Korea, Republic of

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 &quot;difficult&quot; bigram terms.

COLING 2022

Using Language Models to Improve Rule-based Linguistic Annotation of Modern Historical Japanese Corpora

modern historical japanese

historical text

linguistic annotation

rule-based

cultural heritage

universal dependencies

language modeling

transformers

domain adaptation

unsupervised

# Welcome everyone to ACL 2022!

The 60th Annual Meeting of the Association for Computational Linguistics is taking place May 22-27, 2022 as a hybrid event, in Dublin and online. We are happy to welcome all of you to this anniversary edition with an almost 50-50 in-person and virtual participation. 
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ACL 2022

The Association for Computational Linguistics (ACL) is the premier international scientific and professional society for people working on computational problems involving human language, a field often referred to as either computational linguistics or natural language processing (NLP). 

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.

workshop paper

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COLING, the International Conference on Computational Linguistics, is one of the premier conferences for the natural language processing and computational linguistics.

Generating a short story out of an image is arduous. Unlike image captioning, story generation from an image poses multiple challenges: preserving the story coherence, appropriately assessing the quality of the story,
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2) enabling a more style-relevant generation
by incorporating stylistic adapters to control
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and human evaluation. The results show that
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Every picture tells a story: Image-grounded controllable stylistic story generation

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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.

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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.

Man vs. Machine: Extracting Character Networks from Human and Machine Translations

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.

The COVID That Wasn't: Counterfactual Journalism Using GPT

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.

War and Pieces: Comparing Perspectives About World War I and II Across Wikipedia Language Communities

The task of computational textual narrative de- tection 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 annota- tion 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 proba- bilities, 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 defini- tion of narrativity is a determining factor for the results of the computational models employed.

Computational Detection of Narrativity: A Comparison Using Textual Features and Reader Response

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 the studies of the literature of countries with similar specificities.

Towards a contextualised spatial-diachronic history of literature: mapping emotional representations of the city and the country in Polish fiction from 1864 to 1939

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.

 Measuring Presence of Women and Men as Information Sources in News

This paper presents a description of our system in SMM4H-2022, where we participated in task 1a, task 4, and task 6 to task 10. There are three main challenges in SMM4H-2022, namely the domain shift problem, the prediction bias due to category imbalance, and the noise in infor mal text. In this paper, we propose a unified framework for the classification and named en- tity recognition tasks to solve the challenges, and it can be applied to both English and Spanish scenarios. The results of our system are higher than the median F1-scores for 7 tasks and significantly exceed the F1-scores for 5 tasks. The experimental results demonstrate the effectiveness of our system.

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Using Language Models to Improve Rule-based Linguistic Annotation of Modern Historical Japanese Corpora

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