a sentence-level semantic framework instantiated for a number of languages. By comparing parallel AMR graphs, we can identify specific points of divergence. Each divergence is labeled with both a type and a cause. We release a small corpus of annotated English-Spanish data, and analyze the annotations in our corpus.