ymoslem/acl-abstracts huggingface dataset:
Efficient Domain Adaptation of Sentence Embeddings Using Adapters (2023)

Sentence embeddings enable us to capture the semantic similarity of short texts. Most sentence embedding models are trained for general semantic textual similarity tasks. Therefore, to use sentence embeddings in a particular domain, the model must be adapted to it in order to achieve good results. Usually, this is done by fine-tuning the entire sentence embedding model for the domain of interest. While this approach yields state-of-the-art results, all of the model`s weights are updated during fine-tuning, making this method resource-intensive. Therefore, instead of fine-tuning entire sentence embedding models for each target domain individually, we propose to train lightweight adapters. These domain-specific adapters do not require fine-tuning all underlying sentence embedding model parameters. Instead, we only train a small number of additional parameters while keeping the weights of the underlying sentence embedding model fixed. Training domain-specific adapters allows always using the same base model and only exchanging the domain-specific adapters to adapt sentence embeddings to a specific domain. We show that using adapters for parameter-efficient domain adaptation of sentence embeddings yields competitive performance within 1{\%} of a domain-adapted, entirely fine-tuned sentence embedding model while only training approximately 3.6{\%} of the parameters.
AspectCSE: Sentence Embeddings for Aspect-Based Semantic Textual Similarity Using Contrastive Learning and Structured Knowledge (2023)

Generic sentence embeddings provide coarse-grained approximation of semantic textual similarity, but ignore specific aspects that make texts similar. Conversely, aspect-based sentence embeddings provide similarities between texts based on certain predefined aspects. Thus, similarity predictions of texts are more targeted to specific requirements and more easily explainable. In this paper, we present AspectCSE, an approach for aspect-based contrastive learning of sentence embeddings. Results indicate that AspectCSE achieves an average improvement of 3.97{\%} on information retrieval tasks across multiple aspects compared to the previous best results. We also propose the use of Wikidata knowledge graph properties to train models of multi-aspect sentence embeddings in which multiple specific aspects are simultaneously considered during similarity predictions. We demonstrate that multi-aspect embeddings outperform even single-aspect embeddings on aspect-specific information retrieval tasks. Finally, we examine the aspect-based sentence embedding space and demonstrate that embeddings of semantically similar aspect labels are often close, even without explicit similarity training between different aspect labels.
Tackling the Myriads of Collusion Scams on YouTube Comments of Cryptocurrency Videos (2023)

Despite repeated measures, YouTube`s comment section has been a fertile ground for scammers. With the growth of the cryptocurrency market and obscurity around it, a new form of scam, namely {\textquotedblleft}Collusion Scam{\textquotedblright} has emerged as a dominant force within YouTube`s comment space. Unlike typical scams and spams, collusion scams employ a cunning persuasion strategy, using the facade of genuine social interactions within comment threads to create an aura of trust and success to entrap innocent users. In this research, we collect 1,174 such collusion scam threads and perform a detailed analysis, which is tailored towards the successful detection of these scams. We find that utilization of the collusion dynamics can provide an accuracy of 96.67{\%} and an F1-score of 93.04{\%}. Furthermore, we demonstrate the robust predictive power of metadata associated with these threads and user channels, which act as compelling indicators of collusion scams. Finally, we show that modern LLM, like chatGPT, can effectively detect collusion scams without the need for any training.
Exploring Deceptive Domain Transfer Strategies: Mitigating the Differences among Deceptive Domains (2023)

Deceptive text poses a significant threat to users, resulting in widespread misinformation and disorder. While researchers have created numerous cutting-edge techniques for detecting deception in domain-specific settings, whether there is a generic deception pattern so that deception-related knowledge in one domain can be transferred to the other remains mostly unexplored. Moreover, the disparities in textual expression across these many mediums pose an additional obstacle for generalization. To this end, we present a Multi-Task Learning (MTL)-based deception generalization strategy to reduce the domain-specific noise and facilitate a better understanding of deception via a generalized training. As deceptive domains, we use News (fake news), Tweets (rumors), and Reviews (fake reviews) and employ LSTM and BERT model to incorporate domain transfer techniques. Our proposed architecture for the combined approach of domain-independent and domain-specific training improves the deception detection performance by up to 5.28{\%} in F1-score.
Party Extraction from Legal Contract Using Contextualized Span Representations of Parties (2023)

Extracting legal entities from legal documents, particularly legal parties in contract documents, poses a significant challenge for legal assistive software. Many existing party extraction systems tend to generate numerous false positives due to the complex structure of the legal text. In this study, we present a novel and accurate method for extracting parties from legal contract documents by leveraging contextual span representations. To facilitate our approach, we have curated a large-scale dataset comprising 1000 contract documents with party annotations. Our method incorporates several enhancements to the SQuAD 2.0 question-answering system, specifically tailored to handle the intricate nature of the legal text. These enhancements include modifications to the activation function, an increased number of encoder layers, and the addition of normalization and dropout layers stacked on top of the output encoder layer. Baseline experiments reveal that our model, fine-tuned on our dataset, outperforms the current state-of-the-art model. Furthermore, we explore various combinations of the aforementioned techniques to further enhance the accuracy of our method. By employing a hybrid approach that combines 24 encoder layers with normalization and dropout layers, we achieve the best results, exhibiting an exact match score of 0.942 (+6.2{\%} improvement).
From Fake to Hyperpartisan News Detection Using Domain Adaptation (2023)

Unsupervised Domain Adaptation (UDA) is a popular technique that aims to reduce the domain shift between two data distributions. It was successfully applied in computer vision and natural language processing. In the current work, we explore the effects of various unsupervised domain adaptation techniques between two text classification tasks: fake and hyperpartisan news detection. We investigate the knowledge transfer from fake to hyperpartisan news detection without involving target labels during training. Thus, we evaluate UDA, cluster alignment with a teacher, and cross-domain contrastive learning. Extensive experiments show that these techniques improve performance, while including data augmentation further enhances the results. In addition, we combine clustering and topic modeling algorithms with UDA, resulting in improved performances compared to the initial UDA setup.
Prompt-Based Approach for Czech Sentiment Analysis (2023)

This paper introduces the first prompt-based methods for aspect-based sentiment analysis and sentiment classification in Czech. We employ the sequence-to-sequence models to solve the aspect-based tasks simultaneously and demonstrate the superiority of our prompt-based approach over traditional fine-tuning. In addition, we conduct zero-shot and few-shot learning experiments for sentiment classification and show that prompting yields significantly better results with limited training examples compared to traditional fine-tuning. We also demonstrate that pre-training on data from the target domain can lead to significant improvements in a zero-shot scenario.
Measuring Gender Bias in Natural Language Processing: Incorporating Gender-Neutral Linguistic Forms for Non-Binary Gender Identities in Abusive Speech Detection (2023)

Predictions from machine learning models can reflect bias in the data on which they are trained. Gender bias has been shown to be prevalent in natural language processing models. The research into identifying and mitigating gender bias in these models predominantly considers gender as binary, male and female, neglecting the fluidity and continuity of gender as a variable. In this paper, we present an approach to evaluate gender bias in a prediction task, which recognises the non-binary nature of gender. We gender-neutralise a random subset of existing real-world hate speech data. We extend the existing template approach for measuring gender bias to include test examples that are gender-neutral. Measuring the bias across a selection of hate speech datasets we show that the bias for the gender-neutral data is closer to that seen for test instances that identify as male than those that identify as female.
LeSS: A Computationally-Light Lexical Simplifier for Spanish (2023)

Due to having knowledge of only basic vocabulary, many people cannot understand up-to-date written information and thus make informed decisions and fully participate in the society. We propose LeSS, a modular lexical simplification architecture that outperforms state-of-the-art lexical simplification systems for Spanish. In addition to its state-of-the-art performance, LeSS is computationally light, using much less disk space, CPU and GPU, and having faster loading and execution time than the transformer-based lexical simplification models which are predominant in the field.
Hindi to Dravidian Language Neural Machine Translation Systems (2023)

Neural machine translation (NMT) has achieved state-of-art performance in high-resource language pairs, but the performance of NMT drops in low-resource conditions. Morphologically rich languages are yet another challenge in NMT. The common strategy to handle this issue is to apply sub-word segmentation. In this work, we compare the morphologically inspired segmentation methods against the Byte Pair Encoding (BPE) in processing the input for building NMT systems for Hindi to Malayalam and Hindi to Tamil, where Hindi is an Indo-Aryan language and Malayalam and Tamil are south Dravidian languages. These two languages are low resource, morphologically rich and agglutinative. Malayalam is more agglutinative than Tamil. We show that for both the language pairs, the morphological segmentation algorithm out-performs BPE. We also present an elaborate analysis on translation outputs from both the NMT systems.
Looking for Traces of Textual Deepfakes in Bulgarian on Social Media (2023)

Textual deepfakes can cause harm, especially on social media. At the moment, there are models trained to detect deepfake messages mainly for the English language, but no research or datasets currently exist for detecting them in most low-resource languages, such as Bulgarian. To address this gap, we explore three approaches. First, we machine translate an English-language social media dataset with bot messages into Bulgarian. However, the translation quality is unsatisfactory, leading us to create a new Bulgarian-language dataset with real social media messages and those generated by two language models (a new Bulgarian GPT-2 model {--} GPT-WEB-BG, and ChatGPT). We machine translate it into English and test existing English GPT-2 and ChatGPT detectors on it, achieving only 0.44-0.51 accuracy. Next, we train our own classifiers on the Bulgarian dataset, obtaining an accuracy of 0.97. Additionally, we apply the classifier with the highest results to a recently released Bulgarian social media dataset with manually fact-checked messages, which successfully identifies some of the messages as generated by Language Models (LM). Our results show that the use of machine translation is not suitable for textual deepfakes detection. We conclude that combining LM text detection with fact-checking is the most appropriate method for this task, and that identifying Bulgarian textual deepfakes is indeed possible.
Propaganda Detection in Russian Telegram Posts in the Scope of the Russian Invasion of Ukraine (2023)

The emergence of social media has made it more difficult to recognize and analyze misinformation efforts. Popular messaging software Telegram has developed into a medium for disseminating political messages and misinformation, particularly in light of the conflict in Ukraine. In this paper, we introduce a sizable corpus of Telegram posts containing pro-Russian propaganda and benign political texts. We evaluate the corpus by applying natural language processing (NLP) techniques to the task of text classification in this corpus. Our findings indicate that, with an overall accuracy of over 96{\%} for confirmed sources as propagandists and oppositions and 92{\%} for unconfirmed sources, our method can successfully identify and categorize pro- Russian propaganda posts. We highlight the consequences of our research for comprehending political communications and propaganda on social media.
Auto-Encoding Questions with Retrieval Augmented Decoding for Unsupervised Passage Retrieval and Zero-Shot Question Generation (2023)

Dense passage retrieval models have become state-of-the-art for information retrieval on many Open-domain Question Answering (ODQA) datasets. However, most of these models rely on supervision obtained from the ODQA datasets, which hinders their performance in a low-resource setting. Recently, retrieval-augmented language models have been proposed to improve both zero-shot and supervised information retrieval. However, these models have pre-training tasks that are agnostic to the target task of passage retrieval. In this work, we propose Retrieval Augmented Auto-encoding of Questions for zero-shot dense information retrieval. Unlike other pre-training methods, our pre-training method is built for target information retrieval, thereby making the pre-training more efficient. Our method consists of a dense IR model for encoding questions and retrieving documents during training and a conditional language model that maximizes the question`s likelihood by marginalizing over retrieved documents. As a by-product, we can use this conditional language model for zero-shot question generation from documents. We show that the IR model obtained through our method improves the current state-of-the-art of zero-shot dense information retrieval, and we improve the results even further by training on a synthetic corpus created by zero-shot question generation.
NoHateBrazil: A Brazilian Portuguese Text Offensiveness Analysis System (2023)

Hate speech is a surely relevant problem in Brazil. Nevertheless, its regulation is not effective due to the difficulty to identify, quantify and classify offensive comments. Here, we introduce a novel system for offensive comment analysis in Brazilian Portuguese. The system titled {\textquotedblleft}NoHateBrazil{\textquotedblright} recognizes explicit and implicit offensiveness in context at a fine-grained level. Specifically, we propose a framework for data collection, human annotation and machine learning models that were used to build the system. In addition, we assess the potential of our system to reflect stereotypical beliefs against marginalized groups by contrasting them with counter-stereotypes. As a result, a friendly web application was implemented, which besides presenting relevant performance, showed promising results towards mitigation of the risk of reinforcing social stereotypes. Lastly, new measures were proposed to improve the explainability of offensiveness classification and reliability of the model`s predictions.
Socially Responsible Hate Speech Detection: Can Classifiers Reflect Social Stereotypes? (2023)

Recent studies have shown that hate speech technologies may propagate social stereotypes against marginalized groups. Nevertheless, there has been a lack of realistic approaches to assess and mitigate biased technologies. In this paper, we introduce a new approach to analyze the potential of hate-speech classifiers to reflect social stereotypes through the investigation of stereotypical beliefs by contrasting them with counter-stereotypes. We empirically measure the distribution of stereotypical beliefs by analyzing the distinctive classification of tuples containing stereotypes versus counter-stereotypes in machine learning models and datasets. Experiment results show that hate speech classifiers attribute unreal or negligent offensiveness to social identity groups by reflecting and reinforcing stereotypical beliefs regarding minorities. Furthermore, we also found that models that embed expert and context information from offensiveness markers present promising results to mitigate social stereotype bias towards socially responsible hate speech detection.
Predicting Sentence-Level Factuality of News and Bias of Media Outlets (2023)

Automated news credibility and fact-checking at scale require accurate prediction of news factuality and media bias. This paper introduces a large sentence-level dataset, titled {\textquotedblleft}FactNews{\textquotedblright}, composed of 6,191 sentences expertly annotated according to factuality and media bias definitions proposed by AllSides. We use FactNews to assess the overall reliability of news sources by formulating two text classification problems for predicting sentence-level factuality of news reporting and bias of media outlets. Our experiments demonstrate that biased sentences present a higher number of words compared to factual sentences, besides having a predominance of emotions. Hence, the fine-grained analysis of subjectivity and impartiality of news articles showed promising results for predicting the reliability of entire media outlets. Finally, due to the severity of fake news and political polarization in Brazil, and the lack of research for Portuguese, both dataset and baseline were proposed for Brazilian Portuguese.
Classification of US Supreme Court Cases Using BERT-Based Techniques (2023)

Models based on bidirectional encoder representations from transformers (BERT) produce state of the art (SOTA) results on many natural language processing (NLP) tasks such as named entity recognition (NER), part-of-speech (POS) tagging etc. An interesting phenomenon occurs when classifying long documents such as those from the US supreme court where BERT-based models can be considered difficult to use on a first-pass or out-of-the-box basis. In this paper, we experiment with several BERT-based classification techniques for US supreme court decisions or supreme court database (SCDB) and compare them with the previous SOTA results. We then compare our results specifically with SOTA models for long documents. We compare our results for two classification tasks: (1) a broad classification task with 15 categories and (2) a fine-grained classification task with 279 categories. Our best result produces an accuracy of 80{\%} on the 15 broad categories and 60{\%} on the fine-grained 279 categories which marks an improvement of 8{\%} and 28{\%} respectively from previously reported SOTA results.
K\=araka-Based Answer Retrieval for Question Answering in Indic Languages (2023)

K{\={a}}rakas from ancient Paninian grammar form a concise set of semantic roles that capture crucial aspect of sentence meaning pivoted on the action verb. In this paper, we propose employing a k{\={a}}raka-based approach for retrieving answers in Indic question-answering systems. To study and evaluate this novel approach, empirical experiments are conducted over large benchmark corpora in Hindi and Marathi. The results obtained demonstrate the effectiveness of the proposed method. Additionally, we explore the varying impact of two approaches for extracting k{\={a}}rakas. The literature surveyed and experiments conducted encourage hope that k{\={a}}raka annotation can improve communication with machines using natural languages, particularly in low-resource languages.
Comparative Analysis of Named Entity Recognition in the Dungeons and Dragons Domain (2023)

Some Natural Language Processing (NLP) tasks that are in the sufficiently solved state for general domain English still struggle to attain the same level of performance in specific domains. Named Entity Recognition (NER), which aims to find and categorize entities in text is such a task met with difficulties in adapting to domain specificity. This paper compares the performance of 10 NER models on 7 adventure books from the Dungeons and Dragons (D{\&}D) domain which is a subdomain of fantasy literature. Fantasy literature, being rich and diverse in vocabulary, poses considerable challenges for conventional NER. In this study, we use open-source Large Language Models (LLM) to annotate the named entities and character names in each number of official D{\&}D books and evaluate the precision and distribution of each model. The paper aims to identify the challenges and opportunities for improving NER in fantasy literature. Our results show that even in the off-the-shelf configuration, Flair, Trankit, and Spacy achieve better results for identifying named entities in the D{\&}D domain compared to their peers.
Comparative Analysis of Anomaly Detection Algorithms in Text Data (2023)

Text anomaly detection (TAD) is a crucial task that aims to identify texts that deviate significantly from the norm within a corpus. Despite its importance in various domains, TAD remains relatively underexplored in natural language processing. This article presents a systematic evaluation of 22 TAD algorithms on 17 corpora using multiple text representations, including monolingual and multilingual SBERT. The performance of the algorithms is compared based on three criteria: degree of supervision, theoretical basis, and architecture used. The results demonstrate that semi-supervised methods utilizing weak labels outperform both unsupervised methods and semi-supervised methods using only negative samples for training. Additionally, we explore the application of TAD techniques in hate speech detection. The results provide valuable insights for future TAD research and guide the selection of suitable algorithms for detecting text anomalies in different contexts.
Poetry Generation Combining Poetry Theme Labels Representations (2023)

Ancient Chinese poetry is the earliest literary genre that took shape in Chinese literature and has a dissemination effect, showing China`s profound cultural heritage. At the same time, the generation of ancient poetry is an important task in the field of digital humanities, which is of great significance to the inheritance of national culture and the education of ancient poetry. The current work in the field of poetry generation is mainly aimed at improving the fluency and structural accuracy of words and sentences, ignoring the theme unity of poetry generation results. In order to solve this problem, this paper proposes a graph neural network poetry theme representation model based on label embedding. On the basis of the network representation of poetry, the topic feature representation of poetry is constructed and learned from the granularity of words. Then, the features of the poetry theme representation model are combined with the autoregressive language model to construct a theme-oriented ancient Chinese poetry generation model TLPG (Poetry Generation with Theme Label). Through machine evaluation and evaluation by experts in related fields, the model proposed in this paper has significantly improved the topic consistency of poetry generation compared with existing work on the premise of ensuring the fluency and format accuracy of poetry.