Existing benchmarks often fail to account for linguistic diversity, like language variants of English. In this paper, we share our experiences from our ongoing project of building a sentiment classification benchmark for three variants of English: Australian (en-AU), Indian (en-IN), and British (en-UK) English. Using Google Places reviews, we explore the effects of various sampling techniques based on label semantics, review length, and sentiment proportion and report performances on three fine-tuned BERT-based models. Our initial evaluation reveals significant performance variations influenced by sample characteristics, label semantics, and language variety, highlighting the need for nuanced benchmark design. We offer actionable insights for researchers to create robust benchmarks, emphasising the importance of diverse sampling, careful label definition, and comprehensive evaluation across linguistic varieties.

Task-Oriented Dialogue (TOD) systems assist users in completing tasks through natural language interactions, often relying on a single-layered workflow structure for slot-filling in public tasks, such as hotel bookings. However, in enterprise environments, which involve rich domain-specific knowledge, TOD systems face challenges due to task complexity and the lack of standardized documentation. In this work, we introduce HierTOD, an enterprise TOD system driven by hierarchical goals and can support composite workflows. By focusing on goal-driven interactions, our system serves a more proactive role, facilitating mixed-initiative dialogue and improving task completion. Equipped with components for natural language understanding, composite goal retriever, dialogue management, and response generation, backed by a well-organized data service with domain knowledge base and retrieval engine, HierTOD delivers efficient task assistance. Furthermore, our system implementation unifies two TOD paradigms: slot-filling for information collection and step-by-step guidance for task execution. Our human study demonstrates the effectiveness and helpfulness of HierTOD in performing both paradigms.

Natural Language Processing (NLP) operations, such as semantic sentiment analysis and text synthesis, may often impair users' privacy and demand significant on device computational resources. Centralized learning (CL) on the edge offers an alternative energy-efficient approach, yet requires the collection of raw information, which affects the user's privacy. While Federated learning (FL) preserves privacy, it requires high computational energy on board tiny user devices. We introduce split learning (SL) as an energy-efficient alternative, privacy-preserving tiny machine learning (TinyML) scheme and compare it to FL and CL in the presence of Rayleigh fading and additive noise. Our results show that SL reduces processing power and CO2 emissions while maintaining high accuracy, whereas FL offers a balanced compromise between efficiency and privacy. Hence, this study provides insights into deploying energy-efficient, privacy-preserving NLP models on edge devices.

Task-oriented dialogue (TOD) system is designed to accomplish user-defined tasks through dialogues. The TOD system has progressed towards end-to-end modeling by leveraging pre-trained large language models. Fine-tuning the pre-trained language models using only supervised learning leads to the exposure bias and token loss problem and it deviates the models from completing the user's task. To address these issues, we propose a TOD system that leverages a unified pre-trained language model, GPT2, as a base model. It is optimized using supervised learning and reinforcement learning (RL). The issues in the TOD system are mitigated using a non-differentiable reward function. The reward is calculated using the weighted sum of the success rate and BLEU evaluation metrics. The success rate and BLEU metrics in reward calculation guide the language model for user task completion while ensuring a coherent and fluent response. Our model is acquired by fine-tuning a pre-trained model on the dialogue-session level which comprises user utterance, belief state, system act, and system response. Experimental results on MultiWOZ2.1 demonstrate that our model increases the inform rate by 1.60% and the success rate by 3.17% compared to the baseline.

Abstract: This study investigates sentiment patterns within Spanish political party communications on Twitter by employing BETO and RoBERTuito, two pre-trained language models optimized for Spanish text. With a dataset comprising tweets from major Spanish political parties--PSOE, PP, Vox, Podemos, and Ciudadanos--spanning 2019 to 2024, this research analyzes sentiment distributions and explores the relationship between sentiment and party ideology. Results reveal that both models consistently identify a predominant Neutral sentiment across parties, with significant variations in Negative and Positive sentiments that align with ideological distinctions. Vox exhibits higher levels of Negative sentiment, while PSOE demonstrates a relatively high Positive sentiment, supporting the hypothesis that emotional appeals in political messaging reflect ideological stances. This study highlights the utility of pre-trained models in analyzing non-English social media sentiment and underscores the implications of sentiment dynamics in shaping public discourse within a multi-party system. Keywords: Spanish political parties, sentiment analysis, Twitter, BETO, RoBERTuito, political communication, ideology, social media analysis 1. Introduction In the era of digital politics, social media has emerged as a potent platform where public opinion is actively shaped and reflected. For countries like Spain, where a spectrum of political ideologies coexists, understanding the sentiment behind political communications becomes crucial. Sentiment analysis, particularly on platforms like Twitter, serves as a powerful tool to decode public attitudes and the emotional undertones in political party communications (Cambria et al., 2013; Giachanou & Crestani, 2016). By leveraging sentiment analysis, researchers can quantify and interpret political sentiments, thereby offering insights into party strategies and public reactions. In Spain's unique political landscape, where new and traditional parties like Podemos, PSOE, PP, Ciudadanos, and Vox engage vigorously on social media, analyzing sentiment can reveal the underlying strategies each employs. Recent advancements in pre-trained models tailored for the Spanish language, such as BETO and RoBERTuito, offer refined accuracy in detecting nuanced sentiments within Spanish tweets (Pérez et al., 2021).

South Africa and the Democratic Republic of Congo (DRC) present a complex linguistic landscape with languages such as Zulu, Sepedi, Afrikaans, French, English, and Tshiluba (Ciluba), which creates unique challenges for AI-driven translation and sentiment analysis systems due to a lack of accurately labeled data. This study seeks to address these challenges by developing a multilingual lexicon designed for French and Tshiluba, now expanded to include translations in English, Afrikaans, Sepedi, and Zulu. The lexicon enhances cultural relevance in sentiment classification by integrating language-specific sentiment scores. A comprehensive testing corpus is created to support translation and sentiment analysis tasks, with machine learning models such as Random Forest, Support Vector Machine (SVM), Decision Trees, and Gaussian Naive Bayes (GNB) trained to predict sentiment across low resource languages (LRLs). Among them, the Random Forest model performed particularly well, capturing sentiment polarity and handling language-specific nuances effectively. Furthermore, Bidirectional Encoder Representations from Transformers (BERT), a Large Language Model (LLM), is applied to predict context-based sentiment with high accuracy, achieving 99% accuracy and 98% precision, outperforming other models. The BERT predictions were clarified using Explainable AI (XAI), improving transparency and fostering confidence in sentiment classification. Overall, findings demonstrate that the proposed lexicon and machine learning models significantly enhance translation and sentiment analysis for LRLs in South Africa and the DRC, laying a foundation for future AI models that support underrepresented languages, with applications across education, governance, and business in multilingual contexts.

Multimodal Sentiment Analysis (MSA) is an important research area that aims to understand and recognize human sentiment through multiple modalities. The complementary information provided by multimodal fusion promotes better sentiment analysis compared to utilizing only a single modality. Nevertheless, in real-world applications, many unavoidable factors may lead to situations of uncertain modality missing, thus hindering the effectiveness of multimodal modeling and degrading the model's performance. To this end, we propose a Hierarchical Representation Learning Framework (HRLF) for the MSA task under uncertain missing modalities. Specifically, we propose a fine-grained representation factorization module that sufficiently extracts valuable sentiment information by factorizing modality into sentiment-relevant and modality-specific representations through crossmodal translation and sentiment semantic reconstruction. Moreover, a hierarchical mutual information maximization mechanism is introduced to incrementally maximize the mutual information between multi-scale representations to align and reconstruct the high-level semantics in the representations. Ultimately, we propose a hierarchical adversarial learning mechanism that further aligns and adapts the latent distribution of sentiment-relevant representations to produce robust joint multimodal representations. Comprehensive experiments on three datasets demonstrate that HRLF significantly improves MSA performance under uncertain modality missing cases.

The field of Multimodal Sentiment Analysis (MSA) has recently witnessed an emerging direction seeking to tackle the issue of data incompleteness. Recognizing that the language modality typically contains dense sentiment information, we consider it as the dominant modality and present an innovative Language-dominated Noise-resistant Learning Network (LNLN) to achieve robust MSA. The proposed LNLN features a dominant modality correction (DMC) module and dominant modality based multimodal learning (DMML) module, which enhances the model's robustness across various noise scenarios by ensuring the quality of dominant modality representations. Aside from the methodical design, we perform comprehensive experiments under random data missing scenarios, utilizing diverse and meaningful settings on several popular datasets (\textit{e.g.,} MOSI, MOSEI, and SIMS), providing additional uniformity, transparency, and fairness compared to existing evaluations in the literature. Empirically, LNLN consistently outperforms existing baselines, demonstrating superior performance across these challenging and extensive evaluation metrics.

Work on shallow discourse parsing in English has focused on the Wall Street Journal corpus, the only large-scale dataset for the language in the PDTB framework. However, the data is not openly available, is restricted to the news domain, and is by now 35 years old. In this paper, we present and evaluate a new open-access, multi-genre benchmark for PDTB-style shallow discourse parsing, based on the existing UD English GUM corpus, for which discourse relation annotations in other frameworks already exist. In a series of experiments on cross-domain relation classification, we show that while our dataset is compatible with PDTB, substantial out-of-domain degradation is observed, which can be alleviated by joint training on both datasets.

Probabilistic topic models are a powerful tool for extracting latent themes from large text datasets. In many text datasets, we also observe per-document covariates (e.g., source, style, political affiliation) that act as environments that modulate a "global" (environment-agnostic) topic representation. Accurately learning these representations is important for prediction on new documents in unseen environments and for estimating the causal effect of topics on real-world outcomes. To this end, we introduce the Multi-environment Topic Model (MTM), an unsupervised probabilistic model that separates global and environment-specific terms. Through experimentation on various political content, from ads to tweets and speeches, we show that the MTM produces interpretable global topics with distinct environment-specific words. On multi-environment data, the MTM outperforms strong baselines in and out-of-distribution. It also enables the discovery of accurate causal effects.