Quick and (not so) Dirty: Unsupervised Selection of Justification Sentences for Multi-hop Question Answering

We propose an unsupervised strategy for the selection of justification sentences for multi-hop question answering (QA) that (a) maximizes the relevance of the selected sentences, (b) minimizes the overlap between the selected facts, and (c) maximizes the coverage of both question and answer. This unsupervised sentence selection method can be coupled with any supervised QA approach. We show that the sentences selected by our method improve the performance of a state-of-the-art supervised QA model on two multi-hop QA datasets: AI2's Reasoning Challenge (ARC) and Multi-Sentence Reading Comprehension (MultiRC). We obtain new state-of-the-art performance on both datasets among approaches that do not use external resources for training the QA system: 56.82% F1 on ARC (41.24% on Challenge and 64.49% on Easy) and 26.1% EM0 on MultiRC. Our justification sentences have higher quality than the justifications selected by a strong information retrieval baseline, e.g., by 5.4% F1 in MultiRC. We also show that our unsupervised selection of justification sentences is more stable across domains than a state-of-the-art supervised sentence selection method.Comment: Published at EMNLP-IJCNLP 2019 as long conference paper. Corrected the name reference for Speer et.al, 201

Unsupervised Alignment-based Iterative Evidence Retrieval for Multi-hop Question Answering

Evidence retrieval is a critical stage of question answering (QA), necessary not only to improve performance, but also to explain the decisions of the corresponding QA method. We introduce a simple, fast, and unsupervised iterative evidence retrieval method, which relies on three ideas: (a) an unsupervised alignment approach to soft-align questions and answers with justification sentences using only GloVe embeddings, (b) an iterative process that reformulates queries focusing on terms that are not covered by existing justifications, which (c) a stopping criterion that terminates retrieval when the terms in the given question and candidate answers are covered by the retrieved justifications. Despite its simplicity, our approach outperforms all the previous methods (including supervised methods) on the evidence selection task on two datasets: MultiRC and QASC. When these evidence sentences are fed into a RoBERTa answer classification component, we achieve state-of-the-art QA performance on these two datasets.Comment: Accepted at ACL 2020 as a long conference pape

A Test of the Transition Analysis Method for Estimation of Age-at-Death in Adult Human Skeletal Remains

Physical anthropologists and bioarchaeologists often seek to generate biological profiles of individuals represented by skeletal remains. One particularly informative component of the biological profile is skeletal age-at-death. Age-at-death estimation is vital to numerous contexts in both paleodemography and forensic anthropology. Throughout the history of the discipline, numerous authors have published methods for adult age-at-death estimation. These methods have proved invaluable, but they are not free from error. As a result, workers have continually worked to improve the methodological toolkit for estimating age-at-death. In June of 1999, researchers gathered in Rostock, Germany for the sole purpose of evaluating and testing age-at-death estimation methods. The hallmark of this symposium was a theoretical framework known as the Rostock Manifesto published in volume edited by Hoppa and Vaupel (2002a) entitled Paleodemography: age distributions/rom skeletal samples. Included in this work was a new age-at-death estimation method called transition analysis published by Boldsen and colleagues. Transition analysis utilizes traits of the pubic symphysis, auricular surface, and cranial sutures to produce likelihood age-at-death estimates. In their publication, Boldsen et al. (2002) report a remarkable correlation between estimated age and real age in addition to asserting that this method adequately ages individuals in the 5O+ years category. This purpose ofthis research was to perform a validation study of the transition analysis method by utilizing 225 skeletons from the William M. Bass Donated Skeletal Collection curated by the Forensic Anthropology Center at the University of Tennessee. Data were collected in the manner of Bolds en et al. (2002) and used to generate age-at- death estimates. These results were then statistically compared to known ages from the Bass Collection. Results from the study were not as favorable as those published by Boldsen and colleagues. Correlation coefficients were low and analyses of data using the forward continuation ratio, ordinal cumulative pro bit, and unrestrictive cumulative probit models suggest such problems arise from a combination of the method\u27s statistical framework and its lack of applicability

Designing an Experimental Apparatus for Rotational Mixing in Stokes Flow

We report on the design, construction, and operation of a rotational mixing apparatus that magnetically rotates a thin metal rod interacting with tracers suspended in a high-viscosity fluid. The purpose of this apparatus is to achieve Stokes flow, defined as having a Reynolds number below 0.001, where viscous forces dominate over inertial forces in a fluid system. The apparatus, designed using 3D modeling software and constructed using additive manufacturing techniques, holds a rod at a fixed angle with a magnetic field and rotates the rod conically about a fixed point. Tracer trajectories within the fluid were tracked using a custom implementation of the Open-CV python library that analyzed video of the fluid mixing captured by a document camera. It is intended that this apparatus will be used in future research to investigate rotational mixing of viscous fluids, with applications to clinical research in medical science.https://digitalcommons.snc.edu/collaborative_presentations/1093/thumbnail.jp

A low-cost confocal microscope for the undergraduate lab

We demonstrate a simple and cost-efficient scanning confocal microscope setup for use in advanced instructional physics laboratories. The setup is constructed from readily available commercial products, and the implementation of a 3D-printed flexure stage allows for further cost reduction and pedagogical opportunity. Experiments exploring the thickness of a microscope slide and the surface of solid objects with height variation are presented as foundational components of undergraduate laboratory projects, and demonstrate the capabilities of a confocal microscope. This system allows observation of key components of a confocal microscope, including depth perception and data acquisition via transverse scanning, making it an excellent pedagogical resource

Semi-Structured Chain-of-Thought: Integrating Multiple Sources of Knowledge for Improved Language Model Reasoning

An important open question pertaining to the use of large language models for knowledge-intensive tasks is how to effectively integrate knowledge from three sources: the model's parametric memory, external structured knowledge, and external unstructured knowledge. Most existing prompting methods either rely solely on one or two of these sources, or require repeatedly invoking large language models to generate similar or identical content. In this work, we overcome these limitations by introducing a novel semi-structured prompting approach that seamlessly integrates the model's parametric memory with unstructured knowledge from text documents and structured knowledge from knowledge graphs. Experimental results on open-domain multi-hop question answering datasets demonstrate that our prompting method significantly surpasses existing techniques, even exceeding those which require fine-tuning