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Abstract Reasoning - Crossref

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Last Updated: 10 November 2022

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Question Answering as Global Reasoning Over Semantic Abstractions

To answer multiple-choice questions, we suggest a novel approach for exploiting text's semantic structure. To solve these difficulties, we introduce the first system, which is to the best of our knowledge, that depends on a variety of semantic abstractions of the text, such as semantic role labelers, coreference resolvers, and dependency parsers. We translate question answering a question that has sparked specific global and local properties by presenting several abstractions as a family of graphs. This formulation generalizes several existing pre-defined QA procedures.

Source link: https://doi.org/10.1609/aaai.v32i1.11574


Counting Complexity for Reasoning in Abstract Argumentation

When asking for estimated numbers, we are interested in determining the number of extensions of a given argument framework framework, rather than multiple extensions that are identical when limited to the expected arguments count as only one proposed extension. When the problems are parameterized by the treewidth of the undirected argument graph, we obtain classical complexity results and parameterized complexity results. We take the exponential time hypothesis into account and calculate lower bounds of bounded treewidth algorithms for counting extensions and projected extension in this post.

Source link: https://doi.org/10.1609/aaai.v33i01.33012827


Learning Directional Sentence-Pair Embedding for Natural Language Reasoning (Student Abstract)

One of the main tasks of natural language analysis is enabling the models to be able of reasoning and inference over text. Despite deep learning platforms' successes on several cross-sentence inference benchmarks, recent research has found that they are leveraging spurious statistical cues rather than establishing deeper implied connections between two sentences.

Source link: https://doi.org/10.1609/aaai.v34i10.7184


Machine Number Sense: A Dataset of Visual Arithmetic Problems for Abstract and Relational Reasoning

The number sense crosses the introduction of symbolic terms and the proficiency in problem-solving, providing a comprehensive measure of mathematical thinking and intelligence. To endow such a vital cognitive ability to machine intelligence, we suggest a machine number Sense database, which is made up of graphical arithmetic problems automatically generated using a grammar scheme u2014And-Or Graph. These numerical arithmetic problems are represented in the form of geometric figures: Each problem has a set of geometric shapes as its context and embedded number symbols; each problem has a set of geometric shapes as its context and embedded number symbols. In this visual reasoning task, we benchmarked the MNS dataset using four common neural network models as baselines. Without context information, we show that a basic brute-force search algorithm could help solve some of the problems. Taking geometric context into account by an additional perception module would result in a dramatic performance rise with fewer search steps, according to a Crucially.

Source link: https://doi.org/10.1609/aaai.v34i02.5489

* Please keep in mind that all text is summarized by machine, we do not bear any responsibility, and you should always check original source before taking any actions

* Please keep in mind that all text is summarized by machine, we do not bear any responsibility, and you should always check original source before taking any actions