Switching Between Discrete and Continuous Models To Predict Genetic Activity

Molecular biologists use a variety of models when they predict the behavior of genetic systems. A discrete model of the behavior of individual macromolecular elements forms the foundation for their theory of each system. Yet a continuous model of the aggregate properties of the system is necessary for many predictive tasks. I propose to build a computer program, called PEPTIDE, which can predict the behavior of moderately complex genetics systems by performing qualitative simulation on the discrete model, generating a continuous model from the discrete model through aggregation, and applying limit analysis to the continuous model. PEPTIDE's initial knowledge of a specific system will be represented with a discrete model which distinguishes between macromolecule structure and function and which uses five atomic processes as its functional primitives. Qualitative Process (QP) theory [Forbus 83] provides the representation for the continuous model. Whenever a system has multiple models of a domain, the decision of which model to use in a given time becomes a critically important issue. Knowledge of the relative significance of differing element concentrations and the behavior of process structure cycles will allow PEPTIDE to determine when to switch reasoning modes.MIT Artificial Intelligence Laborator

TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension

We present TriviaQA, a challenging reading comprehension dataset containing over 650K question-answer-evidence triples. TriviaQA includes 95K question-answer pairs authored by trivia enthusiasts and independently gathered evidence documents, six per question on average, that provide high quality distant supervision for answering the questions. We show that, in comparison to other recently introduced large-scale datasets, TriviaQA (1) has relatively complex, compositional questions, (2) has considerable syntactic and lexical variability between questions and corresponding answer-evidence sentences, and (3) requires more cross sentence reasoning to find answers. We also present two baseline algorithms: a feature-based classifier and a state-of-the-art neural network, that performs well on SQuAD reading comprehension. Neither approach comes close to human performance (23% and 40% vs. 80%), suggesting that TriviaQA is a challenging testbed that is worth significant future study. Data and code available at -- http://nlp.cs.washington.edu/triviaqa/Comment: Added references, fixed typos, minor baseline updat

An Interactive UI to Support Sensemaking over Collections of Parallel Texts

Scientists and science journalists, among others, often need to make sense of a large number of papers and how they compare with each other in scope, focus, findings, or any other important factors. However, with a large corpus of papers, it's cognitively demanding to pairwise compare and contrast them all with each other. Fully automating this review process would be infeasible, because it often requires domain-specific knowledge, as well as understanding what the context and motivations for the review are. While there are existing tools to help with the process of organizing and annotating papers for literature reviews, at the core they still rely on people to serially read through papers and manually make sense of relevant information. We present AVTALER, which combines peoples' unique skills, contextual awareness, and knowledge, together with the strength of automation. Given a set of comparable text excerpts from a paper corpus, it supports users in sensemaking and contrasting paper attributes by interactively aligning text excerpts in a table so that comparable details are presented in a shared column. AVTALER is based on a core alignment algorithm that makes use of modern NLP tools. Furthermore, AVTALER is a mixed-initiative system: users can interactively give the system constraints which are integrated into the alignment construction process.Comment: 13 pages, 12 figure

Pretrained Language Models for Sequential Sentence Classification

As a step toward better document-level understanding, we explore classification of a sequence of sentences into their corresponding categories, a task that requires understanding sentences in context of the document. Recent successful models for this task have used hierarchical models to contextualize sentence representations, and Conditional Random Fields (CRFs) to incorporate dependencies between subsequent labels. In this work, we show that pretrained language models, BERT (Devlin et al., 2018) in particular, can be used for this task to capture contextual dependencies without the need for hierarchical encoding nor a CRF. Specifically, we construct a joint sentence representation that allows BERT Transformer layers to directly utilize contextual information from all words in all sentences. Our approach achieves state-of-the-art results on four datasets, including a new dataset of structured scientific abstracts.Comment: EMNLP 201

Automatically Generating Personalized User Interfaces with SUPPLE

Today's computer–human interfaces are typically designed with the assumption that they are going to be used by an able-bodied person, who is using a typical set of input and output devices, who has typical perceptual and cognitive abilities, and who is sitting in a stable, warm environment. Any deviation from these assumptions may drastically hamper the person's effectiveness—not because of any inherent barrier to interaction, but because of a mismatch between the person's effective abilities and the assumptions underlying the interface design. We argue that automatic personalized interface generation is a feasible and scalable solution to this challenge. We present our Supple system, which can automatically generate interfaces adapted to a person's devices, tasks, preferences, and abilities. In this paper we formally define interface generation as an optimization problem and demonstrate that, despite a large solution space (of up to 1017 possible interfaces), the problem is computationally feasible. In fact, for a particular class of cost functions, Supple produces exact solutions in under a second for most cases, and in a little over a minute in the worst case encountered, thus enabling run-time generation of user interfaces. We further show how several different design criteria can be expressed in the cost function, enabling different kinds of personalization. We also demonstrate how this approach enables extensive user- and system-initiated run-time adaptations to the interfaces after they have been generated. Supple is not intended to replace human user interface designers—instead, it offers alternative user interfaces for those people whose devices, tasks, preferences, and abilities are not sufficiently addressed by the hand-crafted designs. Indeed, the results of our study show that, compared to manufacturers' defaults, interfaces automatically generated by Supple significantly improve speed, accuracy and satisfaction of people with motor impairments.Engineering and Applied Science