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1. SpartQA: A Textual Question Answering Benchmark for Spatial Reasoning

   Roshanak Mirzaee, Hossein Rajaby (April 2021, The 2021 Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL-2021))

   null (Ed.)

   This paper proposes a question-answering (QA) benchmark for spatial reasoning on nat- ural language text which contains more real- istic spatial phenomena not covered by prior work and is challenging for state-of-the-art language models (LM). We propose a distant supervision method to improve on this task. Specifically, we design grammar and reason- ing rules to automatically generate a spatial de- scription of visual scenes and corresponding QA pairs. Experiments show that further pre- training LMs on these automatically generated data significantly improves LMs’ capability on spatial understanding, which in turn helps to better solve two external datasets, bAbI, and boolQ. We hope that this work can foster inves- tigations into more sophisticated models for spatial reasoning over text. 

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2. Teaching Probabilistic Logical Reasoning to Transformers

   Nafar, Aliakbar; Venable, K Brent; Kordjamshidi, Parisa (March 2024, Association for Computational Linguistics)

   Abstract In this paper, we evaluate the capability of transformer-based language models in making inferences over uncertain text that includes uncertain rules of reasoning. We cover both Pre-trained Language Models (PLMs) and generative Large Language Models (LLMs). Our evaluation results show that both generations of language models struggle with reasoning over uncertain text. We propose a novel end-to-end fine-tuning approach, Probabilistic Constraint Training (PCT), that utilizes probabilistic logical rules as constraints in the fine-tuning phase without relying on these rules in the inference stage. To assess the effectiveness of PCT, we utilize the related corpora and, additionally, create a new and more challenging benchmark that, unlike the previous ones, uses instance-specific rules. Our study demonstrates that PCT improves the transformer-based language model’s intrinsic reasoning and makes their probabilistic logical reasoning process more explicit and explainable. Furthermore, PCT equips these models to effectively handle novel situations, including higher reasoning depth, new domains, and complex probabilistic structures. 

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3. A General Context Learning and Reasoning Framework for Object Detection in Urban Scenes [A General Context Learning and Reasoning Framework for Object Detection in Urban Scenes]

   https://doi.org/10.5220/0011637600003417  

   Wang, Xuan; Tang, Hao; Zhu, Zhigang (January 2023, Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023))

   Contextual information has been widely used in many computer vision tasks. However, existing approaches design specific contextual information mechanisms for different tasks. In this work, we propose a general context learning and reasoning framework for object detection tasks with three components: local contextual labeling, contextual graph generation and spatial contextual reasoning. With simple user defined parameters, local contextual labeling automatically enlarge the small object labels to include more local contextual information. A Graph Convolutional Network learns over the generated contextual graph to build a semantic space. A general spatial relation is used in spatial contextual reasoning to optimize the detection results. All three components can be easily added and removed from a standard object detector. In addition, our approach also automates the training process to find the optimal combinations of user defined parameters. The general framework can be easily adapted to different tasks. In this paper we compare our framework with a previous multistage context learning framework specifically designed for storefront accessibility detection and a state of the art detector for pedestrian detection. Experimental results on two urban scene datasets demonstrate that our proposed general framework can achieve same performance as the specifically designed multistage framework on storefront accessibility detection, and with improved performance on pedestrian detection over the state of art detector. 

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4. Dynamic prompt learning via policy gradient for semi-structured mathematical reasoning

   Lu, P.; Qiu, L.; Chang, K. W.; Wu, Y. N.; Zhu, S. C.; Rajpurohit, T.; Clark, K.; Kalyan, A. (May 2023, International Conference on Learning Representations (ICLR 2023))

   Mathematical reasoning, a core ability of human intelligence, presents unique challenges for machines in abstract thinking and logical reasoning. Recent large pre-trained language models such as GPT-3 have achieved remarkable progress on mathematical reasoning tasks written in text form, such as math word problems (MWP). However, it is unknown if the models can handle more complex problems that involve math reasoning over heterogeneous information, such as tabular data. To fill the gap, we present Tabular Math Word Problems (TABMWP), a new dataset containing 38,431 open-domain grade-level problems that require mathematical reasoning on both textual and tabular data. Each question in TABMWP is aligned with a tabular context, which is presented as an image, semi-structured text, and a structured table. There are two types of questions: free-text and multi-choice, and each problem is annotated with gold solutions to reveal the multi-step reasoning process. We evaluate different pre-trained models on TABMWP, including the GPT-3 model in a few-shot setting. As earlier studies suggest, since few-shot GPT-3 relies on the selection of in-context examples, its performance is unstable and can degrade to near chance. The unstable issue is more severe when handling complex problems like TABMWP. To mitigate this, we further propose a novel approach, PROMPTPG, which utilizes policy gradient to learn to select in-context examples from a small amount of training data and then constructs the corresponding prompt for the test example. Experimental results show that our method outperforms the best baseline by 5.31% on the accuracy metric and reduces the prediction variance significantly compared to random selection, which verifies its effectiveness in selecting in-context examples. 

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5. GeoLM: Empowering Language Models for Geospatially Grounded Language Understanding

   https://doi.org/10.18653/v1/2023.emnlp-main.317  

   Li, Zekun; Zhou, Wenxuan; Chiang, Yao-Yi; Chen, Muhao (January 2023, Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing)

   Humans subconsciously engage in geospatial reasoning when reading articles. We recognize place names and their spatial relations in text and mentally associate them with their physical locations on Earth. Although pretrained language models can mimic this cognitive process using linguistic context, they do not utilize valuable geospatial information in large, widely available geographical databases, e.g., OpenStreetMap. This paper introduces GeoLM, a geospatially grounded language model that enhances the understanding of geo-entities in natural language. GeoLM leverages geo-entity mentions as anchors to connect linguistic information in text corpora with geospatial information extracted from geographical databases. GeoLM connects the two types of context through contrastive learning and masked language modeling. It also incorporates a spatial coordinate embedding mechanism to encode distance and direction relations to capture geospatial context. In the experiment, we demonstrate that GeoLM exhibits promising capabilities in supporting toponym recognition, toponym linking, relation extraction, and geo-entity typing, which bridge the gap between natural language processing and geospatial sciences. The code is publicly available at https://github.com/knowledge-computing/geolm. 

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