Search for: All records

Can NLP assist in building formal models for verifying complex systems? We study this challenge in the context of parsing Network File System (NFS) specifications. We define a semantic-dependency problem over SpecIR, a representation language we introduce to model sentences appearing in NFS specification documents (RFCs) as semantic dependency structures, and present an annotated dataset of 1,198 sentences. We develop and evaluate semantic-dependency parsing systems for this problem. Evaluations show that even when using a state-of-the-art language model, there is significant room for improvement, with the best models achieving an F1 score of only 60.5 and 33.3 in the named-entity-recognition and dependency-link-prediction sub-tasks, respectively. We also release additional unlabeled data and other domain-related texts. Experiments show that these additional resources increase the F1 measure when used for simple domain-adaption and transfer-learning-based approaches, suggesting fruitful directions for further research. 

Abstract Use of cold‐formed steel (CFS) framing as load‐bearing system for gravity and lateral loads in buildings is becoming increasingly common in the North American construction industry, notably in high seismic regions where light‐weight construction is an attractive option. Buildings framed with closely spaced and repetitively placed CFS members can be detailed to develop lateral resistance using a variety of sheathing options. A relatively new option involves the use of steel sheet as sheathing. Steel sheet sheathed CFS shear walls offer high lateral strength and stiffness, and provide ductility courtesy of tension field action within the steel sheet. Despite their acceptance, gaps in the understanding of their behavior do exist, notably, behavior under dynamic loading, the contribution of nonstructural architectural finishes, and the behavior of wall‐lines: shear walls placed inline with gravity walls. To this end, a two‐phased experimental effort was undertaken to advance understanding of the lateral response of CFS‐framed wall‐line systems. Specifically, a suite of wall‐lines, detailed for mid‐rise buildings, were evaluated through simulated seismic loading imposed via shake table and quasi‐static cyclic tests. Damage to the wall‐lines was largely manifested in the form of damage to fastener connections used for attaching the sheathing and gypsum panels, and separation of exterior finish layer. This paper documents and quantifies the progressively incurred physical damage observed in the tested wall‐line assemblies, and correlates it with the evolution of dynamic characteristics and hysteretic energy dissipated across a spectrum of performance levels. 

This paper investigates the ability of artificial neural networks to judge the grammatical acceptability of a sentence, with the goal of testing their linguistic competence. We introduce the Corpus of Linguistic Acceptability (CoLA), a set of 10,657 English sentences labeled as grammatical or ungrammatical from published linguistics literature. As baselines, we train several recurrent neural network models on acceptability classification, and find that our models outperform unsupervised models by Lau et al. (2016) on CoLA. Error-analysis on specific grammatical phenomena reveals that both Lau et al.’s models and ours learn systematic generalizations like subject-verb-object order. However, all models we test perform far below human level on a wide range of grammatical constructions.