Large Language Models (LLMs) demonstrate significant capabilities in processing natural language data, promising efficient knowledge extraction from diverse textual sources to enhance situational awareness and support decision-making. However, concerns arise due to their susceptibility to hallucination, resulting in contextually inaccurate content. This work focuses on harnessing LLMs for automated Event Extraction, introducing a new method to address hallucination by decomposing the task into Event Detection and Event Argument Extraction. Moreover, the proposed method integrates dynamic schema-aware augmented retrieval examples into prompts tailored for each specific inquiry, thereby extending and adapting advanced prompting techniques such as Retrieval-Augmented Generation. Evaluation findings on prominent event extraction benchmarks and results from a synthesized benchmark illustrate the method’s superior performance compared to baseline approaches.

Incorporating auxiliary modalities such as images into event detection models has attracted increasing interest over the last few years. The complexity of natural language in describing situations has motivated researchers to leverage the related visual context to improve event detection performance. However, current approaches in this area suffer from data scarcity, where a large amount of labelled text-image pairs are required for model training. Furthermore, limited access to the visual context at inference time negatively impacts the performance of such models, which makes them practically ineffective in real-world scenarios. In this paper, we present a novel domain-adaptive visually-fused event detection approach that can be trained on a few labelled image-text paired data points. Specifically, we introduce a visual imaginator method that synthesises images from text in the absence of visual context. Moreover, the imaginator can be customised to a specific domain. In doing so, our model can leverage the capabilities of pre-trained vision-language models and can be trained in a few-shot setting. This also allows for effective inference where only single-modality data (i.e. text) is available. The experimental evaluation on the benchmark M2E2 dataset shows that our model outperforms existing state-of-the-art models, by up to 11 points.

Interpretable machine learning seeks to understand the reasoning process of complex black-box systems that are long notorious for lack of explainability. One flourishing approach is through counterfactual explanations, which provide suggestions on what a user can do to alter an outcome. Not only must a counterfactual example counter the original prediction from the black-box classifier but it should also satisfy various constraints for practical applications. Diversity is one of the critical constraints that however remains less discussed. While diverse counterfactuals are ideal, it is computationally challenging to simultaneously address some other constraints. Furthermore, there is a growing privacy concern over the released counterfactual data. To this end, we propose a feature-based learning framework that effectively handles the counterfactual constraints and contributes itself to the limited pool of private explanation models. We demonstrate the flexibility and effectiveness of our method in generating diverse counterfactuals of actionability and plausibility. Our counterfactual engine is more efficient than counterparts of the same capacity while yielding the lowest re-identification risks.

Knowledge graphs (KGs), as a structured form of knowledge representation, have been widely applied in the real world. Recently, few-shot knowledge graph completion (FKGC), which aims to predict missing facts for unseen relations with few-shot associated facts, has attracted increasing attention from practitioners and researchers. However, existing FKGC methods are based on metric learning or meta-learning, which often suffer from the out-of-distribution and overfitting problems. Meanwhile, they are incompetent at estimating uncertainties in predictions, which is critically important as model predictions could be very unreliable in few-shot settings. Furthermore, most of them cannot handle complex relations and ignore path information in KGs, which largely limits their performance. In this paper, we propose a normalizing flow-based neural process for few-shot knowledge graph completion (NP-FKGC). Specifically, we unify normalizing flows and neural processes to model a complex distribution of KG completion functions. This offers a novel way to predict facts for few-shot relations while estimating the uncertainty. Then, we propose a stochastic ManifoldE decoder to incorporate the neural process and handle complex relations in few-shot settings. To further improve performance, we introduce an attentive relation path-based graph neural network to capture path information in KGs. Extensive experiments on three public datasets demonstrate that our method significantly outperforms the existing FKGC methods and achieves state-of-the-art performance. Code is available at https://github.com/RManLuo/NP-FKGC.git.

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Link prediction on dynamic graphs is an important task in graph mining. Existing approaches based on dynamic graph neural networks (DGNNs) typically require a significant amount of historical data (interactions over time), which is not always available in practice. The missing links over time, which is a common phenomenon in graph data, further aggravates the issue and thus creates extremely sparse and dynamic graphs. To address this problem, we propose a novel method based on the neural process, called Graph Sequential Neural ODE Process (GSNOP). Specifically, GSNOP combines the advantage of the neural process and neural ordinary differential equation that models the link prediction on dynamic graphs as a dynamic-changing stochastic process. By defining a distribution over functions, GSNOP introduces the uncertainty into the predictions, making it generalize to more situations instead of overfitting to the sparse data. GSNOP is also agnostic to model structures that can be integrated with any DGNN to consider the chronological and geometrical information for link prediction. Extensive experiments on three dynamic graph datasets show that GSNOP can significantly improve the performance of existing DGNNs and outperform other neural process variants.

Most of the existing news-based market prediction techniques disregard conceptual and emotional relations in the news stream. In this work, we consider the conceptual relationship between news documents using contextualized latent concept modeling as well as leveraging news sentiment and technical indicators. We present our approach as an open-source RESTFul API. We build a corpus of financial news related to currency pairs in the Foreign Exchange and Cryptocurrencies markets. Next, we apply BERT-based embedding to generate word vectors, cluster the vectors to create latent economic concepts, and propose a document representation based on the distribution of words on these concepts as well as news sentiment. We use a recurrent convolutional neural network to jointly use BERT-based text representation and technical indicators embedding for market time series prediction. We further augment our model with technical indicators using another recurrent layer. The experimental results show the superiority of our method compared to the baselines. Our MarketNews dataset, news crawler, and MarketPredict APIs are available for public use.

Obtaining training data for multi-document Summarization (MDS) is time consuming and resource-intensive, so recent neural models can only be trained for limited domains. In this paper, we propose SummPip: an unsupervised method for multi-document summarization, in which we convert the original documents to a sentence graph, taking both linguistic and deep representation into account, then apply spectral clustering to obtain multiple clusters of sentences, and finally compress each cluster to generate the final summary. Experiments on Multi-News and DUC-2004 datasets show that our method is competitive to previous unsupervised methods and is even comparable to the neural supervised approaches. In addition, human evaluation shows our system produces consistent and complete summaries compared to human written ones.

Automatic Cognate Detection (ACD) is a challenging task which has been utilized to help NLP applications like Machine Translation, Information Retrieval and Computational Phylogenetics.Unidentified cognate pairs can pose a challenge to these applications and result in a degradation of performance.In this paper, we detect cognate word pairs among ten Indian languages with Hindi and use deep learning methodologies to predict whether a word pair is cognate or not.We identify IndoWordnet as a potential resource to detect cognate word pairs based on orthographic similarity-based methods and train neural network models using the data obtained from it.We identify parallel corpora as another potential resource and perform the same experiments for them.We also validate the contribution of Wordnets through further experimentation and report improved performance of up to 26%.We discuss the nuances of cognate detection among closely related Indian languages and release the lists of detected cognates as a dataset.We also observe the behaviour of, to an extent, unrelated Indian language pairs and release the lists of detected cognates among them as well.

Nearest neighbour search is a core process in many data mining algorithms. Finding reliable closest matches of a query in a high dimensional space is still a challenging task. This is because the effectiveness of many dissimilarity measures, that are based on a geometric model, such as lp-norm, decreases as the number of dimensions increases. In this paper, we examine how the data distribution can be exploited to measure dissimilarity between two instances and propose a new data dependent dissimilarity measure called 'mp-dissimilarity'. Rather than relying on geometric distance, it measures the dissimilarity between two instances in each dimension as a probability mass in a region that encloses the two instances. It deems the two instances in a sparse region to be more similar than two instances in a dense region, though these two pairs of instances have the same geometric distance. Our empirical results show that the proposed dissimilarity measure indeed provides a reliable nearest neighbour search in high dimensional spaces, particularly in sparse data. Mp-dissimilarity produced better task specific performance than lp-norm and cosine distance in classification and information retrieval tasks.

A social rating network (SRN) is a social network in which edges represent social relationships and users (nodes) express ratings on some of the given items. Such networks play an increasingly important role in reviewing websites such as Epinions.com or online sharing websites like Flickr.com. In this paper, we first observe and analyze the temporal behavior of users in a social rating network, who express ratings and create social relations. Then, we model the temporal dynamics of an SRN based on our observations, using the bidirectional effects of ratings and social relations. While existing models for other types of social networks have captured some of the effects, our model is the first one to represent all four effects, i.e. social relations-on-ratings (social influence), social relations-on-social relations (transitivity), ratings-on-social relations (selection), and ratings-on-ratings (correlational influence). Existing works consider these effects as static and constant throughout the evolution of an SRN, however our observations reveal that these effects are actually dynamic. We propose a probabilistic generative model for SRNs, which models the strength and dynamics of each effect throughout the network evolution. This model can serve for the prediction of future links, ratings or community structures. Due to the sensitive nature of SRNs, another motivation for our work is the generation of synthetic SRN data sets for research purposes. Our experimental studies on two real life datasets (Epinions and Flickr) demonstrate that the proposed model produces social rating networks that agree with real world data on a comprehensive set of evaluation criteria.