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Compound graphs are networks in which vertices can be grouped into larger subsets, with these subsets capable of further grouping, resulting in a nesting that can be many levels deep. In several applications, including biological workflows, chemical equations, and computational data flow analysis, these graphs often exhibit a tree-like nesting structure, where sibling clusters are disjoint. Common compound graph layouts prioritize the lowest level of the grouping, down to the individual ungrouped vertices, which can make the higher level grouped structures more difficult to discern, especially in deeply nested networks. Leveraging the additional structure of the tree-like nesting, we contribute an overview+detail layout for this class of compound graphs that preserves the saliency of the higher level network structure when groups are expanded to show internal nested structure. Our layout draws inner structures adjacent to their parents, using a modified tree layout to place substructures. We describe our algorithm and then present case studies demonstrating the layout's utility to a domain expert working on data flow analysis. Finally, we discuss network parameters and analysis situations in which our layout is well suited. 

Message from the Organizers Welcome to the second edition of the Workshop on Pattern-based Approaches to NLP in the Age of Deep Learning (Pan-DL)! Our workshop is being organized in a hybrid format on December 6, 2023, in conjunction with the 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP). In the past year, the natural language processing (NLP) field (and the world at large!) has been hit by the large language model (LLM) "tsunami." This happened for the right reasons: LLMs perform extremely well in a multitude of NLP tasks, often with minimal training and, perhaps for the first time, have made NLP technology extremely approachable to non-expert users. However, LLMs are not perfect: they are not really explainable, they are not pliable, i.e., they cannot be easily modified to correct any errors observed, and they are not efficient due to the overhead of decoding. In contrast, rule-based methods are more transparent to subject matter experts; they are amenable to having a human in the loop through intervention, manipulation and incorporation of domain knowledge; and further the resulting systems tend to be lightweight and fast. This workshop focuses on all aspects of rule-based approaches, including their application, representation, and interpretability, as well as their strengths and weaknesses relative to state-of-the-art machine learning approaches. Considering the large number of potential directions in this neuro-symbolic space, we emphasized inclusivity in our workshop. We received 19 submissions and accepted 10 for oral presentation. This resulted in an overall acceptance rate of 52%. Our workshop also includes 6 presentations of papers that were accepted in Findings of EMNLP. In addition to the oral presentations of the accepted papers, our workshop includes a keynote talk by Yunyao Li, who has made many important contributions to the field of symbolic approaches for natural language processing. Further, the workshop contains a panel that will discuss the merits and limitations of rules in the new LLM era. The panelists will be academics with expertise in both neural- and rulebased methods, industry experts that employ these methods for commercial products, and subject matter experts that have used rule-based methods for domain-specific applications. We thank Yunyao Li and the panelists for their important contribution to our workshop! Finally, we are thankful to the members of the program committee for their insightful reviews! We are confident that all submissions have benefited from their expert feedback. Their contribution was a key factor for accepting a diverse and high-quality list of papers, which we hope will make the first edition of the Pan-DL workshop a success, and will motivate many future editions. Pan-DL 2023 Organizers December 6, 2023 

This work introduces a natural language inference (NLI) dataset that focuses on the validity of statements in legal wills. This dataset is unique because: (a) each entailment decision requires three inputs: the statement from the will, the law, and the conditions that hold at the time of the testator’s death; and (b) the included texts are longer than the ones in current NLI datasets. We trained eight neural NLI models in this dataset. All the models achieve more than 80% macro F1 and accuracy, which indicates that neural approaches can handle this task reasonably well. However, group accuracy, a stricter evaluation measure that is calculated with a group of positive and negative examples generated from the same statement as a unit, is in mid 80s at best, which suggests that the models’ understanding of the task remains superficial. Further ablative analyses and explanation experiments indicate that all three text segments are used for prediction, but some decisions rely on semantically irrelevant tokens. This indicates that overfitting on these longer texts likely happens, and that additional research is required for this task to be solved. 

Message from the Organizers Welcome to the first edition of the Workshop on Pattern-based Approaches to NLP in the Age of Deep Learning (Pan-DL)! Our workshop is being organized online on October 17, 2022, in conjunction with the 29th International Conference on Computational Linguistics (COLING). We all know that deep-learning methods have dominated the field of natural language processing in the past decade. However, these approaches usually rely on the availability of high-quality and high- quantity data annotation. Furthermore, the learned models are difficult to interpret and incur substantial technical debt. As a result, these approaches tend to exclude users that lack the necessary machine learning background. In contrast, rule-based methods are easier to deploy and adapt; they support human examination of intermediate representations and reasoning steps; they are more transparent to subject- matter experts; they are amenable to having a human in the loop through intervention, manipulation and incorporation of domain knowledge; and further the resulting systems tend to be lightweight and fast. This workshop focuses on all aspects of rule-based approaches, including their application, representation, and interpretability, as well as their strengths and weaknesses relative to state-of-the-art machine learning approaches. Considering the large number of potential directions in this neuro-symbolic space, we emphasized inclusivity in our workshop. We received 13 papers and accepted 10 for oral presentation. This resulted in an overall acceptance rate of 77%. In addition of the oral presentations of the accepted papers, our workshop includes a keynote talk by Ellen Riloff, who has made crucial contributions to the field of natural language processing, many of which are at the intersection of rule- and neural-based methods. Further, the workshop contains a panel that will discuss the merits and limitations of rules in our neural era. The panelists will be academics with expertise in both neural- and rule-based methods, industry experts that employ these methods for commercial products, government officials in charge of AI funding, organizers of natural language processing evaluations, and subject matter experts that have used rule-based methods for domain-specific applications. We thank Ellen Riloff and the panelists for their important contribution to our workshop! Finally, we are thankful to the members of the program committee for their insightful reviews! We are confident that all submissions have benefited from their expert feedback. Their contribution was a key factor for accepting a diverse and high-quality list of papers, which we hope will make the first edition of the Pan-DL workshop a success, and will motivate many future editions. Pan-DL 2022 Organizers October 2022 

An important task in the machine reading of biochemical events expressed in biomedical texts is correctly reading the polarity, i.e., attributing whether the biochemical event is a promotion or an inhibition. Here we present a novel dataset for studying polarity attribution accuracy. We use this dataset to train and evaluate several deep learning models for polarity identification, and compare these to a linguistically-informed model. The best performing deep learning architecture achieves 0.968 average F1 performance in a five-fold cross-validation study, a considerable improvement over the linguistically informed model average F1 of 0.862. 

Recent efforts in bioinformatics have achieved tremendous progress in the machine reading of biomedical literature, and the assembly of the extracted biochemical interactions into large-scale models such as protein signaling pathways. However, batch machine reading of literature at today’s scale (PubMed alone indexes over 1 million papers per year) is unfeasible due to both cost and processing overhead. In this work, we introduce a focused reading approach to guide the machine reading of biomedical literature towards what literature should be read to answer a biomedical query as efficiently as possible. We introduce a family of algorithms for focused reading, including an intuitive, strong baseline, and a second approach which uses a reinforcement learning (RL) framework that learns when to explore (widen the search) or exploit (narrow it). We demonstrate that the RL approach is capable of answering more queries than the baseline, while being more efficient, i.e., reading fewer documents.