Croatia

Towards computational systems capable of dealing with complex and general linguistic phenomena, it is essential to understand figurative language, which verbal humor is an instance of. This paper reports state-of-the-art results for Humor Recognition in Portuguese, specifically, an F1-score of 99.6% with a BERTbased classifier. However, following the surprising high performance in such a challenging task, we further analyzed what was actually learned by the classifiers. Our main conclusions were that classifiers based on contentfeatures achieve the best performance, but rely mostly on stylistic aspects of the text, not necessarily related to humor, such as punctuation and question words. On the other hand, for humor-related features, we identified some important aspects, such as the presence of named entities, ambiguity and incongruity.

EACL 2023

What do Humor Classifiers Learn? An Attempt to Explain Humor Recognition Models

verbal humor

machine learning explainability

humor recognition

### Welcome to the 17th Conference of the European Chapter of the Association for Computational Linguistics (EACL) 
Continuing its mission of expanding and involving the science community of all European countries, EACL had selected the Ukrainian community for the 16th EACL, which took place online due to the COVID pandemic. Unfortunately, the ongoing war made the organisation in Kyiv impossible. Considering the importance of physical interaction among researchers, especially after the restrictions imposed by the COVID pandemic, in addition to an online mode, the conference will be held in Dubrovnik, Croatia, from 2 to 6 of May, 2023. The original aim of strengthening the connection with the Ukrainian community will not change as our program will feature a dedicated session and a workshop to highlight work on Ukrainian language technologies. As the flagship European conference in the field of computational linguistics, EACL welcomes European and international researchers covering a broad spectrum of research areas that are concerned with computational approaches to natural language.

You need to register for the conference in order to access this site. Please visit https://2023.eacl.org/registration for more information.

As the flagship European conference in the field of computational linguistics, EACL welcomes European and international researchers covering a broad spectrum of research areas that are concerned with computational approaches to natural language.

workshop paper

In this paper, we present approaches for the automated extraction and disambiguation of a part of the stylistic device Vossian Antonomasia (VA), namely the target entity described by the expression. We model the problem as a coreference resolution and a question answering task and also combine both. To tackle the tasks at hand, we utilize state-of-the-art models in these areas. In addition, we visualize the connection between source and target entities of VA in a web demo to provide a deeper understanding of their mutual relationship.

Who is the Madonna of Italian-American Literature?: Extracting and Analyzing Target Entities of Vossian Antonomasia

In this paper we present the first information extraction system able to capture smell events, including smell words, smell sources and qualities. We then apply the system to four English corpora, covering a time period between 15th and 20th century. Then, starting from the extracted data, we adapt an existing approach to semantic shift detection to capture which smell sources underwent a change in the way their odour was perceived before and after 1800. We find correspondences between the extracted items and the output of historical research concerning the smell of tobacco and tea. Despite the limited amount of data, the results are promising and indicate that this research can yield valuable insights in the area of diachronical sensory analysis. In the future, we intend to broaden the scope of our data and conduct more comprehensive analyses on a greater variety of smell sources.

Scent Mining: Extracting Olfactory Events, Smell Sources and Qualities

This study relies on natural language processing to explore the nature of online communication in Russia during the war on Ukraine in 2022. The analysis of a large corpus of publications in traditional media and on social media identifies massive state interventions aimed at manipulating public opinion. The study relies on expertise in media studies and political science to trace the major themes and strategies of propagandist narratives on three major Russian social media platforms over several months as well as their perception by the users. Distributions of several keyworded pro-war and anti-war topics are examined to reveal the cross-platform specificity of social media audiences. We release WarMM-2022, a 1.7 M posts corpus. This corpus includes publications related to the Russia-Ukraine war, which appeared in Russian mass media (February to September 2022) and on social networks (July to September 2022). The corpus can be useful for the development of NLP approaches to propaganda detection and subsequent studies of propaganda campaigns in social sciences in addition to traditional methods, such as content analysis, focus groups, surveys.

Wartime Media Monitor (WarMM-2022): A Study of Information Manipulation on Russian Social Media during the Russia-Ukraine War

Named Entity Annotation Projection Applied to Classical Languages

This paper investigates the use of standard and non-standard adverbial markers in modern Chinese literature. In Chinese, adverbials can be derived from many adjectives, adverbs and verbs with the suffix ``de''. The suffix has a standard and a non-standard written form, both of which are frequently used. Contrastive research on these two competing forms has mostly been qualitative or limited to small text samples. In this first large-scale quantitative study, we present statistics on 346 adverbial types from an 8-million-character text corpus drawn from Chinese literature in the 20th century. We present a semantic analysis of the verbs modified by adverbs with standard and non-standard markers, and a chronological analysis of marker choice among six prominent modern Chinese authors. We show that the non-standard form is more frequently used when the adverbial modifies an emotion verb. Further, we demonstrate that marker choice is correlated to text genre and register, as well as the writing style of the author.

Standard and Non-standard Adverbial Markers: a Diachronic Analysis in Modern Chinese Literature

Quotes are universally appealing. Humans recognize good quotes and save them for later reference. However, it may pose a challenge for machines. In this work, we build a new corpus of quotes and propose a new task, quote detection, as a type of span detection. We retrieve the quote set from Goodreads and collect the spans through a custom search on the Gutenberg Book Corpus. We run two types of baselines for quote detection: Conditional random field (CRF) and summarization with pointer-generator networks and Bidirectional and Auto-Regressive Transformers (BART). The results show that the neural sequence-to-sequence models perform substantially better than CRF. From the viewpoint of neural extractive summarization, quote detection seems easier than news summarization. Moreover, model fine-tuning on our corpus and the Cornell Movie-Quotes Corpus introduces incremental performance boosts. Finally, we provide a qualitative analysis to gain insight into the performance.

Quote Detection: A New Task and Dataset for NLP

Authorship verification is used to link texts written by the same author without needing a model per author, making it useful for de-anonymizing users spreading text with malicious intent. Recent advances in Transformer-based language models hold great promise for author verification, though short context lengths and non-diverse training regimes present challenges for their practical application. In this work, we investigate the effect of these challenges in the application of a Cross-Encoder Transformer-based author verification system under multiple conditions. We perform experiments with four Transformer backbones using differently tuned variants of fanfiction data and found that our BigBird pipeline outperformed Longformer, RoBERTa, and ELECTRA and performed competitively against the official top ranked system from the PAN evaluation.
We also examined the effect of authors and fandoms not seen in training on model performance. Through this, we found fandom has the greatest influence on true trials, pairs of text written by the same author, and that a balanced training dataset in terms of class and fandom performed the most consistently.

Improving Long-Text Authorship Verification via Model Selection and Data Tuning

We present a dataset and system for quote attribution in Dutch literature. The system is implemented as a neural module in an existing NLP pipeline for Dutch literature (dutchcoref; van Cranenburgh, 2019). Our contributions are as follows. First, we provide guidelines for Dutch quote attribution and annotate 3,056 quotes in fragments of 42 Dutch literary novels, both contemporary and classic. Second, we present three neural quote attribution classifiers, optimizing for precision, recall, and
F1. Third, we perform an evaluation and analysis of quote attribution performance, showing that in particular, quotes with an implicit speaker are challenging, and that such quotes are prevalent in contemporary fiction (57%, compared to 32% for classic novels). On the task of quote attribution, we achieve an improvement over the rule-based baseline of 8.0% F1 points on contemporary fiction and 1.9% F1 points on classic novels. Code, models, and annotations for the public domain novels are available under an open license at https://github.com/frenkvdberg/dutchqa

Direct Speech Quote Attribution for Dutch Literature

The subject of this article is the application of NLP and text-mining methods to the analysis of two large bibliographies: a Polish one, based on the catalogs of the National Library in Warsaw, and a German one, created by the Deutsche Nationalbibliothek. The data in both collections are stored in MARC 21 format, allowing the selection of relevant fields that are used for further processing (basically author, title, and date). The volume of the Polish corpus (after filtering out non-relevant or incomplete items) includes 1.4 mln of records, and that of the German corpus 7.5 mln records. The time span of both bibliographies extends from 1801 to 2021. The aim of the study is to compare the gender distribution of book authors in Polish and German databases over more than two centuries. The proportions of male and female authors since 1801 were calculated automatically, and NLP methods such as document vector embeddings based on deep BERT networks were used to extract topics from titles. The gender of the Polish authors was recognized based on the morphology of the first names, and that of the German authors based on a predefined list. The study found that the proportion of female authors has been steadily increasing both in Poland and in German countries (currently around 43%). However, the topics of women's and men's writings invariably remain different since 1801.

Large Bibliographies as a Source of Data for the Humanities – NLP in the Analysis of Gender of Book Authors in German Countries and in Poland (1801-2021)

Development funds are essential to finance climate change adaptation and are thus an important part of international climate policy. However, the absence of a common reporting practice makes it difficult to assess the amount and distribution of such funds. This problem has attracted attention in international affairs research and is increasingly being investigated using methods of the broader field of computational social science. Lately, the mentioned research field has questioned the credibility of
reported figures, indicating that adaptation financing is in fact lower than published figures suggest. Projects claiming a greater relevance to climate change adaptation than they target are referred to as "overreported". To estimate realistic rates of overreporting in large data sets over time, we propose an approach based on
state-of-the-art text classification. To date, assessments of credibility have relied on small, manually evaluated samples. We use such a sample data set to train a classifier with an accuracy of 89.81% ± 0.83% (tenfold crossvalidation) and extrapolate to larger data sets to identify overreporting. Additionally, we propose a method that incorporates evidence of smaller, higher-quality data to correct predicted rates using Bayes’ theorem. This enables a comparison of different annotation schemes to
estimate the degree of overreporting in climate change adaptation. Our results support findings that indicate extensive overreporting of 32.03% with a credible interval of [19.81%; 48.34%].

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