The proliferation of social networks has caused an increase in the amount of textual content generated by users. The voluminous nature of such content poses a challenge to users, necessitating the development of technological solutions for automatic summarisation. This paper presents a two-stage framework for generating abstractive summaries from a collection of Twitter texts. In the first stage of the framework, event detection is carried out through clustering, followed by event summarisation in the second stage. Our approach involves generating contextualised vector representations of tweets and applying various clustering techniques to the vectors. The quality of the resulting clusters is evaluated, and the best clusters are selected for the summarisation task based on this evaluation. In contrast to previous studies, we experimented with various clustering techniques as a preprocessing step to obtain better event representations. For the summarisation task, we utilised pre-trained models of three state-of-the-art deep neural network architectures and evaluated their performance on abstractive summarisation of the event clusters. Summaries are generated from clusters that contain (a) unranked tweets, (b) all ranked tweets, and (c) the top 10 ranked tweets. Of these three sets of clusters, we obtained the best ROUGE scores from the top 10 ranked tweets. From the summaries generated from the clusters containing the top ten tweets, we obtained ROUGE-1 F score of 48%, ROUGE-2 F score of 37%, ROUGE-L F score of 44%, and ROUGE-SU F score of 33% which suggests that if relevant tweets are at the top of a cluster, and then better summaries are generated.

Although pretrained large language models (PLMs) have achieved state of the art on many natural language processing tasks, they lack an understanding of subtle expressions of implicit hate speech. Various attempts have been made to enhance the detection of implicit hate by augmenting external context or enforcing label separation via distance-based metrics. Combining these two approaches, we introduce FiADD, a novel focused inferential adaptive density discrimination framework. FiADD enhances the PLM finetuning pipeline by bringing the surface form/meaning of an implicit hate speech closer to its implied form while increasing the intercluster distance among various labels. We test FiADD on three implicit hate datasets and observe significant improvement in the two-way and three-way hate classification tasks. We further experiment on the generalizability of FiADD on three other tasks, detecting sarcasm, irony, and stance, in which surface and implied forms differ, and observe similar performance improvements. Consequently, we analyze the generated latent space to understand its evolution under FiADD, which corroborates the advantage of employing FiADD for implicit hate speech detection.

This special issue focuses on the many roles that context plays in neural machine translation and the evaluation of machine translation. It includes a survey of the current state of research related to context in machine translation and machine translation evaluation, along with three papers that focus on a variety of topics related to evaluation. Together, these highlight the wide range of topics in this area, and its increasing relevance as high-quality machine translation becomes increasingly common in a variety of settings.

Hate speech against individuals and groups with certain demographics is a major issue in social media. Supervised models for hate speech detection mostly utilize labeled data collections to understand textual semantics. However, hate speech detection is a complex task that involves several aspects, including topic and writing style. The complexity of hate speech can be represented by an ensemble of models learned from different aspects of data. Moreover, ensemble members or base models can be modified to give attention to particular aspects of hate speech. In this study, we extract different aspects of hate speech to construct ensembles, thereby improving the performance of hate speech detection by ensemble learning. We conduct detailed experiments on five datasets in multiple languages to generalize our observations. The experimental results, supported by statistical significance tests, show that the performance of hate speech detection can be improved by capturing multiple aspects of hate speech. Our ensemble construction approach outperforms the baselines in terms of the F1 score of the Hate class in 80% of the cases, and the Offensive class in 75% of the cases. We also compare our approach with state-of-the-art ensemble methods from shared tasks and find that our highest-performing method can improve the performance of the Hate class in two out of three datasets. We further discuss our approach and experimental results in terms of ensemble parameters and writing style among ensemble members.

This paper addresses the optimization of retrieval-augmented generation (RAG) processes by exploring various methodologies, including advanced RAG methods. The research, driven by the need to enhance RAG processes as highlighted by recent studies, involved a grid-search optimization of 23,625 iterations. We evaluated multiple RAG methods across different vectorstores, embedding models, and large language models, using cross-domain datasets and contextual compression filters. The findings emphasize the importance of balancing context quality with similarity-based ranking methods, as well as understanding tradeoffs between similarity scores, token usage, runtime, and hardware utilization. Additionally, contextual compression filters were found to be crucial for efficient hardware utilization and reduced token consumption, despite the evident impacts on similarity scores, which may be acceptable depending on specific use cases and RAG methods.

Natural language processing (NLP) has significantly advanced our ability to model and interact with human language through technology. However, these advancements have disproportionately benefited high-resource languages with abundant data for training complex models. Low-resource languages, often spoken by smaller or marginalized communities, need help realizing the full potential of NLP applications. The primary challenges in developing NLP applications for low-resource languages stem from the need for large, well-annotated datasets, standardized tools, and linguistic resources. This scarcity of resources hinders the performance of data-driven approaches that have excelled in high-resource settings. Further, low-resource languages frequently exhibit complex grammatical structures, diverse vocabularies, and unique social contexts, which pose additional challenges for standard NLP techniques. Innovative strategies are emerging to address these challenges. Researchers are actively collecting and curating datasets, even utilizing community engagement platforms to expand data resources. Transfer learning, where models pre-trained on high-resource languages are adapted to low-resource settings, has shown significant promise. Multilingual models like Multilingual Bidirectional Encoder Representations from Transformers (mBERT) and Cross Lingual Models (XLM-R), trained on vast quantities of multilingual data, offer a powerful avenue for cross-lingual knowledge transfer. Additionally, researchers are exploring integrating multimodal approaches, combining textual data with images, audio, or video, to enhance NLP performance in low-resource language scenarios. This survey covers applications like part-of-speech tagging, morphological analysis, sentiment analysis, hate speech detection, dependency parsing, language identification, discourse annotation guidelines, question answering, machine translation, information retrieval, and predictive authoring for augmentative and alternative communication systems. The review also highlights machine learning approaches, deep learning approaches, Transformers, and cross-lingual transfer learning as practical techniques. Developing practical NLP applications for low-resource languages is crucial for preserving linguistic diversity, fostering inclusion within the digital world, and expanding our understanding of human language. While challenges remain, the strategies outlined in this survey demonstrate the ongoing progress and highlight the potential for NLP to empower communities that speak low-resource languages and contribute to a more equitable landscape within language technology.

It is increasingly common to use chatbots as an interface to services. One of the main components of a chatbot is the Natural Language Understanding (NLU) model, which is responsible for interpreting the text and extracting the intent and entities present in that text. It’s possible to focus only on one of these tasks of NLU, such as intent classification. To train an NLU intent classification model, it’s generally necessary to use a considerable amount of annotated data, where each sentence of the dataset receives a label indicating an intent. Performing manually labeling data is arduous and impracticable, depending on the data volume. Thus, an unsupervised machine learning technique, such as data clustering, could be applied to find and label patterns in the data. For this task, it is essential to have an effective vector embedding representation of texts that depicts the semantic information and helps the machine understand the context, intent, and other nuances of the entire text. This paper extensively evaluates different text embedding models for clustering and labeling. We also apply some operations to improve the dataset’s quality, such as removing sentences and establishing various strategies for distance thresholds (cosine similarity) for the clusters’ centroids. Then, we trained some intent classification Models with two different architectures, one built with the Rasa framework and the other with a neural network (NN) using the attendance text from the Coronavirus Platform Service of Ceará, Brazil. We also manually annotated a dataset to be used as validation data. We conducted a study on semiautomatic labeling, implemented through clustering and visual inspection, which introduced some labeling errors to the intent classification models. However, it would be unfeasible to annotate the entire dataset manually. Nevertheless, results of competitive accuracy were still achieved with the trained models.

Hierarchical text classification (HTC) is a natural language processing task which aims to categorise a text document into a set of classes from a hierarchical class structure. Recent approaches to solve HTC tasks focus on leveraging pre-trained language models (PLMs) and the hierarchical class structure by allowing these components to interact in various ways. Specifically, the Hierarchy-aware Prompt Tuning (HPT) method has proven to be effective in applying the prompt tuning paradigm to Bidirectional Encoder Representations from Transformers (BERT) models for HTC tasks. Prompt tuning aims to reduce the gap between the pre-training and fine-tuning phases by transforming the downstream task into the pre-training task of the PLM. Discriminative PLMs, which use a replaced token detection (RTD) pre-training task, have also shown to perform better on flat text classification tasks when using prompt tuning instead of vanilla fine-tuning. In this paper, we propose the Hierarchy-aware Prompt Tuning for Discriminative PLMs (HPTD) approach which injects the HTC task into the RTD task used to pre-train discriminative PLMs. Furthermore, we make several improvements to the prompt tuning approach of discriminative PLMs that enable HTC tasks to scale to much larger hierarchical class structures. Through comprehensive experiments, we show that our method is robust and outperforms current state-of-the-art approaches on two out of three HTC benchmark datasets.

Detecting and removing hate speech content in a timely manner remains a challenge for social media platforms. Automated techniques such as deep learning models offer solutions which can keep up with the volume and velocity of user content production. Research in this area has mainly focused on either binary classification or on classifying tweets into generalised categories such as hateful, offensive, or neither. Less attention has been given to multiclass classification of online hate speech into the type of hate or group at which it is directed. By aggregating and re-annotating several relevant hate speech datasets, this study presents a dataset and evaluates several models for classifying tweets into the categories ethnicity, gender, religion, sexuality, and non-hate. We evaluate the dataset by training several models: logistic regression, LSTM, BERT, and GPT-2. For the LSTM model, we assess a range of NLP features using a multi-classification LSTM model, and conclude that the highest performing feature combination consists of word $n$-grams, character $n$-grams, and dependency tuples. We show that while more recent larger models can achieve a slightly higher performance, increased model complexity alone is not sufficient to achieve significantly improved models. We also compare this approach with a binary classification approach and evaluate the effect of dataset size on model performance.

Machine reading comprehension (MRC) refers to the process of instructing machines to comprehend and respond to inquiries based on a provided text. There are two primary methodologies for achieving this: extracting answers directly from the text or predicting them. Extracting answers involves anticipating the specific segment of text containing the answer, pinpointed by its starting and ending indices within the paragraph. Despite the increasing interest in MRC, exploration within the framework of the Arabic language faces limitations due to various challenges. A significant impediment arises from the inadequacy of resources available for Arabic textual content, which impedes the development of effective models. Furthermore, the inherent intricacies of Arabic, manifesting in its diverse linguistic forms including classical, modern standard, and colloquial, present distinctive hurdles for tasks involving language comprehension. This paper proposes an enhanced version of the bidirectional attention flow (BIDAF) model for Arabic MRC, constructed upon the Arabic Span-Extraction-based Reading Comprehension Benchmark (ASER). ASER comprises 10,000 sets of questions, answers, and passages, partitioned into a training set constituting 90% of the data and a testing set making up the remaining 10%. By introducing a new input feature based on parts-of-speech (POS) word embeddings and replacing Bidirectional Long Short-Term Memory (bi-LSTM) with bidirectional gated recurrent unit, significant improvements were observed. Eight different POS word embeddings were generated using both Continuous Bag of Words (CBOW) and Skip-gram methods, with varying dimensionalities. Evaluation metrics, including exact match (EM) and F1-measure, were utilized to assess model performance, with emphasis on the latter for its accuracy. The proposed enhanced BIDAF model achieved a remarkable accuracy of 75.22% on the ASER dataset, demonstrating its efficacy in Arabic MRC tasks. Additionally, rigorous statistical evaluation using a two-tailed paired samples t-test further validated the findings, highlighting the significance of the proposed enhancements in advancing Arabic language processing capabilities.

The question of context in neural machine translation often focuses on topics related to document-level translation or intersentential context. However, there is a wide range of other aspects that can be considered under the umbrella of context. In this work, we survey ways that researchers have incorporated context into neural machine translation systems and the evaluation thereof. This includes building translation systems that operate at the paragraph level or the document level or ones that translate at the sentence level but incorporate information from other sentences. We also consider how issues like terminology consistency, anaphora, and world knowledge or external information can be considered as types of context relevant to the task of machine translation and its evaluation. Closely tied to these topics is the question of how to best evaluate machine translation output in a way that is sensitive to the contexts in which it appears. To this end, we discuss work on incorporating context into both human and automatic evaluations of machine translation quality. Furthermore, we also discuss recent experiments in the field as they relate to the use of large language models in translation and evaluation. We conclude with a view of the future of machine translation, where we expect to see issues of context continue to come to the forefront.

Morphological analysis is a crucial preprocessing stage for building the state-of-the-art natural language processing applications. We propose a bidirectional LSTM (long short-term memory)-based approach to develop the morphological analyzer for the Gujarati language. Our morph analyzer predicts a root word and the morphological features for the given inflected word. We have experimented with two different methods for label representation for predicting morphological features: the monolithic representation method and the individual label representation method. We have also created the gold morphological dataset of 16,234 unique words for the Gujarati language. The dataset contains morpheme splitting and grammatical feature information for each inflected word. Due to the change in the label representation technique in the proposed model, the accuracy of the present baseline system is improved by a large margin. The proposed system performs very well across the POS categories without the knowledge of language-specific suffix rules.

Text classification methods have been widely investigated as a way to detect content of low credibility: fake news, social media bots, propaganda, etc. Quite accurate models (likely based on deep neural networks) help in moderating public electronic platforms and often cause content creators to face rejection of their submissions or removal of already published texts. Having the incentive to evade further detection, content creators try to come up with a slightly modified version of the text (known as an attack with an adversarial example) that exploit the weaknesses of classifiers and result in a different output. Here we systematically test the robustness of common text classifiers against available attacking techniques and discover that, indeed, meaning-preserving changes in input text can mislead the models. The approaches we test focus on finding vulnerable spans in text and replacing individual characters or words, taking into account the similarity between the original and replacement content. We also introduce BODEGA: a benchmark for testing both victim models and attack methods on four misinformation detection tasks in an evaluation framework designed to simulate real use cases of content moderation. The attacked tasks include (1) fact checking and detection of (2) hyperpartisan news, (3) propaganda, and (4) rumours. Our experimental results show that modern large language models are often more vulnerable to attacks than previous, smaller solutions, e.g. attacks on GEMMA being up to 27% more successful than those on BERT. Finally, we manually analyse a subset adversarial examples and check what kinds of modifications are used in successful attacks.

A negated statement consists of three main components: the negation cue, the negation scope, and the negation reference. The negation cue is the indicator of negation, while the negation scope defines the extent of the negation. The negation reference, which may or may not be within the negation scope, is the part of the statement being negated. Although there has been considerable research on the negation cue and scope, little attention has been given to identifying negation references outside the scope, even though they make up almost half of all negations. In this study, our goal is to identify out-of-scope references (OSRs) to restore the meaning of truncated negated statements identified by negation detection systems. To achieve this, we augment the largest available Spanish clinical dataset by adding annotations for OSRs. Additionally, we fine-tune five robust BERT-based models using transfer learning to address negation detection, uncertainty detection, and OSR identification and linking with their respective negation scopes. Our best model achieves state-of-the-art performance in negation detection while also establishing a competitive baseline for OSR identification (Macro F1 = 0.56) and linking (Macro F1 = 0.86). We support these findings with relevant statistics from the newly annotated dataset and an extensive review of existing literature.

Text readability assessment aims to automatically evaluate the degree of reading difficulty of a given text for a specific group of readers. Most of the previous studies considered it as a classification task and explored a wide range of linguistic features to express the readability of a text from different aspects, such as semantic-based and syntactic-based features. Intuitively, when the external form of a text becomes more complex, individuals will experience more reading difficulties. Based on this motivation, our research attempts to separate the textual external form from the text and investigate its efficiency in determining readability. Specifically, in this paper, we introduce a new concept, namely textual form complexity, to provide a novel insight into text readability. The main idea is that the readability of a text can be measured by the degree to which it is challenging for readers to overcome the distractions of external textual form and obtain the text’s core semantics. To this end, we propose a set of textual form features to express the complexity of the outer form of a text and characterize its readability. Findings show that the proposed external textual form features can be used as effective evaluation indexes to indicate the readability of text. It brings a new perspective to the existing research and provides a new complement to the existing rich features.

Natural language querying allows users to formulate questions in a natural language without requiring specific knowledge of the database query language. Large language models have been very successful in addressing the text-to-SQL problem, which is about translating given questions in textual form into SQL statements. Document-oriented NoSQL databases are gaining popularity in the era of big data due to their ability to handle vast amounts of semi-structured data and provide advanced querying functionalities. However, studies on text-to-NoSQL systems, particularly on systems targeting document databases, are very scarce. In this study, we utilize large language models to create a cross-domain natural language to document database query dataset, DocSpider, leveraging the well-known text-to-SQL challenge dataset Spider. As a document database, we use MongoDB. Furthermore, we conduct experiments to assess the effectiveness of the DocSpider dataset to fine-tune a text-to-NoSQL model against a cross-language transfer learning approach, SQL-to-NoSQL, and zero-shot instruction prompting. The experimental results reveal a significant improvement in the execution accuracy of fine-tuned language models when utilizing the DocSpider dataset.

Human evaluation of machine translation is considered the “gold standard” for evaluation, but it remains a challenging task for which to define best practices. Recent work has focused on incorporating intersentential context into human evaluation, to better distinguish between high-performing machine translation systems and human translations. In this work, we examine several ways that such context influences evaluation and evaluation protocols. We take a close look at annotator variation through the lens of calibration sets and focus on the implications for context-aware evaluation protocols. We then demonstrate one way in which degraded target-side intersentential context can influence annotator scores of individual sentences, a finding that supports the context-aware approach to evaluation and which also has implications for best practices in evaluation protocols.

Emotion recognition in conversation (ERC) faces two major challenges: biased predictions and poor calibration. Classifiers often disproportionately favor certain emotion categories, such as neutral, due to the structural complexity of classifiers, the subjective nature of emotions, and imbalances in training datasets. This bias results in poorly calibrated predictions where the model’s predicted probabilities do not align with the true likelihood of outcomes. To tackle these problems, we introduce the application of conformal prediction (CP) into ERC tasks. CP is a distribution-free method that generates set-valued predictions to ensure marginal coverage in classification, thus improving the calibration of models. However, inherent biases in emotion recognition models prevent baseline CP from achieving a uniform conditional coverage across all classes. We propose a novel CP variant, class spectrum conformation, which significantly reduces coverage bias in CP methods. The methodologies introduced in this study enhance the reliability of prediction calibration and mitigate bias in complex natural language processing tasks.

Language processing systems such as part-of-speech (POS) tagging, named entity recognition, machine translation, speech recognition, and language modeling have been well-studied in high-resource languages. Nevertheless, research on these systems for several low-resource languages, including Bodo, Mizo, Nagamese, and others, is either yet to commence or is in its nascent stages. Language model (LM) plays a vital role in the downstream tasks of modern natural language processing. Extensive studies are carried out on LMs for high-resource languages. However, these low-resource languages are still underreprese. In this study, we first present BodoBERT, an LM for the Bodo language. To the best of our knowledge, this work is the first such effort to develop an LM for Bodo. Second, we present an ensemble deep learning-based POS tagging model for Bodo. The POS tagging model is based on combinations of BiLSTM with conditional random field and stacked embedding of BodoBERT with BytePairEmbeddings. We cover several LMs in the experiment to see how well they work in POS tagging tasks. The best-performing model achieves an F1 score of 0.8041. A comparative experiment was also conducted on Assamese POS taggers, considering that the language is spoken in the same region as Bodo.