ymoslem/acl-abstracts huggingface dataset:
Dialogue Corpus Construction Considering Modality and Social Relationships in Building Common Ground (2022)

Building common ground with users is essential for dialogue agent systems and robots to interact naturally with people. While a few previous studies have investigated the process of building common ground in human-human dialogue, most of them have been conducted on the basis of text chat. In this study, we constructed a dialogue corpus to investigate the process of building common ground with a particular focus on the modality of dialogue and the social relationship between the participants in the process of building common ground, which are important but have not been investigated in the previous work. The results of our analysis suggest that adding the modality or developing the relationship between workers speeds up the building of common ground. Specifically, regarding the modality, the presence of video rather than only audio may unconsciously facilitate work, and as for the relationship, it is easier to convey information about emotions and turn-taking among friends than in first meetings. These findings and the corpus should prove useful for developing a system to support remote communication.
EmoWOZ: A Large-Scale Corpus and Labelling Scheme for Emotion Recognition in Task-Oriented Dialogue Systems (2022)

The ability to recognise emotions lends a conversational artificial intelligence a human touch. While emotions in chit-chat dialogues have received substantial attention, emotions in task-oriented dialogues remain largely unaddressed. This is despite emotions and dialogue success having equally important roles in a natural system. Existing emotion-annotated task-oriented corpora are limited in size, label richness, and public availability, creating a bottleneck for downstream tasks. To lay a foundation for studies on emotions in task-oriented dialogues, we introduce EmoWOZ, a large-scale manually emotion-annotated corpus of task-oriented dialogues. EmoWOZ is based on MultiWOZ, a multi-domain task-oriented dialogue dataset. It contains more than 11K dialogues with more than 83K emotion annotations of user utterances. In addition to Wizard-of-Oz dialogues from MultiWOZ, we collect human-machine dialogues within the same set of domains to sufficiently cover the space of various emotions that can happen during the lifetime of a data-driven dialogue system. To the best of our knowledge, this is the first large-scale open-source corpus of its kind. We propose a novel emotion labelling scheme, which is tailored to task-oriented dialogues. We report a set of experimental results to show the usability of this corpus for emotion recognition and state tracking in task-oriented dialogues.
Data Augmentation with Paraphrase Generation and Entity Extraction for Multimodal Dialogue System (2022)

Contextually aware intelligent agents are often required to understand the users and their surroundings in real-time. Our goal is to build Artificial Intelligence (AI) systems that can assist children in their learning process. Within such complex frameworks, Spoken Dialogue Systems (SDS) are crucial building blocks to handle efficient task-oriented communication with children in game-based learning settings. We are working towards a multimodal dialogue system for younger kids learning basic math concepts. Our focus is on improving the Natural Language Understanding (NLU) module of the task-oriented SDS pipeline with limited datasets. This work explores the potential benefits of data augmentation with paraphrase generation for the NLU models trained on small task-specific datasets. We also investigate the effects of extracting entities for conceivably further data expansion. We have shown that paraphrasing with model-in-the-loop (MITL) strategies using small seed data is a promising approach yielding improved performance results for the Intent Recognition task.
Towards Modelling Self-imposed Filter Bubbles in Argumentative Dialogue Systems (2022)

To build a well-founded opinion it is natural for humans to gather and exchange new arguments. Especially when being confronted with an overwhelming amount of information, people tend to focus on only the part of the available information that fits into their current beliefs or convenient opinions. To overcome this {\textquotedblleft}self-imposed filter bubble{\textquotedblright} (SFB) in the information seeking process, it is crucial to identify influential indicators for the former. Within this paper we propose and investigate indicators for the the user`s SFB, mainly their Reflective User Engagement (RUE), their Personal Relevance (PR) ranking of content-related subtopics as well as their False (FK) and True Knowledge (TK) on the topic. Therefore, we analysed the answers of 202 participants of an online conducted user study, who interacted with our argumentative dialogue system BEA ({\textquotedblleft}Building Engaging Argumentation{\textquotedblright}). Moreover, also the influence of different input/output modalities (speech/speech and drop-down menu/text) on the interaction with regard to the suggested indicators was investigated.
Telling a Lie: Analyzing the Language of Information and Misinformation during Global Health Events (2022)

The COVID-19 pandemic and other global health events are unfortunately excellent environments for the creation and spread of misinformation, and the language associated with health misinformation may be typified by unique patterns and linguistic markers. Allowing health misinformation to spread unchecked can have devastating ripple effects; however, detecting and stopping its spread requires careful analysis of these linguistic characteristics at scale. We analyze prior investigations focusing on health misinformation, associated datasets, and detection of misinformation during health crises. We also introduce a novel dataset designed for analyzing such phenomena, comprised of 2.8 million news articles and social media posts spanning the early 1900s to the present. Our annotation guidelines result in strong agreement between independent annotators. We describe our methods for collecting this data and follow this with a thorough analysis of the themes and linguistic features that appear in information versus misinformation. Finally, we demonstrate a proof-of-concept misinformation detection task to establish dataset validity, achieving a strong performance benchmark (accuracy = 75{\%}; F1 = 0.7).
Misogyny and Aggressiveness Tend to Come Together and Together We Address Them (2022)

We target the complementary binary tasks of identifying whether a tweet is misogynous and, if that is the case, whether it is also aggressive. We compare two ways to address these problems: one multi-class model that discriminates between all the classes at once: not misogynous, non aggressive-misogynous and aggressive-misogynous; as well as a cascaded approach where the binary classification is carried out separately (misogynous vs non-misogynous and aggressive vs non-aggressive) and then joined together. For the latter, two training and three testing scenarios are considered. Our models are built on top of AlBERTo and are evaluated on the framework of Evalita`s 2020 shared task on automatic misogyny and aggressiveness identification in Italian tweets. Our cascaded models {---}including the strong na{\"i}ve baseline{---} outperform significantly the top submissions to Evalita, reaching state-of-the-art performance without relying on any external information.
The ComMA Dataset V0.2: Annotating Aggression and Bias in Multilingual Social Media Discourse (2022)

In this paper, we discuss the development of a multilingual dataset annotated with a hierarchical, fine-grained tagset marking different types of aggression and the {\textquotedblleft}context{\textquotedblright} in which they occur. The context, here, is defined by the conversational thread in which a specific comment occurs and also the {\textquotedblleft}type{\textquotedblright} of discursive role that the comment is performing with respect to the previous comment. The initial dataset, being discussed here consists of a total 59,152 annotated comments in four languages - Meitei, Bangla, Hindi, and Indian English - collected from various social media platforms such as YouTube, Facebook, Twitter and Telegram. As is usual on social media websites, a large number of these comments are multilingual, mostly code-mixed with English. The paper gives a detailed description of the tagset being used for annotation and also the process of developing a multi-label, fine-grained tagset that has been used for marking comments with aggression and bias of various kinds including sexism (called gender bias in the tagset), religious intolerance (called communal bias in the tagset), class/caste bias and ethnic/racial bias. We also define and discuss the tags that have been used for marking the different discursive role being performed through the comments, such as attack, defend, etc. Finally, we present a basic statistical analysis of the dataset. The dataset is being incrementally made publicly available on the project website.
Tweet Emotion Dynamics: Emotion Word Usage in Tweets from US and Canada (2022)

Over the last decade, Twitter has emerged as one of the most influential forums for social, political, and health discourse. In this paper, we introduce a massive dataset of more than 45 million geo-located tweets posted between 2015 and 2021 from US and Canada (TUSC), especially curated for natural language analysis. We also introduce Tweet Emotion Dynamics (TED) {---} metrics to capture patterns of emotions associated with tweets over time. We use TED and TUSC to explore the use of emotion-associated words across US and Canada; across 2019 (pre-pandemic), 2020 (the year the pandemic hit), and 2021 (the second year of the pandemic); and across individual tweeters. We show that Canadian tweets tend to have higher valence, lower arousal, and higher dominance than the US tweets. Further, we show that the COVID-19 pandemic had a marked impact on the emotional signature of tweets posted in 2020, when compared to the adjoining years. Finally, we determine metrics of TED for 170,000 tweeters to benchmark characteristics of TED metrics at an aggregate level. TUSC and the metrics for TED will enable a wide variety of research on studying how we use language to express ourselves, persuade, communicate, and influence, with particularly promising applications in public health, affective science, social science, and psychology.
A Turkish Hate Speech Dataset and Detection System (2022)

Social media posts containing hate speech are reproduced and redistributed at an accelerated pace, reaching greater audiences at a higher speed. We present a machine learning system for automatic detection of hate speech in Turkish, along with a hate speech dataset consisting of tweets collected in two separate domains. We first adopted a definition for hate speech that is in line with our goals and amenable to easy annotation; then designed the annotation schema for annotating the collected tweets. The Istanbul Convention dataset consists of tweets posted following the withdrawal of Turkey from the Istanbul Convention. The Refugees dataset was created by collecting tweets about immigrants by filtering based on commonly used keywords related to immigrants. Finally, we have developed a hate speech detection system using the transformer architecture (BERTurk), to be used as a baseline for the collected dataset. The binary classification accuracy is 77{\%} when the system is evaluated using 5-fold cross-validation on the Istanbul Convention dataset and 71{\%} for the Refugee dataset. We also tested a regression model with 0.66 and 0.83 RMSE on a scale of [0-4], for the Istanbul Convention and Refugees datasets.
Life is not Always Depressing: Exploring the Happy Moments of People Diagnosed with Depression (2022)

In this work, we explore the relationship between depression and manifestations of happiness in social media. While the majority of works surrounding depression focus on symptoms, psychological research shows that there is a strong link between seeking happiness and being diagnosed with depression. We make use of Positive-Unlabeled learning paradigm to automatically extract happy moments from social media posts of both controls and users diagnosed with depression, and qualitatively analyze them with linguistic tools such as LIWC and keyness information. We show that the life of depressed individuals is not always bleak, with positive events related to friends and family being more noteworthy to their lives compared to the more mundane happy events reported by control users.
Evaluating Tokenizers Impact on OOVs Representation with Transformers Models (2022)

Transformer models have achieved significant improvements in multiple downstream tasks in recent years. One of the main contributions of Transformers is their ability to create new representations for out-of-vocabulary (OOV) words. In this paper, we have evaluated three categories of OOVs: (A) new domain-specific terms (e.g., {\textquotedblleft}eucaryote'{\textquotedblright} in microbiology), (B) misspelled words containing typos, and (C) cross-domain homographs (e.g., {\textquotedblleft}arm{\textquotedblright} has different meanings in a clinical trial and anatomy). We use three French domain-specific datasets on the legal, medical, and energetical domains to robustly analyze these categories. Our experiments have led to exciting findings that showed: (1) It is easier to improve the representation of new words (A and B) than it is for words that already exist in the vocabulary of the Transformer models (C), (2) To ameliorate the representation of OOVs, the most effective method relies on adding external morpho-syntactic context rather than improving the semantic understanding of the words directly (fine-tuning) and (3) We cannot foresee the impact of minor misspellings in words because similar misspellings have different impacts on their representation. We believe that tackling the challenges of processing OOVs regarding their specificities will significantly help the domain adaptation aspect of BERT.
Assessing the Quality of an Italian Crowdsourced Idiom Corpus:the Dodiom Experiment (2022)

This paper describes how idiom-related language resources, collected through a crowdsourcing experiment carried out by means of Dodiom, a Game-with-a-purpose, have been analysed by language experts. The paper focuses on the criteria adopted for the data annotation and evaluation process. The main scope of this project is, indeed, the evaluation of the quality of the linguistic data obtained through a crowdsourcing project, namely to assess if the data provided and evaluated by the players who joined the game are actually considered of good quality by the language experts. Finally, results of the annotation and evaluation processes as well as future work are presented.
Medical Crossing: a Cross-lingual Evaluation of Clinical Entity Linking (2022)

Medical data annotation requires highly qualified expertise. Despite the efforts devoted to medical entity linking in different languages, available data is very sparse in terms of both data volume and languages. In this work, we establish benchmarks for cross-lingual medical entity linking using clinical reports, clinical guidelines, and medical research papers. We present a test set filtering procedure designed to analyze the {\textquotedblleft}hard cases{\textquotedblright} of entity linking approaching zero-shot cross-lingual transfer learning, evaluate state-of-the-art models, and draw several interesting conclusions based on our evaluation results.
MTLens: Machine Translation Output Debugging (2022)

The performance of Machine Translation (MT) systems varies significantly with inputs of diverging features such as topics, genres, and surface properties. Though there are many MT evaluation metrics that generally correlate with human judgments, they are not directly useful in identifying specific shortcomings of MT systems. In this demo, we present a benchmarking interface that enables improved evaluation of specific MT systems in isolation or multiple MT systems collectively by quantitatively evaluating their performance on many tasks across multiple domains and evaluation metrics. Further, it facilitates effective debugging and error analysis of MT output via the use of dynamic filters that help users hone in on problem sentences with specific properties, such as genre, topic, sentence length, etc. The interface can be extended to include additional filters such as lexical, morphological, and syntactic features. Aside from helping debug MT output, it can also help in identifying problems in reference translations and evaluation metrics.
IceBATS: An Icelandic Adaptation of the Bigger Analogy Test Set (2022)

Word embedding models have become commonplace in a wide range of NLP applications. In order to train and use the best possible models, accurate evaluation is needed. For extrinsic evaluation of word embedding models, analogy evaluation sets have been shown to be a good quality estimator. We introduce an Icelandic adaptation of a large analogy dataset, BATS, evaluate it on three different word embedding models and show that our evaluation set is apt at measuring the capabilities of such models.
Transfer Learning Methods for Domain Adaptation in Technical Logbook Datasets (2022)

Event identification in technical logbooks poses challenges given the limited logbook data available in specific technical domains, the large set of possible classes, and logbook entries typically being in short form and non-standard technical language. Technical logbook data typically has both a domain, the field it comes from (e.g., automotive), and an application, what it is used for (e.g., maintenance). In order to better handle the problem of data scarcity, using a variety of technical logbook datasets, this paper investigates the benefits of using transfer learning from sources within the same domain (but different applications), from within the same application (but different domains) and from all available data. Results show that performing transfer learning within a domain provides statistically significant improvements, and in all cases but one the best performance. Interestingly, transfer learning from within the application or across the global dataset degrades results in all cases but one, which benefited from adding as much data as possible. A further analysis of the dataset similarities shows that the datasets with higher similarity scores performed better in transfer learning tasks, suggesting that this can be utilized to determine the effectiveness of adding a dataset in a transfer learning task for technical logbooks.
Downstream Task Performance of BERT Models Pre-Trained Using Automatically De-Identified Clinical Data (2022)

Automatic de-identification is a cost-effective and straightforward way of removing large amounts of personally identifiable information from large and sensitive corpora. However, these systems also introduce errors into datasets due to their imperfect precision. These corruptions of the data may negatively impact the utility of the de-identified dataset. This paper de-identifies a very large clinical corpus in Swedish either by removing entire sentences containing sensitive data or by replacing sensitive words with realistic surrogates. These two datasets are used to perform domain adaptation of a general Swedish BERT model. The impact of the de-identification techniques is assessed by training and evaluating the models using six clinical downstream tasks. The results are then compared to a similar BERT model domain-adapted using an unaltered version of the clinical corpus. The results show that using an automatically de-identified corpus for domain adaptation does not negatively impact downstream performance. We argue that automatic de-identification is an efficient way of reducing the privacy risks of domain-adapted models and that the models created in this paper should be safe to distribute to other academic researchers.
Dilated Convolutional Neural Networks for Lightweight Diacritics Restoration (2022)

Diacritics restoration has become a ubiquitous task in the Latin-alphabet-based English-dominated Internet language environment. In this paper, we describe a small footprint 1D dilated convolution-based approach which operates on a character-level. We find that neural networks based on 1D dilated convolutions are competitive alternatives to solutions based on recurrent neural networks or linguistic modeling for the task of diacritics restoration. Our approach surpasses the performance of similarly sized models and is also competitive with larger models. A special feature of our solution is that it even runs locally in a web browser. We also provide a working example of this browser-based implementation. Our model is evaluated on different corpora, with emphasis on the Hungarian language. We performed comparative measurements about the generalization power of the model in relation to three Hungarian corpora. We also analyzed the errors to understand the limitation of corpus-based self-supervised training.
Generating Artificial Texts as Substitution or Complement of Training Data (2022)

The quality of artificially generated texts has considerably improved with the advent of transformers. The question of using these models to generate learning data for supervised learning tasks naturally arises, especially when the original language resource cannot be distributed, or when it is small. In this article, this question is explored under 3 aspects: (i) are artificial data an efficient complement? (ii) can they replace the original data when those are not available or cannot be distributed for confidentiality reasons? (iii) can they improve the explainability of classifiers? Different experiments are carried out on classification tasks - namely sentiment analysis on product reviews and Fake News detection - using artificially generated data by fine-tuned GPT-2 models. The results show that such artificial data can be used in a certain extend but require pre-processing to significantly improve performance. We also show that bag-of-words approaches benefit the most from such data augmentation.
From Pattern to Interpretation. Using Colibri Core to Detect Translation Patterns in the Peshitta. (2022)

This article presents the first results of the CLARIAH-funded project {\textquoteleft}Patterns in Translation: Using Colibri Core for the Syriac Bible' (PaTraCoSy). This project seeks to use Colibri Core to detect translation patterns in the Peshitta, the Syriac translation of the Hebrew Bible. We first describe how we constructed word and phrase alignment between these two texts. This step is necessary to succesfully implement the functionalities of Colibri Core. After this, we further describe our first investigations with the software. We describe how we use the built-in pattern modeller to detect n-gram and skipgram patterns in both Hebrew and Syriac texts. Colibri Core does not allow the creation of a bilingual model, which is why we compare the separate models. After a presentation of a few general insights on the overall translation behaviour of the Peshitta, we delve deeper into the concrete patterns we can detect by the n-gram/skipgram analysis. We provide multiple examples from the book of Genesis, a book which has been treated broadly in scholarly research into the Syriac translation, but which also appears to have interesting features based on our Colibri Core research.