pwc-archive/papers-with-abstracts huggingface dataset:
Contrastive Multimodal Learning for Emergence of Graphical Sensory-Motor Communication
Authors: Tristan Karch, Yoann Lemesle, Romain Laroche, Clément Moulin-Frier, Pierre-Yves Oudeyer
Date: 2022-10-03 00:00:00

In this paper, we investigate whether artificial agents can develop a shared language in an ecological setting where communication relies on a sensory-motor channel. To this end, we introduce the Graphical Referential Game (GREG) where a speaker must produce a graphical utterance to name a visual referent object while a listener has to select the corresponding object among distractor referents, given the delivered message. The utterances are drawing images produced using dynamical motor primitives combined with a sketching library. To tackle GREG we present CURVES: a multimodal contrastive deep learning mechanism that represents the energy (alignment) between named referents and utterances generated through gradient ascent on the learned energy landscape. We demonstrate that CURVES not only succeeds at solving the GREG but also enables agents to self-organize a language that generalizes to feature compositions never seen during training. In addition to evaluating the communication performance of our approach, we also explore the structure of the emerging language. Specifically, we show that the resulting language forms a coherent lexicon shared between agents and that basic compositional rules on the graphical productions could not explain the compositional generalization.
Towards an Efficient ML System: Unveiling a Trade-off between Task Accuracy and Engineering Efficiency in a Large-scale Car Sharing Platform
Authors: Kyung Ho Park, Hyunhee Chung, Soonwoo Kwon
Date: 2022-10-10 00:00:00

Upon the significant performance of the supervised deep neural networks, conventional procedures of developing ML system are \textit{task-centric}, which aims to maximize the task accuracy. However, we scrutinized this \textit{task-centric} ML system lacks in engineering efficiency when the ML practitioners solve multiple tasks in their domain. To resolve this problem, we propose an \textit{efficiency-centric} ML system that concatenates numerous datasets, classifiers, out-of-distribution detectors, and prediction tables existing in the practitioners' domain into a single ML pipeline. Under various image recognition tasks in the real world car-sharing platform, our study illustrates how we established the proposed system and lessons learned from this journey as follows. First, the proposed ML system accomplishes supreme engineering efficiency while achieving a competitive task accuracy. Moreover, compared to the \textit{task-centric} paradigm, we discovered that the \textit{efficiency-centric} ML system yields satisfactory prediction results on multi-labelable samples, which frequently exist in the real world. We analyze these benefits derived from the representation power, which learned broader label spaces from the concatenated dataset. Last but not least, our study elaborated how we deployed this \textit{efficiency-centric} ML system is deployed in the real world live cloud environment. Based on the proposed analogies, we highly expect that ML practitioners can utilize our study to elevate engineering efficiency in their domain.
Denoising Masked AutoEncoders Help Robust Classification
Authors: Quanlin Wu, Hang Ye, Yuntian Gu, Huishuai Zhang, LiWei Wang, Di He
Date: 2022-10-10 00:00:00
Tasks: Classification, Decoder, Denoising, Robust classification

In this paper, we propose a new self-supervised method, which is called Denoising Masked AutoEncoders (DMAE), for learning certified robust classifiers of images. In DMAE, we corrupt each image by adding Gaussian noises to each pixel value and randomly masking several patches. A Transformer-based encoder-decoder model is then trained to reconstruct the original image from the corrupted one. In this learning paradigm, the encoder will learn to capture relevant semantics for the downstream tasks, which is also robust to Gaussian additive noises. We show that the pre-trained encoder can naturally be used as the base classifier in Gaussian smoothed models, where we can analytically compute the certified radius for any data point. Although the proposed method is simple, it yields significant performance improvement in downstream classification tasks. We show that the DMAE ViT-Base model, which just uses 1/10 parameters of the model developed in recent work arXiv:2206.10550, achieves competitive or better certified accuracy in various settings. The DMAE ViT-Large model significantly surpasses all previous results, establishing a new state-of-the-art on ImageNet dataset. We further demonstrate that the pre-trained model has good transferability to the CIFAR-10 dataset, suggesting its wide adaptability. Models and code are available at https://github.com/quanlin-wu/dmae.
Advancing the cybersecurity of the healthcare system with self-optimising and self-adaptative artificial intelligence (part 2)
Authors: Petar Radanliev, David De Roure
Date: 2022-08-30 00:00:00

This article advances the knowledge on teaching and training new artificial intelligence algorithms, for securing, preparing, and adapting the healthcare system to cope with future pandemics. The core objective is to develop a concept healthcare system supported by autonomous artificial intelligence that can use edge health devices with real-time data. The article constructs two case scenarios for applying cybersecurity with autonomous artificial intelligence for (1) self-optimising predictive cyber risk analytics of failures in healthcare systems during a Disease X event (i.e., undefined future pandemic), and (2) self-adaptive forecasting of medical production and supply chain bottlenecks during future pandemics. To construct the two testing scenarios, the article uses the case of Covid-19 to synthesise data for the algorithms i.e., for optimising and securing digital healthcare systems in anticipation of disease X. The testing scenarios are built to tackle the logistical challenges and disruption of complex production and supply chains for vaccine distribution with optimisation algorithms.
The Validity of Lexicon-based Sentiment Analysis in Interdisciplinary Research
Authors: Emily Öhman
Date: 2021-12-01 00:00:00
Proceeding: NLP4DH (ICON) 2021 12
Tasks: Emotion Recognition, Sentiment Analysis

Lexicon-based sentiment and emotion analysis methods are widely used particularly in applied Natural Language Processing (NLP) projects in fields such as computational social science and digital humanities. These lexicon-based methods have often been criticized for their lack of validation and accuracy – sometimes fairly. However, in this paper, we argue that lexicon-based methods work well particularly when moving up in granularity and show how useful lexicon-based methods can be for projects where neither qualitative analysis nor a machine learning-based approach is possible. Indeed, we argue that the measure of a lexicon’s accuracy should be grounded in its usefulness.
How Does the Hate Speech Corpus Concern Sociolinguistic Discussions? A Case Study on Korean Online News Comments
Authors: Won Ik Cho, Jihyung Moon
Date: 2021-12-01 00:00:00
Proceeding: NLP4DH (ICON) 2021 12

Social consensus has been established on the severity of online hate speech since it not only causes mental harm to the target, but also gives displeasure to the people who read it. For Korean, the definition and scope of hate speech have been discussed widely in researches, but such considerations were hardly extended to the construction of hate speech corpus. Therefore, we create a Korean online hate speech dataset with concrete annotation guideline to see how real world toxic expressions concern sociolinguistic discussions. This inductive observation reveals that hate speech in online news comments is mainly composed of social bias and toxicity. Furthermore, we check how the final corpus corresponds with the definition and scope of hate speech, and confirm that the overall procedure and outcome is in concurrence with the sociolinguistic discussions.
MacBERTh: Development and Evaluation of a Historically Pre-trained Language Model for English (1450-1950)
Authors: Enrique Manjavacas Arevalo, Lauren Fonteyn
Date: 2021-12-01 00:00:00
Proceeding: NLP4DH (ICON) 2021 12
Tasks: Language Modeling, Language Modelling, Large Language Model

The new pre-train-then-fine-tune paradigm in Natural made important performance gains accessible to a wider audience. Once pre-trained, deploying a large language model presents comparatively small infrastructure requirements, and offers robust performance in many NLP tasks. The Digital Humanities community has been an early adapter of this paradigm. Yet, a large part of this community is concerned with the application of NLP algorithms to historical texts, for which large models pre-trained on contemporary text may not provide optimal results. In the present paper, we present “MacBERTh”—a transformer-based language model pre-trained on historical English—and exhaustively assess its benefits on a large set of relevant downstream tasks. Our experiments highlight that, despite some differences across target time periods, pre-training on historical language from scratch outperforms models pre-trained on present-day language and later adapted to historical language.
Named Entity Recognition for French medieval charters
Authors: Sergio Torres Aguilar, Dominique Stutzmann
Date: 2021-12-01 00:00:00
Proceeding: NLP4DH (ICON) 2021 12
Tasks: named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER)

This paper presents the process of annotating and modelling a corpus to automatically detect named entities in medieval charters in French. It introduces a new annotated corpus and a new system which outperforms state-of-the art libraries. Charters are legal documents and among the most important historical sources for medieval studies as they reflect economic and social dynamics as well as the evolution of literacy and writing practices. Automatic detection of named entities greatly improves the access to these unstructured texts and facilitates historical research. The experiments described here are based on a corpus encompassing about 500k words (1200 charters) coming from three charter collections of the 13th and 14th centuries. We annotated the corpus and then trained two state-of-the art NLP libraries for Named Entity Recognition (Spacy and Flair) and a custom neural model (Bi-LSTM-CRF). The evaluation shows that all three models achieve a high performance rate on the test set and a high generalization capacity against two external corpora unseen during training. This paper describes the corpus and the annotation model, and discusses the issues related to the linguistic processing of medieval French and formulaic discourse, so as to interpret the results within a larger historical perspective.
Processing M.A. Castrén’s Materials: Multilingual Historical Typed and Handwritten Manuscripts
Authors: Niko Partanen, Jack Rueter, Khalid Alnajjar, Mika Hämäläinen
Date: 2021-12-01 00:00:00
Proceeding: NLP4DH (ICON) 2021 12

The study forms a technical report of various tasks that have been performed on the materials collected and published by Finnish ethnographer and linguist, Matthias Alexander Castrén (1813–1852). The Finno-Ugrian Society is publishing Castrén’s manuscripts as new critical and digital editions, and at the same time different research groups have also paid attention to these materials. We discuss the workflows and technical infrastructure used, and consider how datasets that benefit different computational tasks could be created to further improve the usability of these materials, and also to aid the further processing of similar archived collections. We specifically focus on the parts of the collections that are processed in a way that improves their usability in more technical applications, complementing the earlier work on the cultural and linguistic aspects of these materials. Most of these datasets are openly available in Zenodo. The study points to specific areas where further research is needed, and provides benchmarks for text recognition tasks.
Lotte and Annette: A Framework for Finding and Exploring Key Passages in Literary Works
Authors: Frederik Arnold, Robert Jäschke
Date: 2021-12-01 00:00:00
Proceeding: NLP4DH (ICON) 2021 12

We present an approach that leverages expert knowledge contained in scholarly works to automatically identify key passages in literary works. Specifically, we extend a text reuse detection method for finding quotations, such that our system Lotte can deal with common properties of quotations, for example, ellipses or inaccurate quotations. An evaluation shows that Lotte outperforms four existing approaches. To generate key passages, we combine overlapping quotations from multiple scholarly texts. An interactive website, called Annette, for visualizing and exploring key passages makes the results accessible and explorable.
Using Referring Expression Generation to Model Literary Style
Authors: Nick Montfort, Ardalan SadeghiKivi, Joanne Yuan, Alan Y. Zhu
Date: 2021-12-01 00:00:00
Proceeding: NLP4DH (ICON) 2021 12
Tasks: model, Referring Expression, Referring expression generation

Novels and short stories are not just remarkable because of what events they represent. The narrative style they employ is significant. To understand the specific contributions of different aspects of this style, it is possible to create limited symbolic models of narrating that hold almost all of the narrative discourse constant while varying a single aspect. In this paper we use a new implementation of a system for narrative discourse generation, Curveship, to change how existents at the story level are named. This by itself allows for the telling of the same underlying story in ways that evoke, for instance, a fabular or parable-like mode, the style of narrator Patrick Bateman in Brett Easton Ellis’s American Psycho, and the unusual dialect of Anthony Burgess’s A Clockwork Orange.
The concept of nation in nineteenth-century Greek fiction through computational literary analysis
Authors: Fotini Koidaki, Despina Christou, Katerina Tiktopoulou, Grigorios Tsoumakas
Date: 2021-12-01 00:00:00
Proceeding: NLP4DH (ICON) 2021 12

How the construction of national consciousness may be captured in the literary production of a whole century? What can the macro-analysis of the 19th-century prose fiction reveal about the formation of the concept of the nation-state of Greece? How could the concept of nationality be detected in literary writing and then interpreted? These are the questions addressed by the research that is published in this paper and which focuses on exploring how the concept of the nation is figured and shaped in 19th-century Greek prose fiction. This paper proposes a methodological approach that combines well-known text mining techniques with computational close reading methods in order to retrieve the nation-related passages and to analyze them linguistically and semantically. The main objective of the paper at hand is to map the frequency and the phraseology of the nation-related references, as well as to explore the phrase patterns in relation to the topic modeling results.
Logical Layout Analysis Applied to Historical Newspapers
Authors: Nicolas Gutehrlé, Iana Atanassova
Date: 2021-12-01 00:00:00
Proceeding: NLP4DH (ICON) 2021 12

In recent years, libraries and archives led important digitisation campaigns that opened the access to vast collections of historical documents. While such documents are often available as XML ALTO documents, they lack information about their logical structure. In this paper, we address the problem of logical layout analysis applied to historical documents. We propose a method which is based on the study of a dataset in order to identify rules that assign logical labels to both block and lines of text from XML ALTO documents. Our dataset contains newspapers in French, published in the first half of the 20th century. The evaluation shows that our methodology performs well for the identification of first lines of paragraphs and text lines, with F1 above 0.9. The identification of titles obtains an F1 of 0.64. This method can be applied to preprocess XML ALTO documents in preparation for downstream tasks, and also to annotate large-scale datasets to train machine learning and deep learning algorithms.
“Don’t worry, it’s just noise’”: quantifying the impact of files treated as single textual units when they are really collections
Authors: Thibault Clérice
Date: 2021-12-01 00:00:00
Proceeding: NLP4DH (ICON) 2021 12

Literature works may present many autonomous or semi-autonomous units, such as poems for the first or chapter for the second. We make the hypothesis that such cuts in the text’s flow, if not taken care of in the way we process text, have an impact on the application of the distributional hypothesis. We test this hypothesis with a large 20M tokens corpus of Latin works, by using text files as a single unit or multiple “autonomous” units for the analysis of selected words. For groups of rare words and words specific to heavily segmented works, the results show that their semantic space is mostly different between both versions of the corpus. For the 1000 most frequent words of the corpus, variations are important as soon as the window for defining neighborhood is larger or equal to 10 words.
NLP in the DH pipeline: Transfer-learning to a Chronolect
Authors: Aynat Rubinstein, Avi Shmidman
Date: 2021-12-01 00:00:00
Proceeding: NLP4DH (ICON) 2021 12
Tasks: Transfer Learning

A big unknown in Digital Humanities (DH) projects that seek to analyze previously untouched corpora is the question of how to adapt existing Natural Language Processing (NLP) resources to the specific nature of the target corpus. In this paper, we study the case of Emergent Modern Hebrew (EMH), an under-resourced chronolect of the Hebrew language. The resource we seek to adapt, a diacritizer, exists for both earlier and later chronolects of the language. Given a small annotated corpus of our target chronolect, we demonstrate that applying transfer-learning from either of the chronolects is preferable to training a new model from scratch. Furthermore, we consider just how much annotated data is necessary. For our task, we find that even a minimal corpus of 50K tokens provides a noticeable gain in accuracy. At the same time, we also evaluate accuracy at three additional increments, in order to quantify the gains that can be expected by investing in a larger annotated corpus.
Using Computational Grounded Theory to Understand Tutors’ Experiences in the Gig Economy
Authors: Lama Alqazlan, Rob Procter, Michael Castelle
Date: 2021-12-01 00:00:00
Proceeding: NLP4DH (ICON) 2021 12

The introduction of online marketplace platforms has led to the advent of new forms of flexible, on-demand (or ‘gig’) work. Yet, most prior research concerning the experience of gig workers examines delivery or crowdsourcing platforms, while the experience of the large numbers of workers who undertake educational labour in the form of tutoring gigs remains understudied. To address this, we use a computational grounded theory approach to analyse tutors’ discussions on Reddit. This approach consists of three phases including data exploration, modelling and human-centred interpretation. We use both validation and human evaluation to increase the trustworthiness and reliability of the computational methods. This paper is a work in progress and reports on the first of the three phases of this approach.
Can Domain Pre-training Help Interdisciplinary Researchers from Data Annotation Poverty? A Case Study of Legal Argument Mining with BERT-based Transformers
Authors: Gechuan Zhang, David Lillis, Paul Nulty
Date: 2021-12-01 00:00:00
Proceeding: NLP4DH (ICON) 2021 12
Tasks: Argument Mining

Interdisciplinary Natural Language Processing (NLP) research traditionally suffers from the requirement for costly data annotation. However, transformer frameworks with pre-training have shown their ability on many downstream tasks including digital humanities tasks with limited small datasets. Considering the fact that many digital humanities fields (e.g. law) feature an abundance of non-annotated textual resources, and the recent achievements led by transformer models, we pay special attention to whether domain pre-training will enhance transformer’s performance on interdisciplinary tasks and how. In this work, we use legal argument mining as our case study. This aims to automatically identify text segments with particular linguistic structures (i.e., arguments) from legal documents and to predict the reasoning relations between marked arguments. Our work includes a broad survey of a wide range of BERT variants with different pre-training strategies. Our case study focuses on: the comparison of general pre-training and domain pre-training; the generalisability of different domain pre-trained transformers; and the potential of merging general pre-training with domain pre-training. We also achieve better results than the current transformer baseline in legal argument mining.
Japanese Beauty Marketing on Social Media: Critical Discourse Analysis Meets NLP
Authors: Emily Öhman, Amy Gracy Metcalfe
Date: 2021-12-01 00:00:00
Proceeding: NLP4DH (ICON) 2021 12
Tasks: Date Understanding, Marketing

This project is a pilot study intending to combine traditional corpus linguistics, Natural Language Processing, critical discourse analysis, and digital humanities to gain an up-to-date understanding of how beauty is being marketed on social media, specifically Instagram, to followers. We use topic modeling combined with critical discourse analysis and NLP tools for insights into the “Japanese Beauty Myth” and show an overview of the dataset that we make publicly available.
Text Zoning of Theater Reviews: How Different are Journalistic from Blogger Reviews?
Authors: Mylene Maignant, Thierry Poibeau, Gaëtan Brison
Date: 2021-12-01 00:00:00
Proceeding: NLP4DH (ICON) 2021 12
Tasks: Sentence

This paper aims at modeling the structure of theater reviews based on contemporary London performances by using text zoning. Text zoning consists in tagging sentences so as to reveal text structure. More than 40 000 theater reviews going from 2010 to 2020 were collected to analyze two different types of reception (journalistic vs digital). We present our annotation scheme and the classifiers used to perform the text zoning task, aiming at tagging reviews at the sentence level. We obtain the best results using the random forest algorithm, and show that this approach makes it possible to give a first insight of the similarities and differences between our two subcorpora.
Word Sense Induction with Attentive Context Clustering
Authors: Moshe Stekel, Amos Azaria, Shai Gordin
Date: 2021-12-01 00:00:00
Proceeding: NLP4DH (ICON) 2021 12
Tasks: Clustering, Word Sense Induction

In this paper, we present ACCWSI (Attentive Context Clustering WSI), a method for Word Sense Induction, suitable for languages with limited resources. Pretrained on a small corpus and given an ambiguous word (query word) and a set of excerpts that contain it, ACCWSI uses an attention mechanism for generating context-aware embeddings, distinguishing between the different senses assigned to the query word. These embeddings are then clustered to provide groups of main common uses of the query word. This method demonstrates practical applicability for shedding light on the meanings of ambiguous words in ancient languages, such as Classical Hebrew.