Algebraizable Weak Logics

We extend the standard framework of abstract algebraic logic to the setting of logics which are not closed under uniform substitution. We introduce the notion of weak logics as consequence relations closed under limited forms of substitutions and we give a modified definition of algebraizability that preserves the uniqueness of the equivalent algebraic semantics of algebraizable logics. We provide several results for this novel framework, in particular a connection between the algebraizability of a weak logic and the standard algebraizability of its schematic fragment. We apply this framework to the context of logics defined over team semantics and we show that the classical version of inquisitive and dependence logic is algebraizable, while their intuitionistic versions are not

Fully Automated Fact Checking Using External Sources

Given the constantly growing proliferation of false claims online in recent years, there has been also a growing research interest in automatically distinguishing false rumors from factually true claims. Here, we propose a general-purpose framework for fully-automatic fact checking using external sources, tapping the potential of the entire Web as a knowledge source to confirm or reject a claim. Our framework uses a deep neural network with LSTM text encoding to combine semantic kernels with task-specific embeddings that encode a claim together with pieces of potentially-relevant text fragments from the Web, taking the source reliability into account. The evaluation results show good performance on two different tasks and datasets: (i) rumor detection and (ii) fact checking of the answers to a question in community question answering forums.Comment: RANLP-201

Gpachov at CheckThat! 2023: A Diverse Multi-Approach Ensemble for Subjectivity Detection in News Articles

The wide-spread use of social networks has given rise to subjective, misleading, and even false information on the Internet. Thus, subjectivity detection can play an important role in ensuring the objectiveness and the quality of a piece of information. This paper presents the solution built by the Gpachov team for the CLEF-2023 CheckThat! lab Task~2 on subjectivity detection. Three different research directions are explored. The first one is based on fine-tuning a sentence embeddings encoder model and dimensionality reduction. The second one explores a sample-efficient few-shot learning model. The third one evaluates fine-tuning a multilingual transformer on an altered dataset, using data from multiple languages. Finally, the three approaches are combined in a simple majority voting ensemble, resulting in 0.77 macro F1 on the test set and achieving 2nd place on the English subtask

Quantitative polynomial functors

Data types are the basic building blocks of modern type theories and programming languages. Having more powerful data types around can increase the proof-theoretic strength of the theory, i.e., allow more programs to be written, and can also make existing proofs/programs more convenient to write. Recent advances in type theories such as cubical type theory have also been accompanied by advances in data type theory, such as quotient and higher inductive types. In this paper, we explore what a corresponding notion of (non-higher, so far) inductive types for the also recently introduced type theory Quantitative Type Theory (QTT) might be. QTT combines dependent types and linear types, in the sense of linear logic. By using linearity to track variable (and hence resource) usage of programs, QTT thus promises to enable formal reasoning about both functional and non-functional correctness of programs. A variant of QTT is implemented in the Idris 2 programming language, and we hope that our work can be used as a foundational justification for the implementation of data types there. Conversely, we have used Idris 2 to mechanically verify parts of our development

Fact Checking in Community Forums

Community Question Answering (cQA) forums are very popular nowadays, as they represent effective means for communities around particular topics to share information. Unfortunately, this information is not always factual. Thus, here we explore a new dimension in the context of cQA, which has been ignored so far: checking the veracity of answers to particular questions in cQA forums. As this is a new problem, we create a specialized dataset for it. We further propose a novel multi-faceted model, which captures information from the answer content (what is said and how), from the author profile (who says it), from the rest of the community forum (where it is said), and from external authoritative sources of information (external support). Evaluation results show a MAP value of 86.54, which is 21 points absolute above the baseline.Comment: AAAI-2018; Fact-Checking; Veracity; Community-Question Answering; Neural Networks; Distributed Representation

Algebraizable Weak Logics

We extend the standard framework of abstract algebraic logic to the setting of logics which are not closed under uniform substitution. We introduce the notion of weak logics as consequence relations closed under limited forms of substitutions and we give a modified definition of algebraizability that preserves the uniqueness of the equivalent algebraic semantics of algebraizable logics. We provide several results for this novel framework, in particular a connection between the algebraizability of a weak logic and the standard algebraizability of its schematic fragment. We apply this framework to the context of logics defined over team semantics and we show that the classical version of inquisitive and dependence logic is algebraizable, while their intuitionistic versions are not