Applying fact-based verification to online misinformation detection can be a complex task due to the challenges of varying input lengths and the lack of sufficient data to train fact-based verification models for misinformation detection. We collected and created two specific datasets consisting of news articles and a set of fact articles (fact pool) which are used to develop a novel online misinformation detection pipeline based on information retrieval and claim verification. In addition to the AveriTec dataset, we collected and published the Misbar 1 1 https://anonymous.4open.science/r/Misinformation-detection-using-automatic-fact-based-news-verification-65D3 . dataset, which consists of expert-annotated long news articles and verification articles. We also propose a novel eXplainable Misinformation Detection method with Fact Verification, called XMDFaVer, that detects online misinformation through several stages: text summarization to condense lengthy news articles into short claims, a question generation and answering model to clarify the claim with reference to the associated fact pool, and finally a classifier to assess the claim against the factual data and discern the authenticity of the news articles. XMDFaVer demonstrates strong and consistent performance across most test cases, outperforming standard and fact-based baselines. The results also show that using more fact articles improves detection accuracy, confirming the effectiveness and robustness of the approach.

Investigating Answer Validation Using Noise Identification and Classification in Goal-Oriented Dialogues

The utilization of artificial intelligence tools and methods in agriculture has increased over the last few years. However, farmers and agronomists are uncertain about trusting such tools and the findings of machine/deep learning model predictions. This work develops a novel eXplainable AI (XAI) technique for smart farming yield prediction that can more effectively explain prediction outcomes when compared with state-of-the-art current explainers, LIME and SHAP. We call the proposed model, which considers both attributes and time lag, the Duo Attention eXplainable Mechanism (DAXM). We have developed and tested the model with two separate farming data sets and the results of our experiments demonstrate the effectiveness of prediction features for a three-week window on both tomato and strawberry yield prediction. We show that the explanation of such features can be achieved more effectively through our proposed DAXM model while these explanations significantly differ at the 95% confidence level from those generated by LIME and SHAP. DAXM is also more aligned with expert opinion with a higher degree of agreement with expert-reported feature importance measures as compared with LIME and SHAP. The proposed XAI approach for smart farming yield prediction offers effective explanations that can enrich user adaption of the cutting-edge neural models in the domain.

Supervised Question Answering systems (QA systems) rely on domain-specific humanlabeled data for training.Unsupervised QA systems generate their own question-answer training pairs, typically using secondary knowledge sources to achieve this outcome.Our approach (called PIE-QG) uses Open Information Extraction (OpenIE) to generate synthetic training questions from paraphrased passages and uses the question-answer pairs as training data for a language model for a state-of-the-art QA system based on BERT.Triples in the form of are extracted from each passage, and questions are formed with subjects (or objects) and predicates while objects (or subjects) are considered as answers.Experimenting on five extractive QA datasets demonstrates that our technique achieves onpar performance with existing state-of-the-art QA systems with the benefit of being trained on an order of magnitude fewer documents and without any recourse to external reference data sources.

Summary With the development of internet technologies, social media and mobile devices, short texts have become an increasingly popular medium among users to communicate with friends, search information and review products. Measuring the similarity between short texts is a fundamental task due to its importance in many applications, such as text retrieval, topic discovery, and event detection. However, short texts generally comprise sparse, noisy, and ambiguous information. Hence, effectively measuring the distance between short texts is a challenging task. In this paper, we exploit the advantageous corpus‐wide word co‐occurrence information into document‐level feature enrichment to mitigate the challenges caused by the sparseness of short texts for distance measurement. We propose a novel context‐aware weighted Biterm method for short text Distance Measurement (BDM). In BDM, we extract biterms (ie, word pairs) from a short text corpus and exploit a biterm topic model to determine the global weights of biterms in the corpus. We then determine the local importance of a biterm in different contexts (ie, short texts) based on the corpus‐level biterm weight. The distance between two short texts is computed using the context‐aware weighted biterms. Experimental results on three real‐world datasets demonstrate better accuracy and effectiveness of the proposed BDM.

Abstract The impact of frame semantic enrichment of texts on the task of factoid question answering (QA) is studied in this paper. In particular, we consider different techniques for answer processing with frame semantics: the level of semantic class identification and role assignment to texts, and the fusion of frame semantic‐based answer‐processing approaches with other methods used in the Text REtrieval Conference (TREC). The impact of each of these aspects on the overall performance of a QA system is analyzed in this paper. The TREC 2004 and TREC 2006 factoid question sets were used for the experiments. These demonstrate that the exploitation of encapsulated frame semantics in FrameNet in a shallow semantic parsing process can enhance answer‐processing performance in factoid QA systems. This improvement is dependent on the level of semantic annotation, the frame semantic alignment method, and the method of fusing frame semantic‐based answer‐processing models with other existing models. A more comprehensively annotated environment with all different part‐of‐speech target predicates provides a higher chance of correct factoid answer retrieval where semantic alignment is based on both semantic classes and a relaxed set of semantic roles for answer span identification. Our experiments on fusion techniques of frame semantic‐based and entity‐based answer‐processing models show that merging answer lists with respect to their scores and redundancy by exploiting a fusion function leads to a more effective overall factoid QA system compared to the use of individual models.