Large-language-model (LLM) agents excel at reactive dialogue but struggle with proactive, goal-driven interactions due to myopic decoding and costly planning. We introduce DialogXpert, which leverages a frozen LLM to propose a small, high-quality set of candidate actions per turn and employs a compact Q-network over fixed BERT embeddings trained via temporal-difference learning to select optimal moves within this reduced space. By tracking the user's emotions DialogXpert tailors each decision to advance the task while nurturing a genuine, empathetic connection. Across negotiation, emotional support, and tutoring benchmarks, DialogXpert drives conversations to under 3 turns with success rates exceeding 94% and, with a larger LLM prior, pushes success above 97% while markedly improving negotiation outcomes. This framework delivers real-time, strategic, and emotionally intelligent dialogue planning at scale.

Fanyu Wang, Xiaoxi Kang, Paul Burgess, Aashish Srivastava, Chetan Arora, Adnan Trakic, Lay-Ki Soon, Md Khalid Hossain, Lizhen Qu. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

Vy Vo, Lizhen Qu, Tao Feng, Yuncheng Hua, Xiaoxi Kang, Songhai Fan, Tim Dwyer, Lay-Ki Soon, Gholamreza Haffari. Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2025.

This paper presents our investigation of the potential of generative AI in crafting personalised humour, focusing on how user-specified keywords and demographic information can guide joke generation. We introduce a two-part system: (1) a personalised joke generator built using GPT-3 that tailors humour based on keywords and user demographics (age, gender, continent), and (2) an automated joke ranking pipeline powered by fine-tuned BERT models to filter and prioritise high-quality, humorous content, trained on Reddit-sourced joke data. To evaluate the effectiveness and reception of the generated jokes, we conducted a human-centered evaluation involving 32 participants with diverse personality profiles. Evaluators rated the jokes across four humour dimensions: Funniness, Offensiveness, Surprise, and Reality Representation. Our findings reveal notable variations in humour perception across personality types based on MBTI. For instance, Sentinels responded more positively to the generated jokes, while Analysts tended to rate them lower across most categories. Additionally, a strong correlation between perceived funniness and surprise highlights the role of unexpectedness in humour appreciation. These results underscore the importance of personality-aware humour generation and suggest that current AI models require further refinement to effectively cater to diverse audiences.

Face authentication systems have brought significant convenience and advanced developments, yet they have become unreliable due to their sensitivity to inconspicuous perturbations, such as adversarial attacks. Existing defenses often exhibit weaknesses when facing various attack algorithms and adaptive attacks or compromise accuracy for enhanced security. To address these challenges, we have developed a novel and highly efficient non-deep-learning-based image filter called the Iterative Window Mean Filter (IWMF) and proposed a new framework for adversarial purification, named IWMF-Diff, which integrates IWMF and denoising diffusion models. These methods can function as pre-processing modules to eliminate adversarial perturbations without necessitating further modifications or retraining of the target system. We demonstrate that our proposed methodologies fulfill four critical requirements: preserved accuracy, improved security, generalizability to various threats in different settings, and better resistance to adaptive attacks. This performance surpasses that of the state-of-the-art adversarial purification method, DiffPure. Our code is released athttps://github.com/azrealwang/iwmfdiff.

Standard English and Malaysian English exhibit notable differences, posing challenges for natural language processing (NLP) tasks on Malaysian English. An experiment using state-of-the-art Named Entity Recognition (NER) solutions in Malaysian English news articles highlights that they cannot handle morphosyntactic variations in Malaysian English. Unfortunately, most of the existing datasets are mainly based on Standard English, which is not sufficient to enhance NLP tasks in Malaysian English. To the best of our knowledge, there is no annotated dataset that can be used to improve the model. To address this issue, we have constructed a Malaysian English News (MEN) dataset, which contains 200 news articles that are manually annotated with entities and relations. We then fine-tuned the spaCy NER tool and validated that having a dataset tailor-made for Malaysian English could significantly improve the performance of NER in Malaysian English. This paper presents our efforts to acquire data, the annotation methodology, and a detailed analysis of the annotated dataset. To ensure the quality of the annotation, we have measured the Inter-Annotator Agreement (IAA), and any disagreements were resolved by a subject matter expert through adjudication. After a rigorous quality check, we have developed a dataset with 6,061 entities and 3,268 relation instances. Finally, we discuss spaCy fine-tuning setup and analysis of NER performance. This unique dataset will contribute significantly to the advancement of NLP research in Malaysian English, allowing researchers to accelerate their progress, particularly in NER and relation extraction.

Content Warning: this paper may contain content that is offensive or upsetting.

In this paper, we present CrudeOilNews, a corpus of English Crude Oil news for event extraction. It is the first of its kind for Commodity News and serves to contribute towards resource building for economic and financial text mining. This paper describes the data collection process, the annotation methodology, and the event typology used in producing the corpus. Firstly, a seed set of 175 news articles were manually annotated, of which a subset of 25 news was used as the adjudicated reference test set for inter-annotator and system evaluation. The inter-annotator agreement was generally substantial, and annotator performance was adequate, indicating that the annotation scheme produces consistent event annotations of high quality. Subsequently, the dataset is expanded through (1) data augmentation and (2) Human-in-the-loop active learning. The resulting corpus has 425 news articles with approximately 11k events annotated. As part of the active learning process, the corpus was used to train basic event extraction models for machine labeling; the resulting models also serve as a validation or as a pilot study demonstrating the use of the corpus in machine learning purposes. The annotated corpus is made available for academic research purpose at https://github.com/meisin/CrudeOilNews-Corpus

Entity matching (EM) is the process of linking records from different data sources. While extensive research has been done in various aspects of EM, many of these studies generally assume EM tasks as schema-specific, which attempt to match record pairs at attributes level. Unfortunately, in the real-world, tables that undergo EM may not have an aligned schema, and often, the schema or metadata of the table and attributes are not known beforehand.In view of this challenge, this paper presents an effective approach for schema-agnostic EM, where having schema-aligned tables is not compulsory. The proposed method stemmed from the idea of treating tuples in tables for EM similar to sentence pair classification problem in natural language processing (NLP). A pre-trained language model, BERT is adopted by fine-tuning it using labeled dataset. The proposed method was experimented using benchmark datasets and compared against two state-of-the-art approaches,namely DeepMatcher and Magellan. The experimental results show that our proposed solution outperforms by an average of 9% in F1 score. The performance is in fact consistent across different types of datasets, showing significant improvement of 29.6% for one of dirty datasets. These prove that our proposed solution is versatile for EM.

Dependability on XML has increased tremendously over the years. As such the need for efficient query processing technique is certainly important. Despite the fact that these techniques are able to process queries with various edge combinations, they still suffer from processing overheads by buffering large amount of intermediate results particularly for parent–child (P–C) edges. Therefore, in this paper, we propose architecture named ReLaQ, which comprises of two components, ReLab[Formula: see text] (node annotator) and QTwig (query processor) for efficient XML query processing. QTwig improves retrieval time by incorporating a pruning technique that avoids accessing irrelevant data during query processing. Experimental results indicated that ReLaQ superseded TwigStack for both path and twig queries using both regular- and skewed-structured datasets. In addition, this is also proven by means of correctness analysis of ReLaQ.

Achieving the effectiveness in relation to the relevance of query result is the most crucial part of XML keyword search. Developing an XML Keyword search approach which addresses the user search intention, keyword ambiguity problems and query/search result grading (ranking) problem is still challenging. In this paper, we propose a novel approach called XDMA for keyword search in XML databases that builds two indices to resolve these problems. Then, a keyword search technique based on two-level matching between two indices is presented. Further, by utilizing the logarithmic and probability functions, a terminology that defines the Mutual Score to find the desired T-typed node is put forward. We also introduce the similarity measure to retrieve the exact data through the selected T-typed node. In addition, grading for the query results having comparable relevance scores is employed. Finally, we demonstrate the effectiveness of our proposed approach, XDMA with a comprehensive experimental evaluation using the datasets of DBLP, WSU and eBay.

The field of ontology has received attention lately due to the increasing needs in conceptualizing the domain knowledge for resolving various jobs' demand. Numerous new techniques, tools and applications have then been developed for their suitability in managing knowledge. However, most works carried out focused on non-fiction domain and categorizing the concepts into component or cluster. Hence, the originality of the content flow is not preserved. This paper presents an automated ontology construction in fiction domain. The significance of the study lies in (1) designing a simple and easy algorithmic framework for automated ontology construction while preserving the originality of the content flow in an ontology, (2) identification of suitable threshold value in extracting true terms, and (3) process an unstructured fiction-based domain text into meaningful structure automatically.