pwc-archive/papers-with-abstracts huggingface dataset:
On the Effects of Video Grounding on Language Models
Authors: Ehsan Doostmohammadi, Marco Kuhlmann
Date: 2022-10-01 00:00:00
Proceeding: MMMPIE (COLING) 2022 10
Tasks: Image Captioning, Question Answering, Video Grounding, Visual Question Answering, Visual Question Answering (VQA)

Transformer-based models trained on text and vision modalities try to improve the performance on multimodal downstream tasks or tackle the problem Transformer-based models trained on text and vision modalities try to improve the performance on multimodal downstream tasks or tackle the problem of lack of grounding, e.g., addressing issues like models’ insufficient commonsense knowledge. While it is more straightforward to evaluate the effects of such models on multimodal tasks, such as visual question answering or image captioning, it is not as well-understood how these tasks affect the model itself, and its internal linguistic representations. In this work, we experiment with language models grounded in videos and measure the models’ performance on predicting masked words chosen based on their imageability. The results show that the smaller model benefits from video grounding in predicting highly imageable words, while the results for the larger model seem harder to interpret.of lack of grounding, e.g., addressing issues like models’ insufficient commonsense knowledge. While it is more straightforward to evaluate the effects of such models on multimodal tasks, such as visual question answering or image captioning, it is not as well-understood how these tasks affect the model itself, and its internal linguistic representations. In this work, we experiment with language models grounded in videos and measure the models’ performance on predicting masked words chosen based on their imageability. The results show that the smaller model benefits from video grounding in predicting highly imageable words, while the results for the larger model seem harder to interpret.
Analyzing BERT Cross-lingual Transfer Capabilities in Continual Sequence Labeling
Authors: Juan Manuel Coria, Mathilde Veron, Sahar Ghannay, Guillaume Bernard, Hervé Bredin, Olivier Galibert, Sophie Rosset
Date: 2022-10-01 00:00:00
Proceeding: MMMPIE (COLING) 2022 10
Tasks: Continual Learning, Cross-Lingual Transfer, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), slot-filling, Slot Filling, Transfer Learning

Knowledge transfer between neural language models is a widely used technique that has proven to improve performance in a multitude of natural language tasks, in particular with the recent rise of large pre-trained language models like BERT. Similarly, high cross-lingual transfer has been shown to occur in multilingual language models. Hence, it is of great importance to better understand this phenomenon as well as its limits. While most studies about cross-lingual transfer focus on training on independent and identically distributed (i.e. i.i.d.) samples, in this paper we study cross-lingual transfer in a continual learning setting on two sequence labeling tasks: slot-filling and named entity recognition. We investigate this by training multilingual BERT on sequences of 9 languages, one language at a time, on the MultiATIS++ and MultiCoNER corpora. Our first findings are that forward transfer between languages is retained although forgetting is present. Additional experiments show that lost performance can be recovered with as little as a single training epoch even if forgetting was high, which can be explained by a progressive shift of model parameters towards a better multilingual initialization. We also find that commonly used metrics might be insufficient to assess continual learning performance.
Pixel-Level BPE for Auto-Regressive Image Generation
Authors: Anton Razzhigaev, Anton Voronov, Andrey Kaznacheev, Andrey Kuznetsov, Denis Dimitrov, Alexander Panchenko
Date: 2022-10-01 00:00:00
Proceeding: MMMPIE (COLING) 2022 10
Tasks: Image Generation

Pixel-level autoregression with Transformer models (Image GPT or iGPT) is one of the recent approaches to image generation that has not received massive attention and elaboration due to quadratic complexity of attention as it imposes huge memory requirements and thus restricts the resolution of the generated images. In this paper, we propose to tackle this problem by adopting Byte-Pair-Encoding (BPE) originally proposed for text processing to the image domain to drastically reduce the length of the modeled sequence. The obtained results demonstrate that it is possible to decrease the amount of computation required to generate images pixel-by-pixel while preserving their quality and the expressiveness of the features extracted from the model. Our results show that there is room for improvement for iGPT-like models with more thorough research on the way to the optimal sequence encoding techniques for images.
Cost-Effective Language Driven Image Editing with LX-DRIM
Authors: Rodrigo Santos, António Branco, João Ricardo Silva
Date: 2022-10-01 00:00:00
Proceeding: MMMPIE (COLING) 2022 10
Tasks: Visual Grounding

Cross-modal language and image processing is envisaged as a way to improve language understanding by resorting to visual grounding, but only recently, with the emergence of neural architectures specifically tailored to cope with both modalities, has it attracted increased attention and obtained promising results. In this paper we address a cross-modal task of language-driven image design, in particular the task of altering a given image on the basis of language instructions. We also avoid the need for a specifically tailored architecture and resort instead to a general purpose model in the Transformer family. Experiments with the resulting tool, LX-DRIM, show very encouraging results, confirming the viability of the approach for language-driven image design while keeping it affordable in terms of compute and data.
Developing a tool for fair and reproducible use of paid crowdsourcing in the digital humanities
Authors: Tuomo Hiippala, Helmiina Hotti, Rosa Suviranta
Date: 2022-10-01 00:00:00
Proceeding: LaTeCHCLfL (COLING) 2022 10

This system demonstration paper describes ongoing work on a tool for fair and reproducible use of paid crowdsourcing in the digital humanities. Paid crowdsourcing is widely used in natural language processing and computer vision, but has been rarely applied in the digital humanities due to ethical concerns. We discuss concerns associated with paid crowdsourcing and describe how we seek to mitigate them in designing the tool and crowdsourcing pipelines. We demonstrate how the tool may be used to create annotations for diagrams, a complex mode of expression whose description requires human input.
Using Language Models to Improve Rule-based Linguistic Annotation of Modern Historical Japanese Corpora
Authors: Jerry Bonnell, Mitsunori Ogihara
Date: 2022-10-01 00:00:00
Proceeding: LaTeCHCLfL (COLING) 2022 10
Tasks: Dependency Parsing, Domain Adaptation, Language Modelling, Unsupervised Domain Adaptation

Annotation of unlabeled textual corpora with linguistic metadata is a fundamental technology in many scholarly workflows in the digital humanities (DH). Pretrained natural language processing pipelines offer tokenization, tagging, and dependency parsing of raw text simultaneously using an annotation scheme like Universal Dependencies (UD). However, the accuracy of these UD tools remains unknown for historical texts and current methods lack mechanisms that enable helpful evaluations by domain experts. To address both points for the case of Modern Historical Japanese text, this paper proposes the use of unsupervised domain adaptation methods to develop a domain-adapted language model (LM) that can flag instances of inaccurate UD output from a pretrained LM and the use of these instances to form rules that, when applied, improves pretrained annotation accuracy. To test the efficacy of the proposed approach, the paper evaluates the domain-adapted LM against three baselines that are not adapted to the historical domain. The experiments conducted demonstrate that the domain-adapted LM improves UD annotation in the Modern Historical Japanese domain and that rules produced using this LM are best indicative of characteristics of the domain in terms of out-of-vocabulary rate and candidate normalized form discovery for “difficult” bigram terms.
To the Most Gracious Highness, from Your Humble Servant: Analysing Swedish 18th Century Petitions Using Text Classification
Authors: Ellinor Lindqvist, Eva Pettersson, Joakim Nivre
Date: 2022-10-01 00:00:00
Proceeding: LaTeCHCLfL (COLING) 2022 10
Tasks: Classification, Feature Importance, text-classification, Text Classification

Petitions are a rich historical source, yet they have been relatively little used in historical research. In this paper, we aim to analyse Swedish texts from around the 18th century, and petitions in particular, using automatic means of text classification. We also test how text pre-processing and different feature representations affect the result, and we examine feature importance for our main class of interest - petitions. Our experiments show that the statistical algorithms NB, RF, SVM, and kNN are indeed very able to classify different genres of historical text. Further, we find that normalisation has a positive impact on classification, and that content words are particularly informative for the traditional models. A fine-tuned BERT model, fed with normalised data, outperforms all other classification experiments with a macro average F1 score at 98.8. However, using less computationally expensive methods, including feature representation with word2vec, fastText embeddings or even TF-IDF values, with a SVM classifier also show good results for both unnormalise and normalised data. In the feature importance analysis, where we obtain the features most decisive for the classification models, we find highly relevant characteristics of the petitions, namely words expressing signs of someone inferior addressing someone superior.
Automatized Detection and Annotation for Calls to Action in Latin-American Social Media Postings
Authors: Wassiliki Siskou, Clara Giralt Mirón, Sarah Molina-Raith, Miriam Butt
Date: 2022-10-01 00:00:00
Proceeding: LaTeCHCLfL (COLING) 2022 10

Voter mobilization via social media has shown to be an effective tool. While previous research has primarily looked at how calls-to-action (CTAs) were used in Twitter messages from non-profit organizations and protest mobilization, we are interested in identifying the linguistic cues used in CTAs found on Facebook and Twitter for an automatic identification of CTAs. The work is part of an on-going collaboration with researchers from political science, who are investigating CTAs in the period leading up to recent elections in three different Latin American countries. We developed a new NLP pipeline for Spanish to facilitate their work. Our pipeline annotates social media posts with a range of linguistic information and then conducts targeted searches for linguistic cues that allow for an automatic annotation and identification of relevant CTAs. By using carefully crafted and linguistically informed heuristics, our system so far achieves an F1-score of 0.72.
The Distribution of Deontic Modals in Jane Austen’s Mature Novels
Authors: Lauren Levine
Date: 2022-10-01 00:00:00
Proceeding: LaTeCHCLfL (COLING) 2022 10

Deontic modals are auxiliary verbs which express some kind of necessity, obligation, or moral recommendation. This paper investigates the collocation and distribution within Jane Austen’s six mature novels of the following deontic modals: must, should, ought, and need. We also examine the co-occurrences of these modals with name mentions of the heroines in the six novels, categorizing each occurrence with a category of obligation if applicable. The paper offers a brief explanation of the categories of obligation chosen for this investigation. In order to examine the types of obligations associated with each heroine, we then investigate the distribution of these categories in relation to mentions of each heroine. The patterns observed show a general concurrence with the thematic characterizations of Austen’s heroines which are found in literary analysis.
Man vs. Machine: Extracting Character Networks from Human and Machine Translations
Authors: Aleksandra Konovalova, Antonio Toral
Date: 2022-10-01 00:00:00
Proceeding: LaTeCHCLfL (COLING) 2022 10

Most of the work on Character Networks to date is limited to monolingual texts. Conversely, in this paper we apply and analyze Character Networks on both source texts (English novels) and their Finnish translations (both human- and machine-translated). We assume that this analysis could provide some insights on changes in translations that could modify the character networks, as well as the narrative. The results show that the character networks of translations differ from originals in case of long novels, and the differences may also vary depending on the novel and translator’s strategy.
The COVID That Wasn’t: Counterfactual Journalism Using GPT
Authors: Sil Hamilton, Andrew Piper
Date: 2022-10-01 00:00:00
Proceeding: LaTeCHCLfL (COLING) 2022 10
Tasks: Articles, counterfactual, Language Modeling, Language Modelling, Text Generation

In this paper, we explore the use of large language models to assess human interpretations of real world events. To do so, we use a language model trained prior to 2020 to artificially generate news articles concerning COVID-19 given the headlines of actual articles written during the pandemic. We then compare stylistic qualities of our artificially generated corpus with a news corpus, in this case 5,082 articles produced by CBC News between January 23 and May 5, 2020. We find our artificially generated articles exhibits a considerably more negative attitude towards COVID and a significantly lower reliance on geopolitical framing. Our methods and results hold importance for researchers seeking to simulate large scale cultural processes via recent breakthroughs in text generation.
War and Pieces: Comparing Perspectives About World War I and II Across Wikipedia Language Communities
Authors: Ana Smith, Lillian Lee
Date: 2022-10-01 00:00:00
Proceeding: LaTeCHCLfL (COLING) 2022 10
Tasks: Articles, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), Text Generation

Wikipedia is widely used to train models for various tasks including semantic association, text generation, and translation. These tasks typically involve aligning and using text from multiple language editions, with the assumption that all versions of the article present the same content. But this assumption may not hold. We introduce a methodology for approximating the extent to which narratives of conflict may diverge in this scenario, focusing on articles about World War I and II battles written by Wikipedia’s communities of editors across four language editions. For simplicity, our unit of analysis representing each language communities’ perspectives is based on national entities and their subject-object-relation context, identified using named entity recognition and open-domain information extraction. Using a vector representation of these tuples, we evaluate how similarly different language editions portray how and how often these entities are mentioned in articles. Our results indicate that (1) language editions tend to reference associated countries more and (2) how much one language edition’s depiction overlaps with all others varies.
Computational Detection of Narrativity: A Comparison Using Textual Features and Reader Response
Authors: Max Steg, Karlo H. R. Slot, Federico Pianzola
Date: 2022-10-01 00:00:00
Proceeding: LaTeCHCLfL (COLING) 2022 10

The task of computational textual narrative detection focuses on detecting the presence of narrative parts, or the degree of narrativity in texts. In this work, we focus on detecting the local degree of narrativity in texts, using short text passages. We performed a human annotation experiment on 325 English texts ranging across 20 genres to capture readers’ perception by means of three cognitive aspects: suspense, curiosity, and surprise. We then employed a linear regression model to predict narrativity scores for 17,372 texts. When comparing our average annotation scores to similar annotation experiments with different cognitive aspects, we found that Pearson’s r ranges from .63 to .75. When looking at the calculated narrative probabilities, Pearson’s r is .91. We found that it is possible to use suspense, curiosity and surprise to detect narrativity. However, there are still differences between methods. This does not imply that there are inherently correct methods, but rather suggests that the underlying definition of narrativity is a determining factor for the results of the computational models employed.
Towards a contextualised spatial-diachronic history of literature: mapping emotional representations of the city and the country in Polish fiction from 1864 to 1939
Authors: Agnieszka Karlińska, Cezary Rosiński, Jan Wieczorek, Patryk Hubar, Jan Kocoń, Marek Kubis, Stanisław Woźniak, Arkadiusz Margraf, Wiktor Walentynowicz
Date: 2022-10-01 00:00:00
Proceeding: LaTeCHCLfL (COLING) 2022 10

In this article, we discuss the conditions surrounding the building of historical and literary corpora. We describe the assumptions and method of making the original corpus of the Polish novel (1864-1939). Then, we present the research procedure aimed at demonstrating the variability of the emotional value of the concept of “the city” and “the country” in the texts included in our corpus. The proposed method considers the complex socio-political nature of Central and Eastern Europe, especially the fact that there was no unified Polish state during this period. The method can be easily replicated in studies of the literature of countries with similar specificities.
Measuring Presence of Women and Men as Information Sources in News
Authors: Muitze Zulaika, Xabier Saralegi, Iñaki San Vicente
Date: 2022-10-01 00:00:00
Proceeding: LaTeCHCLfL (COLING) 2022 10

In the news, statements from information sources are often quoted, made by individuals who interact in the news. Detecting those quotes and the gender of their sources is a key task when it comes to media analysis from a gender perspective. It is a challenging task: the structure of the quotes is variable, gender marks are not present in many languages, and quote authors are often omitted due to frequent use of coreferences. This paper proposes a strategy to measure the presence of women and men as information sources in news. We approach the problem of detecting sentences including quotes and the gender of the speaker as a joint task, by means of a supervised multiclass classifier of sentences. We have created the first datasets for Spanish and Basque by manually annotating quotes and the gender of the associated sources in news items. The results obtained show that BERT based approaches are significantly better than bag-of-words based classical ones, achieving accuracies close to 90%. We also analyse a bilingual learning strategy and generating additional training examples synthetically; both provide improvements up to 3.4% and 5.6%, respectively.
RACAI@SMM4H’22: Tweets Disease Mention Detection Using a Neural Lateral Inhibitory Mechanism
Authors: Andrei-Marius Avram, Vasile Pais, Maria Mitrofan
Date: 2022-10-01 00:00:00
Proceeding: SMM4H (COLING) 2022 10

This paper presents our system employed for the Social Media Mining for Health (SMM4H) 2022 competition Task 10 - SocialDisNER. The goal of the task was to improve the detection of diseases in tweets. Because the tweets were in Spanish, we approached this problem using a system that relies on a pre-trained multilingual model and is fine-tuned using the recently introduced lateral inhibition layer. We further experimented on this task by employing a conditional random field on top of the system and using a voting-based ensemble that contains various architectures. The evaluation results outlined that our best performing model obtained 83.7% F1-strict on the validation set and 82.1% F1-strict on the test set.
PingAnTech at SMM4H task1: Multiple pre-trained model approaches for Adverse Drug Reactions
Authors: Xi Liu, Han Zhou, Chang Su
Date: 2022-10-01 00:00:00
Proceeding: SMM4H (COLING) 2022 10
Tasks: Language Modeling, Language Modelling

This paper describes the solution for the Social Media Mining for Health (SMM4H) 2022 Shared Task. We participated in Task1a., Task1b. and Task1c. To solve the problem of the presence of Twitter data, we used a pre-trained language model. We used training strategies that involved: adversarial training, head layer weighted fusion, etc., to improve the performance of the model. The experimental results show the effectiveness of our designed system. For task 1a, the system achieved an F1 score of 0.68; for task 1b Overlapping F1 score of 0.65 and a Strict F1 score of 0.49. Task 1c yields Overlapping F1 and Strict F1 scores of 0.36 and 0.30, respectively.
dezzai@SMM4H’22: Tasks 5 & 10 - Hybrid models everywhere
Authors: Miguel Ortega-Martín, Alfonso Ardoiz, Oscar Garcia, Jorge Álvarez, Adrián Alonso
Date: 2022-10-01 00:00:00
Proceeding: SMM4H (COLING) 2022 10

This paper presents our approaches to SMM4H’22 task 5 - Classification of tweets of self-reported COVID-19 symptoms in Spanish, and task 10 - Detection of disease mentions in tweets – SocialDisNER (in Spanish). We have presented hybrid systems that combine Deep Learning techniques with linguistic rules and medical ontologies, which have allowed us to achieve outstanding results in both tasks.
zydhjh4593@SMM4H’22: A Generic Pre-trained BERT-based Framework for Social Media Health Text Classification
Authors: Chenghao Huang, Xiaolu Chen, Yuxi Chen, Yutong Wu, Weimin Yuan, Yan Wang, Yanru Zhang
Date: 2022-10-01 00:00:00
Proceeding: SMM4H (COLING) 2022 10
Tasks: text-classification, Text Classification

This paper describes our proposed framework for the 10 text classification tasks of Task 1a, 2a, 2b, 3a, 4, 5, 6, 7, 8, and 9, in the Social Media Mining for Health (SMM4H) 2022. According to the pre-trained BERT-based models, various techniques, including regularized dropout, focal loss, exponential moving average, 5-fold cross-validation, ensemble prediction, and pseudo-labeling, are applied for further formulating and improving the generalization performance of our framework. In the evaluation, the proposed framework achieves the 1st place in Task 3a with a 7% higher F1-score than the median, and obtains a 4% higher averaged F1-score than the median in all participating tasks except Task 1a.
MANTIS at SMM4H’2022: Pre-Trained Language Models Meet a Suite of Psycholinguistic Features for the Detection of Self-Reported Chronic Stress
Authors: Sourabh Zanwar, Daniel Wiechmann, Yu Qiao, Elma Kerz
Date: 2022-10-01 00:00:00
Proceeding: SMM4H (COLING) 2022 10

This paper describes our submission to Social Media Mining for Health (SMM4H) 2022 Shared Task 8, aimed at detecting self-reported chronic stress on Twitter. Our approach leverages a pre-trained transformer model (RoBERTa) in combination with a Bidirectional Long Short-Term Memory (BiLSTM) network trained on a diverse set of psycholinguistic features. We handle the class imbalance issue in the training dataset by augmenting it by another dataset used for stress classification in social media.