pwc-archive/papers-with-abstracts huggingface dataset:
Leveraging Semantic and Sentiment Knowledge for User-Generated Text Sentiment Classification
Authors: Jawad Khan, Niaz Ahmad, Aftab Alam, Youngmoon Lee
Date: 2022-10-01 00:00:00
Proceeding: COLING (WNUT) 2022 10
Tasks: Classification, Decision Making, feature selection, Sentiment Analysis, Sentiment Classification, text-classification, Text Classification, Word Embeddings

Sentiment analysis is essential to process and understand unstructured user-generated content for better data analytics and decision-making. State-of-the-art techniques suffer from a high dimensional feature space because of noisy and irrelevant features from the noisy user-generated text. Our goal is to mitigate such problems using DNN-based text classification and popular word embeddings (Glove, fastText, and BERT) in conjunction with statistical filter feature selection (mRMR and PCA) to select relevant sentiment features and pick out unessential/irrelevant ones. We propose an effective way of integrating the traditional feature construction methods with the DNN-based methods to improve the performance of sentiment classification. We evaluate our model on three real-world benchmark datasets demonstrating that our proposed method improves the classification performance of several existing methods.
An Emotional Journey: Detecting Emotion Trajectories in Dutch Customer Service Dialogues
Authors: Sofie Labat, Amir Hadifar, Thomas Demeester, Veronique Hoste
Date: 2022-10-01 00:00:00
Proceeding: COLING (WNUT) 2022 10

The ability to track fine-grained emotions in customer service dialogues has many real-world applications, but has not been studied extensively. This paper measures the potential of prediction models on that task, based on a real-world dataset of Dutch Twitter conversations in the domain of customer service. We find that modeling emotion trajectories has a small, but measurable benefit compared to predictions based on isolated turns. The models used in our study are shown to generalize well to different companies and economic sectors.
Supervised and Unsupervised Evaluation of Synthetic Code-Switching
Authors: Evgeny Orlov, Ekaterina Artemova
Date: 2022-10-01 00:00:00
Proceeding: COLING (WNUT) 2022 10
Tasks: Sentence

Code-switching (CS) is a phenomenon of mixing words and phrases from multiple languages within a single sentence or conversation. The ever-growing amount of CS communication among multilingual speakers in social media has highlighted the need to adapt existing NLP products for CS speakers and lead to a rising interest in solving CS NLP tasks. A large number of contemporary approaches use synthetic CS data for training. As previous work has shown the positive effect of pretraining on high-quality CS data, the task of evaluating synthetic CS becomes crucial. In this paper, we address the task of evaluating synthetic CS in two settings. In supervised setting, we apply Hinglish finetuned models to solve the quality rating prediction task of HinglishEval competition and establish a new SOTA. In unsupervised setting, we employ the method of acceptability measures with the same models. We find that in both settings, models finetuned on CS data consistently outperform their original counterparts.
Automatic Identification of 5C Vaccine Behaviour on Social Media
Authors: Ajay Hemanth Sampath Kumar, Aminath Shausan, Gianluca Demartini, Afshin Rahimi
Date: 2022-10-01 00:00:00
Proceeding: COLING (WNUT) 2022 10
Tasks: Sentiment Analysis

Monitoring vaccine behaviour through social media can guide health policy. We present a new dataset of 9471 tweets posted in Australia from 2020 to 2022, annotated with sentiment toward vaccines and also 5C, the five types of behaviour toward vaccines, a scheme commonly used in health psychology literature. We benchmark our dataset using BERT and Gradient Boosting Machine and show that jointly training both sentiment and 5C tasks (F1=48) outperforms individual training (F1=39) in this highly imbalanced data. Our sentiment analysis indicates close correlation between the sentiments and prominent events during the pandemic. We hope that our dataset and benchmark models will inform further work in online monitoring of vaccine behaviour. The dataset and benchmark methods are accessible online.
Automatic Extraction of Structured Mineral Drillhole Results from Unstructured Mining Company Reports
Authors: Adam Dimeski, Afshin Rahimi
Date: 2022-10-01 00:00:00
Proceeding: COLING (WNUT) 2022 10

Aggregate mining exploration results can help companies and governments to optimise and police mining permits and operations, a necessity for transition to a renewable energy future, however, these results are buried in unstructured text. We present a novel dataset from 23 Australian mining company reports, framing the extraction of structured drillhole information as a sequence labelling task. Our two benchmark models based on Bi-LSTM-CRF and BERT, show their effectiveness in this task with a F1 score of 77% and 87%, respectively. Our dataset and benchmarks are accessible online.
“Kanglish alli names!” Named Entity Recognition for Kannada-English Code-Mixed Social Media Data
Authors: Sumukh S, Manish Shrivastava
Date: 2022-10-01 00:00:00
Proceeding: COLING (WNUT) 2022 10
Tasks: named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER, POS, Semantic Role Labeling

Code-mixing (CM) is a frequently observed phenomenon on social media platforms in multilingual societies such as India. While the increase in code-mixed content on these platforms provides good amount of data for studying various aspects of code-mixing, the lack of automated text analysis tools makes such studies difficult. To overcome the same, tools such as language identifiers and parts of-speech (POS) taggers for analysing code-mixed data have been developed. One such tool is Named Entity Recognition (NER), an important Natural Language Processing (NLP) task, which is not only a subtask of Information Extraction, but is also needed for downstream NLP tasks such as semantic role labeling. While entity extraction from social media data is generally difficult due to its informal nature, code-mixed data further complicates the problem due to its informal, unstructured and incomplete information. In this work, we present the first ever corpus for Kannada-English code-mixed social media data with the corresponding named entity tags for NER. We provide strong baselines with machine learning classification models such as CRF, Bi-LSTM, and Bi-LSTM-CRF on our corpus with word, character, and lexical features.
Span Extraction Aided Improved Code-mixed Sentiment Classification
Authors: Ramaneswaran S, Sean Benhur, Sreyan Ghosh
Date: 2022-10-01 00:00:00
Proceeding: COLING (WNUT) 2022 10
Tasks: Classification, Multi-Task Learning, Sentiment Analysis, Sentiment Classification

Sentiment classification is a fundamental NLP task of detecting the sentiment polarity of a given text. In this paper we show how solving sentiment span extraction as an auxiliary task can help improve final sentiment classification performance in a low-resource code-mixed setup. To be precise, we don’t solve a simple multi-task learning objective, but rather design a unified transformer framework that exploits the bidirectional connection between the two tasks simultaneously. To facilitate research in this direction we release gold-standard human-annotated sentiment span extraction dataset for Tamil-english code-switched texts. Extensive experiments and strong baselines show that our proposed approach outperforms sentiment and span prediction by 1.27% and 2.78% respectively when compared to the best performing MTL baseline. We also establish the generalizability of our approach on the Twitter Sentiment Extraction dataset. We make our code and data publicly available on GitHub
AdBERT: An Effective Few Shot Learning Framework for Aligning Tweets to Superbowl Advertisements
Authors: Debarati Das, Roopana Chenchu, Maral Abdollahi, Jisu Huh, Jaideep Srivastava
Date: 2022-10-01 00:00:00
Proceeding: COLING (WNUT) 2022 10
Tasks: Few-Shot Learning, Marketing

The tremendous increase in social media usage for sharing Television (TV) experiences has provided a unique opportunity in the Public Health and Marketing sectors to understand viewer engagement and attitudes through viewer-generated content on social media. However, this opportunity also comes with associated technical challenges. Specifically, given a televised event and related tweets about this event, we need methods to effectively align these tweets and the corresponding event. In this paper, we consider the specific ecosystem of the Superbowl 2020 and map viewer tweets to advertisements they are referring to. Our proposed model, AdBERT, is an effective few-shot learning framework that is able to handle the technical challenges of establishing ad-relatedness, class imbalance as well as the scarcity of labeled data. As part of this study, we have curated and developed two datasets that can prove to be useful for Social TV research: 1) dataset of ad-related tweets and 2) dataset of ad descriptions of Superbowl advertisements. Explaining connections to SentenceBERT, we describe the advantages of AdBERT that allow us to make the most out of a challenging and interesting dataset which we will open-source along with the models developed in this paper.
Increasing Robustness for Cross-domain Dialogue Act Classification on Social Media Data
Authors: Marcus Vielsted, Nikolaj Wallenius, Rob van der Goot
Date: 2022-10-01 00:00:00
Proceeding: COLING (WNUT) 2022 10
Tasks: Dialogue Act Classification, Lexical Normalization

Automatically detecting the intent of an utterance is important for various downstream natural language processing tasks. This task is also called Dialogue Act Classification (DAC) and was primarily researched on spoken one-to-one conversations. The rise of social media has made this an interesting data source to explore within DAC, although it comes with some difficulties: non-standard form, variety of language types (across and within platforms), and quickly evolving norms. We therefore investigate the robustness of DAC on social media data in this paper. More concretely, we provide a benchmark that includes cross-domain data splits, as well as a variety of improvements on our transformer-based baseline. Our experiments show that lexical normalization is not beneficial in this setup, balancing the labels through resampling is beneficial in some cases, and incorporating context is crucial for this task and leads to the highest performance improvements 7 F1 percentage points in-domain and 20 cross-domain).
Disfluency Detection for Vietnamese
Authors: Mai Dao, Thinh Hung Truong, Dat Quoc Nguyen
Date: 2022-10-01 00:00:00
Proceeding: COLING (WNUT) 2022 10
Tasks: Vietnamese Word Segmentation, XLM-R

In this paper, we present the first empirical study for Vietnamese disfluency detection. To conduct this study, we first create a disfluency detection dataset for Vietnamese, with manual annotations over two disfluency types. We then empirically perform experiments using strong baseline models, and find that: automatic Vietnamese word segmentation improves the disfluency detection performances of the baselines, and the highest performance results are obtained by fine-tuning pre-trained language models in which the monolingual model PhoBERT for Vietnamese does better than the multilingual model XLM-R.
A multi-level approach for hierarchical Ticket Classification
Authors: Matteo Marcuzzo, Alessandro Zangari, Michele Schiavinato, Lorenzo Giudice, Andrea Gasparetto, Andrea Albarelli
Date: 2022-10-01 00:00:00
Proceeding: COLING (WNUT) 2022 10
Tasks: Classification, Word Embeddings

The automatic categorization of support tickets is a fundamental tool for modern businesses. Such requests are most commonly composed of concise textual descriptions that are noisy and filled with technical jargon. In this paper, we test the effectiveness of pre-trained LMs for the classification of issues related to software bugs. First, we test several strategies to produce single, ticket-wise representations starting from their BERT-generated word embeddings. Then, we showcase a simple yet effective way to build a multi-level classifier for the categorization of documents with two hierarchically dependent labels. We experiment on a public bugs dataset and compare our results with standard BERT-based and traditional SVM classifiers. Our findings suggest that both embedding strategies and hierarchical label dependencies considerably impact classification accuracy.
Towards better structured and less noisy Web data: Oscar with Register annotations
Authors: Veronika Laippala, Anna Salmela, Samuel Rönnqvist, Alham Fikri Aji, Li-Hsin Chang, Asma Dhifallah, Larissa Goulart, Henna Kortelainen, Marc Pàmies, Deise Prina Dutra, Valtteri Skantsi, Lintang Sutawika, Sampo Pyysalo
Date: 2022-10-01 00:00:00
Proceeding: COLING (WNUT) 2022 10

Web-crawled datasets are known to be noisy, as they feature a wide range of language use covering both user-generated and professionally edited content as well as noise originating from the crawling process. This article presents one solution to reduce this noise by using automatic register (genre) identification -whether the texts are, e.g., forum discussions, lyrical or how-to pages. We apply the multilingual register identification model by Rönnqvist et al. (2021) and label the widely used Oscar dataset. Additionally, we evaluate the model against eight new languages, showing that the performance is comparable to previous findings on a restricted set of languages. Finally, we present and apply a machine learning method for further cleaning text files originating from Web crawls from remains of boilerplate and other elements not belonging to the main text of the Web page. The register labeled and cleaned dataset covers 351 million documents in 14 languages and is available at https://huggingface.co/datasets/TurkuNLP/register_oscar.
True or False? Detecting False Information on Social Media Using Graph Neural Networks
Authors: Samyo Rode-Hasinger, Anna Kruspe, Xiao Xiang Zhu
Date: 2022-10-01 00:00:00
Proceeding: COLING (WNUT) 2022 10
Tasks: Graph Neural Network, Misinformation

In recent years, false information such as fake news, rumors and conspiracy theories on many relevant issues in society have proliferated. This phenomenon has been significantly amplified by the fast and inexorable spread of misinformation on social media and instant messaging platforms. With this work, we contribute to containing the negative impact on society caused by fake news. We propose a graph neural network approach for detecting false information on Twitter. We leverage the inherent structure of graph-based social media data aggregating information from short text messages (tweets), user profiles and social interactions. We use knowledge from pre-trained language models efficiently, and show that user-defined descriptions of profiles provide useful information for improved prediction performance. The empirical results indicate that our proposed framework significantly outperforms text- and user-based methods on misinformation datasets from two different domains, even in a difficult multilingual setting.
Analyzing the Real Vulnerability of Hate Speech Detection Systems against Targeted Intentional Noise
Authors: Piush Aggarwal, Torsten Zesch
Date: 2022-10-01 00:00:00
Proceeding: COLING (WNUT) 2022 10
Tasks: Hate Speech Detection

Hate speech detection systems have been shown to be vulnerable against obfuscation attacks, where a potential hater tries to circumvent detection by deliberately introducing noise in their posts. In previous work, noise is often introduced for all words (which is likely overestimating the impact) or single untargeted words (likely underestimating the vulnerability). We perform a user study asking people to select words they would obfuscate in a post. Using this realistic setting, we find that the real vulnerability of hate speech detection systems against deliberately introduced noise is almost as high as when using a whitebox attack and much more severe than when using a non-targeted dictionary. Our results are based on 4 different datasets, 12 different obfuscation strategies, and hate speech detection systems using different paradigms.
Multilevel Hypernode Graphs for Effective and Efficient Entity Linking
Authors: David Montero, Javier Martínez, Javier Yebes
Date: 2022-10-01 00:00:00
Proceeding: COLING (TextGraphs) 2022 10
Tasks: Entity Linking

Information extraction on documents still remains a challenge, especially when dealing with unstructured documents with complex and variable layouts. Graph Neural Networks seem to be a promising approach to overcome these difficulties due to their flexible and sparse nature, but they have not been exploited yet. In this work, we present a multi-level graph-based model that performs entity building and linking on unstructured documents, purely based on GNNs, and extremely light (0.3 million parameters). We also propose a novel strategy for an optimal propagation of the information between the graph levels based on hypernodes. The conducted experiments on public and private datasets demonstrate that our model is suitable for solving the tasks, and that the proposed propagation strategy is optimal and outperforms other approaches.
Cross-Modal Contextualized Hidden State Projection Method for Expanding of Taxonomic Graphs
Authors: Irina Nikishina, Alsu Vakhitova, Elena Tutubalina, Alexander Panchenko
Date: 2022-10-01 00:00:00
Proceeding: COLING (TextGraphs) 2022 10
Tasks: graph construction, Graph Embedding

Taxonomy is a graph of terms organized hierarchically using is-a (hypernymy) relations. We suggest novel candidate-free task formulation for the taxonomy enrichment task. To solve the task, we leverage lexical knowledge from the pre-trained models to predict new words missing in the taxonomic resource. We propose a method that combines graph-, and text-based contextualized representations from transformer networks to predict new entries to the taxonomy. We have evaluated the method suggested for this task against text-only baselines based on BERT and fastText representations. The results demonstrate that incorporation of graph embedding is beneficial in the task of hyponym prediction using contextualized models. We hope the new challenging task will foster further research in automatic text graph construction methods.
A Clique-based Graphical Approach to Detect Interpretable Adjectival Senses in Hungarian
Authors: Enikő Héja, Noémi Ligeti-Nagy
Date: 2022-10-01 00:00:00
Proceeding: COLING (TextGraphs) 2022 10

The present paper introduces an ongoing research which aims to detect interpretable adjectival senses from monolingual corpora applying an unsupervised WSI approach. According to our expectations the findings of our investigation are going to contribute to the work of lexicographers, linguists and also facilitate the creation of benchmarks with semantic information for the NLP community. For doing so, we set up four criteria to distinguish between senses. We experiment with a graphical approach to model our criteria and then perform a detailed, linguistically motivated manual evaluation of the results.
GUSUM: Graph-based Unsupervised Summarization Using Sentence Features Scoring and Sentence-BERT
Authors: Tuba Gokhan, Phillip Smith, Mark Lee
Date: 2022-10-01 00:00:00
Proceeding: COLING (TextGraphs) 2022 10
Tasks: Document Summarization, Extractive Document Summarization, Extractive Text Summarization, Graph Ranking, Sentence, Sentence Embedding, Sentence-Embedding, Sentence Embeddings, Text Summarization

Unsupervised extractive document summarization aims to extract salient sentences from a document without requiring a labelled corpus. In existing graph-based methods, vertex and edge weights are usually created by calculating sentence similarities. In this paper, we develop a Graph-Based Unsupervised Summarization(GUSUM) method for extractive text summarization based on the principle of including the most important sentences while excluding sentences with similar meanings in the summary. We modify traditional graph ranking algorithms with recent sentence embedding models and sentence features and modify how sentence centrality is computed. We first define the sentence feature scores represented at the vertices, indicating the importance of each sentence in the document. After this stage, we use Sentence-BERT for obtaining sentence embeddings to better capture the sentence meaning. In this way, we define the edges of a graph where semantic similarities are represented. Next we create an undirected graph that includes sentence significance and similarities between sentences. In the last stage, we determine the most important sentences in the document with the ranking method we suggested on the graph created. Experiments on CNN/Daily Mail, New York Times, arXiv, and PubMed datasets show our approach achieves high performance on unsupervised graph-based summarization when evaluated both automatically and by humans.
Text-Aware Graph Embeddings for Donation Behavior Prediction
Authors: MeiXing Dong, Xueming Xu, Rada Mihalcea
Date: 2022-10-01 00:00:00
Proceeding: COLING (TextGraphs) 2022 10
Tasks: Prediction

Predicting user behavior is essential for a large number of applications including recommender and dialog systems, and more broadly in domains such as healthcare, education, and economics. In this paper, we show that we can effectively predict donation behavior by using text-aware graph models, building upon graphs that connect user behaviors and their interests. Using a university donation dataset, we show that the graph representation significantly improves over learning from textual representations. Moreover, we show how incorporating implicit information inferred from text associated with the graph entities brings additional improvements. Our results demonstrate the role played by text-aware graph representations in predicting donation behavior.
Word Sense Disambiguation of French Lexicographical Examples Using Lexical Networks
Authors: Aman Sinha, Sandrine Ollinger, Mathieu Constant
Date: 2022-10-01 00:00:00
Proceeding: COLING (TextGraphs) 2022 10
Tasks: Word Sense Disambiguation

This paper focuses on the task of word sense disambiguation (WSD) on lexicographic examples relying on the French Lexical Network (fr-LN). For this purpose, we exploit the lexical and relational properties of the network, that we integrated in a feedforward neural WSD model on top of pretrained French BERT embeddings. We provide a comparative study with various models and further show the impact of our approach regarding polysemic units.
Temporal Graph Analysis of Misinformation Spreaders in Social Media
Authors: Flora Sakketou, Joan Plepi, Henri-Jacques Geiss, Lucie Flek
Date: 2022-10-01 00:00:00
Proceeding: COLING (TextGraphs) 2022 10
Tasks: Misinformation

Proactively identifying misinformation spreaders is an important step towards mitigating the impact of fake news on our society. Although the news domain is subject to rapid changes over time, the temporal dynamics of the spreaders’ language and network have not been explored yet. In this paper, we analyze the users’ time-evolving semantic similarities and social interactions and show that such patterns can, on their own, indicate misinformation spreading. Building on these observations, we propose a dynamic graph-based framework that leverages the dynamic nature of the users’ network for detecting fake news spreaders. We validate our design choice through qualitative analysis and demonstrate the contributions of our model’s components through a series of exploratory and ablative experiments on two datasets.