Implicit Discourse Relation Classification via Multi-Task Neural Networks

Without discourse connectives, classifying implicit discourse relations is a challenging task and a bottleneck for building a practical discourse parser. Previous research usually makes use of one kind of discourse framework such as PDTB or RST to improve the classification performance on discourse relations. Actually, under different discourse annotation frameworks, there exist multiple corpora which have internal connections. To exploit the combination of different discourse corpora, we design related discourse classification tasks specific to a corpus, and propose a novel Convolutional Neural Network embedded multi-task learning system to synthesize these tasks by learning both unique and shared representations for each task. The experimental results on the PDTB implicit discourse relation classification task demonstrate that our model achieves significant gains over baseline systems.Comment: This is the pre-print version of a paper accepted by AAAI-1

Merleau-Ponty on Embodied Subjectivity from the Perspective of Subject-Object Circularity

The phenomenological point of view of the body is usually appreciated for having introduced the notion of the ‘lived’ body. We cannot merely analyze and explain the body as one of the elements of the world of objects. We must also describe it, for example, as the center of our perspective on the world, the place where our sensing is ‘localized’, the agens which directly executes our intentions. However, in Husserl, the idea of the body as lived primarily complements his objectivism: the body (Leib) is an objective and mental reality, a ‘double unity’, as he writes. In contrast, Merleau-Ponty’s later considerations of the body in Phenomenology of Perception tend to the idea of a circular relationship between the objective and subjective dimensions of the body – between the objective and the lived. One of the means to overcome the idea of the body as a site of the correlation between two opposite and complementary realms is, for Merleau-Ponty, the philosophical interpretation of an early neurological notion of ‘body schema’. Body schema is neither an idea nor a physiological-physical fact, it is rather a practical diagram of our relationships with the world, an action-based norm in reference to which things make sense. In the recently published preparatory notes for his 1953 courses, Merleau-Ponty dedicates much effort to further developing the notion of body schema, and interprets fresh sources that he did not use in Phenomenology of Perception. Notably, he studies various possibilities of how this practical ‘diagram’ can be de-differentiated (pathology) or further refined (cognitive and cultural superstructures, symbolic systems), which shows the fundamentally dynamic unity of the body. This paper summarizes the basic elements of Merleau-Ponty’s 1953 renewed philosophical interpretation of the notion of body schema, while contrasting it to the more traditional understanding of the body in phenomenology and in recent philosophical texts dealing with body schema

Synthesizing Conjunctive Queries for Code Search

This paper presents Squid, a new conjunctive query synthesis algorithm for searching code with target patterns. Given positive and negative examples along with a natural language description, Squid analyzes the relations derived from the examples by a Datalog-based program analyzer and synthesizes a conjunctive query expressing the search intent. The synthesized query can be further used to search for desired grammatical constructs in the editor. To achieve high efficiency, we prune the huge search space by removing unnecessary relations and enumerating query candidates via refinement. We also introduce two quantitative metrics for query prioritization to select the queries from multiple candidates, yielding desired queries for code search. We have evaluated Squid on over thirty code search tasks. It is shown that Squid successfully synthesizes the conjunctive queries for all the tasks, taking only 2.56 seconds on average

Automated synthesis of data extraction and transformation programs

Due to the abundance of data in today’s data-rich world, end-users increasingly need to perform various data extraction and transformation tasks. While many of these tedious tasks can be performed in a programmatic way, most end-users lack the required programming expertise to automate them and end up spending their valuable time in manually performing various data- related tasks. The field of program synthesis aims to overcome this problem by automatically generating programs from informal specifications, such as input-output examples or natural language. This dissertation focuses on the design and implementation of new systems for automating important classes of data transformation and extraction tasks. It introduces solutions for automating data manipulation tasks on fully- structured data formats like relational tables, or on semi-structured formats such as XML and JSON documents. First, we describe a novel algorithm for synthesizing hierarchical data transformations from input-output examples. A key novelty of our approach is that it reduces the synthesis of tree transformations to the simpler problem of synthesizing transformations over the paths of the tree. We also describe a new and effective algorithm for learning path transformations that combines logical SMT-based reasoning with machine learning techniques based on decision trees. Next, we present a new methodology for learning programs that migrate tree-structured documents to relational table representations from input-output examples. Our approach achieves its goal by decomposing the synthesis task to two subproblems of (A) learning the column extraction logic, and (B) learning the row extraction logic. We propose a technique for learning column extraction programs using deterministic finite automata, and a new algorithm for predicate learning which combines integer linear programing and logic minimization. Finally, we address the problem of automating data extraction tasks from natural language. Specifically, we focus on data retrieval from relational databases and describe a novel approach for learning SQL queries from English descriptions. The method we describe is fully automatic and database-agnostic (i.e., does not require customization for each database). Our method combines semantic parsing techniques from the NLP community with novel programming languages ideas involving probabilistic type inhabitation and automated sketch repair.Computer Science

Human Nature And Cyclic Character Of Economic Crises

Crisis clearly distinguishes itself from the large mass of economic phenomena through its provocative force, which fuels theoretical discourse. The more harmful, the more generous the energies it deploys and consumes for explanatory and obstacle overcoming purposes. The result is that every crisis teaches us a lesson. What interests us is who writes and who learns from this lesson, and if they do. Then we try to find why serious crises, like the current one, occur once or twice in a century. What is the role that big world market players have in crisis “preparation”, onset and resorption? Do solutions originate in the state’s support or in the market? Does globalization erase national borders in such situations? How and to what extent real economy may penalize a guilty party that constantly comes from nominal economy? What are the problems raised by such an outcome for the strategy to follow and for economic sciences in general, etc.?crisis, Keynes, truth, compromise, market, state, human nature, speculation, indebtedness, personal calculation

Inference of Regular Expressions for Text Extraction from Examples

A large class of entity extraction tasks from text that is either semistructured or fully unstructured may be addressed by regular expressions, because in many practical cases the relevant entities follow an underlying syntactical pattern and this pattern may be described by a regular expression. In this work we consider the long-standing problem of synthesizing such expressions automatically, based solely on examples of the desired behavior. We present the design and implementation of a system capable of addressing extraction tasks of realistic complexity. Our system is based on an evolutionary procedure carefully tailored to the specific needs of regular expression generation by examples. The procedure executes a search driven by a multiobjective optimization strategy aimed at simultaneously improving multiple performance indexes of candidate solutions while at the same time ensuring an adequate exploration of the huge solution space. We assess our proposal experimentally in great depth, on a number of challenging datasets. The accuracy of the obtained solutions seems to be adequate for practical usage and improves over earlier proposals significantly. Most importantly, our results are highly competitive even with respect to human operators. A prototype is available as a web application at http://regex.inginf.units.it