pwc-archive/papers-with-abstracts huggingface dataset:
``Voices of the Great War'': A Richly Annotated Corpus of Italian Texts on the First World War
Authors: Federico Boschetti, Irene De Felice, Stefano Dei Rossi, Felice Dell{'}Orletta, Michele Di Giorgio, Martina Miliani, Lucia C. Passaro, Angelica Puddu, Giulia Venturi, Nicola Labanca, Aless Lenci, ro, Simonetta Montemagni
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5

{``}Voices of the Great War{''} is the first large corpus of Italian historical texts dating back to the period of First World War. This corpus differs from other existing resources in several respects. First, from the linguistic point of view it gives account of the wide range of varieties in which Italian was articulated in that period, namely from a diastratic (educated vs. uneducated writers), diaphasic (low/informal vs. high/formal registers) and diatopic (regional varieties, dialects) points of view. From the historical perspective, through a collection of texts belonging to different genres it represents different views on the war and the various styles of narrating war events and experiences. The final corpus is balanced along various dimensions, corresponding to the textual genre, the language variety used, the author type and the typology of conveyed contents. The corpus is fully annotated with lemmas, part-of-speech, terminology, and named entities. Significant corpus samples representative of the different {``}voices{''} have also been enriched with meta-linguistic and syntactic information. The layer of syntactic annotation forms the first nucleus of an Italian historical treebank complying with the Universal Dependencies standard. The paper illustrates the final resource, the methodology and tools used to build it, and the Web Interface for navigating it.
DEbateNet-mig15:Tracing the 2015 Immigration Debate in Germany Over Time
Authors: Gabriella Lapesa, Andre Blessing, Nico Blokker, Erenay Dayanik, Sebastian Haunss, Jonas Kuhn, Sebastian Pad{\'o}
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Articles

DEbateNet-migr15 is a manually annotated dataset for German which covers the public debate on immigration in 2015. The building block of our annotation is the political science notion of a claim, i.e., a statement made by a political actor (a politician, a party, or a group of citizens) that a specific action should be taken (e.g., vacant flats should be assigned to refugees). We identify claims in newspaper articles, assign them to actors and fine-grained categories and annotate their polarity and date. The aim of this paper is two-fold: first, we release the full DEbateNet-mig15 corpus and document it by means of a quantitative and qualitative analysis; second, we demonstrate its application in a discourse network analysis framework, which enables us to capture the temporal dynamics of the political debate
A Corpus of Spanish Political Speeches from 1937 to 2019
Authors: Elena {\'A}lvarez-Mellado
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Navigate

This paper documents a corpus of political speeches in Spanish. The documents in the corpus belong to the Christmas speeches that have been delivered yearly by the head of state of Spain since 1937. The historical period covered by these speeches ranges from the Spanish Civil War and the Francoist dictatorship up until today. As a result, the corpus reflects some of the most significant events and political changes in the recent history of Spain. Up until now, the speeches as a whole had not been collected into a single, systematic and reusable resource, as most of the texts were scattered among different sources. The paper describes: (1) the composition of the corpus; (2) the Python interface that facilitates querying and analyzing the corpus using the NLTK and spaCy libraries and (3) a set of HTML visualizations aimed at the general public to navigate the corpus and explore differences between TF-IDF frequencies.
A New Latin Treebank for Universal Dependencies: Charters between Ancient Latin and Romance Languages
Authors: Flavio Massimiliano Cecchini, Timo Korkiakangas, Marco Passarotti
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5

The present work introduces a new Latin treebank that follows the Universal Dependencies (UD) annotation standard. The treebank is obtained from the automated conversion of the Late Latin Charter Treebank 2 (LLCT2), originally in the Prague Dependency Treebank (PDT) style. As this treebank consists of Early Medieval legal documents, its language variety differs considerably from both the Classical and Medieval learned varieties prevalent in the other currently available UD Latin treebanks. Consequently, besides significant phenomena from the perspective of diachronic linguistics, this treebank also poses several challenging technical issues for the current and future syntactic annotation of Latin in the UD framework. Some of the most relevant cases are discussed in depth, with comparisons between the original PDT and the resulting UD annotations. Additionally, an overview of the UD-style structure of the treebank is given, and some diachronic aspects of the transition from Latin to Romance languages are highlighted.
Identification of Indigenous Knowledge Concepts through Semantic Networks, Spelling Tools and Word Embeddings
Authors: Renato Rocha Souza, Amelie Dorn, Barbara Piringer, W, Eveline l-Vogt
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Word Embeddings

In order to access indigenous, regional knowledge contained in language corpora, semantic tools and network methods are most typically employed. In this paper we present an approach for the identification of dialectal variations of words, or words that do not pertain to High German, on the example of non-standard language legacy collection questionnaires of the Bavarian Dialects in Austria (DB{\"O}). Based on selected cultural categories relevant to the wider project context, common words from each of these cultural categories and their lemmas using GermaLemma were identified. Through word embedding models the semantic vicinity of each word was explored, followed by the use of German Wordnet (Germanet) and the Hunspell tool. Whilst none of these tools have a comprehensive coverage of standard German words, they serve as an indication of dialects in specific semantic hierarchies. Methods and tools applied in this study may serve as an example for other similar projects dealing with non-standard or endangered language collections, aiming to access, analyze and ultimately preserve native regional language heritage.
A Multi-Orthography Parallel Corpus of Yiddish Nouns
Authors: Jonne Saleva
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Grapheme-to-Phoneme Conversion, Transliteration

Yiddish is a low-resource language belonging to the Germanic language family and written using the Hebrew alphabet. As a language, Yiddish can be considered resource-poor as it lacks both public accessible corpora and a widely-used standard orthography, with various countries and organizations influencing the spellings speakers use. While existing corpora of Yiddish text do exist, they are often only written in a single, potentially non-standard orthography, with no parallel version with standard orthography available. In this work, we introduce the first multi-orthography parallel corpus of Yiddish nouns built by scraping word entries from Wiktionary. We also demonstrate how the corpus can be used to bootstrap a transliteration model using the Sequitur-G2P grapheme-to-phoneme conversion toolkit to map between various orthographies. Our trained system achieves error rates between 16.79{\%} and 28.47{\%} on the test set, depending on the orthographies considered. In addition to quantitative analysis, we also conduct qualitative error analysis of the trained system, concluding that non-phonetically spelled Hebrew words are the largest cause of error. We conclude with remarks regarding future work and release the corpus and associated code under a permissive license for the larger community to use.
An Annotated Corpus of Adjective-Adverb Interfaces in Romance Languages
Authors: Katharina Gerhalter, Gerlinde Schneider, Christopher Pollin, Martin Hummel
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5

The final outcome of the project Open Access Database: Adjective-Adverb Interfaces in Romance is an annotated and lemmatised corpus of various linguistic phenomena related to Romance adjectives with adverbial functions. The data is published under open-access and aims to serve linguistic research based on transparent and accessible corpus-based data. The annotation model was developed to offer a cross-linguistic categorization model for the heterogeneous word-class {``}adverb{''}, based on its diverse forms, functions and meanings. The project focuses on the interoperability and accessibility of data, with particular respect to reusability in the sense of the FAIR Data Principles. Topics presented by this paper include data compilation and creation, annotation in XML/TEI, data preservation and publication process by means of the GAMS repository and accessibility via a search interface. These aspects are tied together by semantic technologies, using an ontology-based approach.
Language Resources for Historical Newspapers: the Impresso Collection
Authors: Maud Ehrmann, Matteo Romanello, Simon Clematide, Phillip Benjamin Str{\"o}bel, Rapha{\"e}l Barman
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5

Following decades of massive digitization, an unprecedented amount of historical document facsimiles can now be retrieved and accessed via cultural heritage online portals. If this represents a huge step forward in terms of preservation and accessibility, the next fundamental challenge{--} and real promise of digitization{--} is to exploit the contents of these digital assets, and therefore to adapt and develop appropriate language technologies to search and retrieve information from this {`}Big Data of the Past{'}. Yet, the application of text processing tools on historical documents in general, and historical newspapers in particular, poses new challenges, and crucially requires appropriate language resources. In this context, this paper presents a collection of historical newspaper data sets composed of text and image resources, curated and published within the context of the {`}impresso - Media Monitoring of the Past{'} project. With corpora, benchmarks, semantic annotations and language models in French, German and Luxembourgish covering ca. 200 years, the objective of the impresso resource collection is to contribute to historical language resources, and thereby strengthen the robustness of approaches to non-standard inputs and foster efficient processing of historical documents.
Allgemeine Musikalische Zeitung as a Searchable Online Corpus
Authors: Bernd Kampe, Tinghui Duan, Udo Hahn
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Philosophy

The massive digitization efforts related to historical newspapers over the past decades have focused on mass media sources and ordinary people as their primary recipients. Much less attention has been paid to newspapers published for a more specialized audience, e.g., those aiming at scholarly or cultural exchange within intellectual communities much narrower in scope, such as newspapers devoted to music criticism, arts or philosophy. Only some few of these specialized newspapers have been digitized up until now, but they are usually not well curated in terms of digitization quality, data formatting, completeness, redundancy (de-duplication), supply of metadata, and, hence, searchability. This paper describes our approach to eliminate these drawbacks for a major German-language newspaper resource of the Romantic Age, the Allgemeine Musikalische Zeitung (General Music Gazette). We here focus on a workflow that copes with a posteriori digitization problems, inconsistent OCRing and index building for searchability. In addition, we provide a user-friendly graphic interface to empower content-centric access to this (and other) digital resource(s) adopting open-source software for the purpose of Web presentation.
Stylometry in a Bilingual Setup
Authors: Silvie Cinkova, Jan Rybicki
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Language Modelling

The method of stylometry by most frequent words does not allow direct comparison of original texts and their translations, i.e. across languages. For instance, in a bilingual Czech-German text collection containing parallel texts (originals and translations in both directions, along with Czech and German translations from other languages), authors would not cluster across languages, since frequency word lists for any Czech texts are obviously going to be more similar to each other than to a German text, and the other way round. We have tried to come up with an interlingua that would remove the language-specific features and possibly keep the linguistically independent features of individual author signal, if they exist. We have tagged, lemmatized, and parsed each language counterpart with the corresponding language model in UDPipe, which provides a linguistic markup that is cross-lingual to a significant extent. We stripped the output of language-dependent items, but that alone did not help much. As a next step, we transformed the lemmas of both language counterparts into shared pseudolemmas based on a very crude Czech-German glossary, with a 95.6{\%} success. We show that, for stylometric methods based on the most frequent words, we can do without translations.
Dialect Clustering with Character-Based Metrics: in Search of the Boundary of Language and Dialect
Authors: Yo Sato, Kevin Heffernan
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Clustering, Sentence

We present in this work a universal, character-based method for representing sentences so that one can thereby calculate the distance between any two sentence pair. With a small alphabet, it can function as a proxy of phonemes, and as one of its main uses, we carry out dialect clustering: cluster a dialect/sub-language mixed corpus into sub-groups and see if they coincide with the conventional boundaries of dialects and sub-languages. By using data with multiple Japanese dialects and multiple Slavic languages, we report how well each group clusters, in a manner to partially respond to the question of what separates languages from dialects.
ThemePro: A Toolkit for the Analysis of Thematic Progression
Authors: Monica Dominguez, Juan Soler, Leo Wanner
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Text Generation

This paper introduces ThemePro, a toolkit for the automatic analysis of thematic progression. Thematic progression is relevant to natural language processing (NLP) applications dealing, among others, with discourse structure, argumentation structure, natural language generation, summarization and topic detection. A web platform demonstrates the potential of this toolkit and provides a visualization of the results including syntactic trees, hierarchical thematicity over propositions and thematic progression over whole texts.
Machine-Aided Annotation for Fine-Grained Proposition Types in Argumentation
Authors: Yohan Jo, Elijah Mayfield, Chris Reed, Eduard Hovy
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: BIG-bench Machine Learning, Hybrid Machine Learning

We introduce a corpus of the 2016 U.S. presidential debates and commentary, containing 4,648 argumentative propositions annotated with fine-grained proposition types. Modern machine learning pipelines for analyzing argument have difficulty distinguishing between types of propositions based on their factuality, rhetorical positioning, and speaker commitment. Inability to properly account for these facets leaves such systems inaccurate in understanding of fine-grained proposition types. In this paper, we demonstrate an approach to annotating for four complex proposition types, namely normative claims, desires, future possibility, and reported speech. We develop a hybrid machine learning and human workflow for annotation that allows for efficient and reliable annotation of complex linguistic phenomena, and demonstrate with preliminary analysis of rhetorical strategies and structure in presidential debates. This new dataset and method can support technical researchers seeking more nuanced representations of argument, as well as argumentation theorists developing new quantitative analyses.
Chinese Discourse Parsing: Model and Evaluation
Authors: Lin Chuan-An, Shyh-Shiun Hung, Hen-Hsen Huang, Hsin-Hsi Chen
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Binarization, Discourse Parsing, model

Chinese discourse parsing, which aims to identify the hierarchical relationships of Chinese elementary discourse units, has not yet a consistent evaluation metric. Although Parseval is commonly used, variations of evaluation differ from three aspects: micro vs. macro F1 scores, binary vs. multiway ground truth, and left-heavy vs. right-heavy binarization. In this paper, we first propose a neural network model that unifies a pre-trained transformer and CKY-like algorithm, and then compare it with the previous models with different evaluation scenarios. The experimental results show that our model outperforms the previous systems. We conclude that (1) the pre-trained context embedding provides effective solutions to deal with implicit semantics in Chinese texts, and (2) using multiway ground truth is helpful since different binarization approaches lead to significant differences in performance.
Shallow Discourse Parsing for Under-Resourced Languages: Combining Machine Translation and Annotation Projection
Authors: Henny Sluyter-G{\"a}thje, Peter Bourgonje, Manfred Stede
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Discourse Parsing, Machine Translation, Translation

Shallow Discourse Parsing (SDP), the identification of coherence relations between text spans, relies on large amounts of training data, which so far exists only for English - any other language is in this respect an under-resourced one. For those languages where machine translation from English is available with reasonable quality, MT in conjunction with annotation projection can be an option for producing an SDP resource. In our study, we translate the English Penn Discourse TreeBank into German and experiment with various methods of annotation projection to arrive at the German counterpart of the PDTB. We describe the key characteristics of the corpus as well as some typical sources of errors encountered during its creation. Then we evaluate the GermanPDTB by training components for selected sub-tasks of discourse parsing on this silver data and compare performance to the same components when trained on the gold, original PDTB corpus.
A Corpus of Encyclopedia Articles with Logical Forms
Authors: Nathan Rasmussen, William Schuler
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Articles

People can extract precise, complex logical meanings from text in documents such as tax forms and game rules, but language processing systems lack adequate training and evaluation resources to do these kinds of tasks reliably. This paper describes a corpus of annotated typed lambda calculus translations for approximately 2,000 sentences in Simple English Wikipedia, which is assumed to constitute a broad-coverage domain for precise, complex descriptions. The corpus described in this paper contains a large number of quantifiers and interesting scoping configurations, and is presented specifically as a resource for quantifier scope disambiguation systems, but also more generally as an object of linguistic study.
The Potsdam Commentary Corpus 2.2: Extending Annotations for Shallow Discourse Parsing
Authors: Peter Bourgonje, Manfred Stede
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Discourse Parsing, Relation

We present the Potsdam Commentary Corpus 2.2, a German corpus of news editorials annotated on several different levels. New in the 2.2 version of the corpus are two additional annotation layers for coherence relations following the Penn Discourse TreeBank framework. Specifically, we add relation senses to an already existing layer of discourse connectives and their arguments, and we introduce a new layer with additional coherence relation types, resulting in a German corpus that mirrors the PDTB. The aim of this is to increase usability of the corpus for the task of shallow discourse parsing. In this paper, we provide inter-annotator agreement figures for the new annotations and compare corpus statistics based on the new annotations to the equivalent statistics extracted from the PDTB.
On the Creation of a Corpus for Coherence Evaluation of Discursive Units
Authors: Elham Mohammadi, Timothe Beiko, Leila Kosseim
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Coherence Evaluation, Sentence, Sentence Ordering

In this paper, we report on our experiments towards the creation of a corpus for coherence evaluation. Most corpora for textual coherence evaluation are composed of randomly shuffled sentences that focus on sentence ordering, regardless of whether the sentences were originally related by a discourse relation. To the best of our knowledge, no publicly available corpus has been designed specifically for the evaluation of coherence of known discursive units. In this paper, we focus on coherence modeling at the intra-discursive level and describe our approach to build a corpus of incoherent pairs of sentences. We experimented with a variety of corruption strategies to create synthetic incoherent pairs of discourse arguments from coherent ones. Using discourse argument pairs from the Penn Discourse Tree Bank, we generate incoherent discourse argument pairs, by swapping either their discourse connective or a discourse argument. To evaluate how incoherent the generated corpora are, we use a convolutional neural network to try to distinguish the original pairs from the corrupted ones. Results of the classifier as well as a manual inspection of the corpora show that generating such corpora is still a challenge as the generated instances are clearly not {``}incoherent enough{''}, indicating that more effort should be spent on developing more robust ways of generating incoherent corpora.
Joint Learning of Syntactic Features Helps Discourse Segmentation
Authors: Takshak Desai, Parag Pravin Dakle, Dan Moldovan
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Discourse Segmentation, token-classification

This paper describes an accurate framework for carrying out multi-lingual discourse segmentation with BERT (Devlin et al., 2019). The model is trained to identify segments by casting the problem as a token classification problem and jointly learning syntactic features like part-of-speech tags and dependency relations. This leads to significant improvements in performance. Experiments are performed in different languages, such as English, Dutch, German, Portuguese Brazilian and Basque to highlight the cross-lingual effectiveness of the segmenter. In particular, the model achieves a state-of-the-art F-score of 96.7 for the RST-DT corpus (Carlson et al., 2003) improving on the previous best model by 7.2{\%}. Additionally, a qualitative explanation is provided for how proposed changes contribute to model performance by analyzing errors made on the test data.