Genie: A Generator of Natural Language Semantic Parsers for Virtual Assistant Commands

To understand diverse natural language commands, virtual assistants today are trained with numerous labor-intensive, manually annotated sentences. This paper presents a methodology and the Genie toolkit that can handle new compound commands with significantly less manual effort. We advocate formalizing the capability of virtual assistants with a Virtual Assistant Programming Language (VAPL) and using a neural semantic parser to translate natural language into VAPL code. Genie needs only a small realistic set of input sentences for validating the neural model. Developers write templates to synthesize data; Genie uses crowdsourced paraphrases and data augmentation, along with the synthesized data, to train a semantic parser. We also propose design principles that make VAPL languages amenable to natural language translation. We apply these principles to revise ThingTalk, the language used by the Almond virtual assistant. We use Genie to build the first semantic parser that can support compound virtual assistants commands with unquoted free-form parameters. Genie achieves a 62% accuracy on realistic user inputs. We demonstrate Genie's generality by showing a 19% and 31% improvement over the previous state of the art on a music skill, aggregate functions, and access control.Comment: To appear in PLDI 201

Reinventing 'The Invention of Tradition'? Indigenous Pasts and the Roman Present

Thirty years ago Eric Hobsbawm and Terence Ranger introduced The invention of tradition as a concept to explain the creation and rise of certain traditions in times of profound cultural change. Taking stock of current theoretical understandings and focusing on the Roman world, this volume explores the concept of 'inventing traditions' as a means to understand processes of continuity, change and cultural innovation. The notion has been highly influential among studies concerned with the Greek and Roman eastern Mediterranean. Elsewhere in the Roman world and traditions other than Greek, however, have been neglected. This volume aims to evaluate critically the usefulness of the idea of 'inventing traditions' for the successor culture that was Rome. It focuses on the western part of the Roman Empire, which has been virtually ignored by such studies, and on non-Greek traditions. Why, in the Roman present, were some (indigenous) traditions forgotten while others invented or maintained? Using the past for reasons of legitimation in a highly volatile present is a cultural strategy that (also) characterises our present-day, globalized world. Can 'inventing traditions' be regarded as a common human characteristic occurring throughout world history

SpatialNLI: A Spatial Domain Natural Language Interface to Databases Using Spatial Comprehension

A natural language interface (NLI) to databases is an interface that translates a natural language question to a structured query that is executable by database management systems (DBMS). However, an NLI that is trained in the general domain is hard to apply in the spatial domain due to the idiosyncrasy and expressiveness of the spatial questions. Inspired by the machine comprehension model, we propose a spatial comprehension model that is able to recognize the meaning of spatial entities based on the semantics of the context. The spatial semantics learned from the spatial comprehension model is then injected to the natural language question to ease the burden of capturing the spatial-specific semantics. With our spatial comprehension model and information injection, our NLI for the spatial domain, named SpatialNLI, is able to capture the semantic structure of the question and translate it to the corresponding syntax of an executable query accurately. We also experimentally ascertain that SpatialNLI outperforms state-of-the-art methods.Comment: 10 page

Determinants of Depressive Symptoms at 1 Year Following ICU Discharge in Survivors of $ 7 Days of Mechanical Ventilation : Results From the RECOVER Program, a Secondary Analysis of a Prospective Multicenter Cohort Study

Abstract : Background: Moderate to severe depressive symptoms occur in up to one-third of patients at 1 year following ICU discharge, negatively affecting patient outcomes. This study evaluated patient and caregiver factors associated with the development of these symptoms. Methods: This study used the Rehabilitation and Recovery in Patients after Critical Illness and Their Family Caregivers (RECOVER) Program (Phase 1) cohort of 391 patients from 10 medical/surgical university-affiliated ICUs across Canada. We determined the association between patient depressive symptoms (captured by using the Beck Depression Inventory II [BDI-II]), patient characteristics (age, sex, socioeconomic status, Charlson score, and ICU length of stay [LOS]), functional independence measure (FIM) motor subscale score, and caregiver characteristics (Caregiver Assistance Scale and Center for Epidemiologic Studies-Depression Scale) by using linear mixed models at time points 3, 6, and 12 months. Results: BDI-II data were available for 246 patients. Median age at ICU admission was 56 years (interquartile range, 45-65 years), 143 (58%) were male, and median ICU LOS was 19 days (interquartile range, 13-32 days). During the 12-month follow-up, 67 of 246 (27.2%) patients had a BDI-II score ≥ 20, indicating moderate to severe depressive symptoms. Mixed models showed worse depressive symptoms in patients with lower FIM motor subscale scores (1.1 BDI-II points per 10 FIM points), lower income status (by 3.7 BDI-II points; P = .007), and incomplete secondary education (by 3.8 BDI-II points; P = .009); a curvilinear relation with age (P = .001) was also reported, with highest BDI-II at ages 45 to 50 years. No associations were found between patient BDI-II and comorbidities (P = .92), sex (P = .25), ICU LOS (P = .51), or caregiver variables (Caregiver Assistance Scale [P = .28] and Center for Epidemiologic Studies Depression Scale [P = .74]). Conclusions: Increased functional dependence, lower income, and lower education are associated with increased severity of post-ICU depressive symptoms, whereas age has a curvilinear relation with symptom severity. Knowledge of risk factors may inform surveillance and targeted mental health follow-up. Early mobilization and rehabilitation aiming to improve function may serve to modify mood disorders

Learning an Executable Neural Semantic Parser

This paper describes a neural semantic parser that maps natural language utterances onto logical forms which can be executed against a task-specific environment, such as a knowledge base or a database, to produce a response. The parser generates tree-structured logical forms with a transition-based approach which combines a generic tree-generation algorithm with domain-general operations defined by the logical language. The generation process is modeled by structured recurrent neural networks, which provide a rich encoding of the sentential context and generation history for making predictions. To tackle mismatches between natural language and logical form tokens, various attention mechanisms are explored. Finally, we consider different training settings for the neural semantic parser, including a fully supervised training where annotated logical forms are given, weakly-supervised training where denotations are provided, and distant supervision where only unlabeled sentences and a knowledge base are available. Experiments across a wide range of datasets demonstrate the effectiveness of our parser.Comment: In Journal of Computational Linguistic

Learning Search-Control Heuristics for Logic Programs: Applications to Speedup Learning and Language Acquisition

This paper presents a general framework, learning search-control heuristics for logic programs, which can be used to improve both the efficiency and accuracy of knowledgebased systems expressed as definite-clause logic programs. The approach combines techniques of explanation-based learning and recent advances in inductive logic programming to learn clause-selection heuristics that guide program execution. Two specific applications of this framework are detailed: dynamic optimization of Prolog programs (improving efficiency) and natural language acquisition (improving accuracy). In the area of program optimization, a prototype system, Dolphin is able to transform some intractable specifications into polynomial-time algorithms, and outperforms competing approaches in several benchmark speedup domains. A prototype language acquisition system, Chill is also described. It is capable of automatically acquiring semantic grammars, which uniformly incorprate syntactic and semantic constraints ..

Learning toParse Database Queries Using Inductive Logic Programming

This paper presents recent work using the Chill parser acquisition system to automate the construction of a natural-language interface for database queries. Chill treats parser acquisition as the learning of search-control rules within a logic program representing a shift-reduce parser and uses techniques from Inductive Logic Programming to learn relational control knowledge. Starting with a general framework for constructing a suitable logical form, Chill is able to train on a corpus comprising sentences paired with database queries and induce parsers that map subsequent sentences directly into executable queries. Experimental results with a complete database-query application for U.S. geography show that Chill is able to learn parsers that outperform a preexisting, hand-crafted counterpart. These results demonstrate the ability of a corpus-based system to produce more than purely syntactic representations. They also provide direct evidence of the utility of an empirical approach atthe level of a complete natural language application