Matching-CNN Meets KNN: Quasi-Parametric Human Parsing

Both parametric and non-parametric approaches have demonstrated encouraging performances in the human parsing task, namely segmenting a human image into several semantic regions (e.g., hat, bag, left arm, face). In this work, we aim to develop a new solution with the advantages of both methodologies, namely supervision from annotated data and the flexibility to use newly annotated (possibly uncommon) images, and present a quasi-parametric human parsing model. Under the classic K Nearest Neighbor (KNN)-based nonparametric framework, the parametric Matching Convolutional Neural Network (M-CNN) is proposed to predict the matching confidence and displacements of the best matched region in the testing image for a particular semantic region in one KNN image. Given a testing image, we first retrieve its KNN images from the annotated/manually-parsed human image corpus. Then each semantic region in each KNN image is matched with confidence to the testing image using M-CNN, and the matched regions from all KNN images are further fused, followed by a superpixel smoothing procedure to obtain the ultimate human parsing result. The M-CNN differs from the classic CNN in that the tailored cross image matching filters are introduced to characterize the matching between the testing image and the semantic region of a KNN image. The cross image matching filters are defined at different convolutional layers, each aiming to capture a particular range of displacements. Comprehensive evaluations over a large dataset with 7,700 annotated human images well demonstrate the significant performance gain from the quasi-parametric model over the state-of-the-arts, for the human parsing task.Comment: This manuscript is the accepted version for CVPR 201

Parsing as Reduction

We reduce phrase-representation parsing to dependency parsing. Our reduction is grounded on a new intermediate representation, "head-ordered dependency trees", shown to be isomorphic to constituent trees. By encoding order information in the dependency labels, we show that any off-the-shelf, trainable dependency parser can be used to produce constituents. When this parser is non-projective, we can perform discontinuous parsing in a very natural manner. Despite the simplicity of our approach, experiments show that the resulting parsers are on par with strong baselines, such as the Berkeley parser for English and the best single system in the SPMRL-2014 shared task. Results are particularly striking for discontinuous parsing of German, where we surpass the current state of the art by a wide margin

Apportioning Development Effort in a Probabilistic LR Parsing System through Evaluation

We describe an implemented system for robust domain-independent syntactic parsing of English, using a unification-based grammar of part-of-speech and punctuation labels coupled with a probabilistic LR parser. We present evaluations of the system's performance along several different dimensions; these enable us to assess the contribution that each individual part is making to the success of the system as a whole, and thus prioritise the effort to be devoted to its further enhancement. Currently, the system is able to parse around 80% of sentences in a substantial corpus of general text containing a number of distinct genres. On a random sample of 250 such sentences the system has a mean crossing bracket rate of 0.71 and recall and precision of 83% and 84% respectively when evaluated against manually-disambiguated analyses.Comment: 10 pages, 1 Postscript figure. To Appear in Proceedings of the Conference on Empirical Methods in Natural Language Processing, University of Pennsylvania, May 199

Parsing of Spoken Language under Time Constraints

Spoken language applications in natural dialogue settings place serious requirements on the choice of processing architecture. Especially under adverse phonetic and acoustic conditions parsing procedures have to be developed which do not only analyse the incoming speech in a time-synchroneous and incremental manner, but which are able to schedule their resources according to the varying conditions of the recognition process. Depending on the actual degree of local ambiguity the parser has to select among the available constraints in order to narrow down the search space with as little effort as possible. A parsing approach based on constraint satisfaction techniques is discussed. It provides important characteristics of the desired real-time behaviour and attempts to mimic some of the attention focussing capabilities of the human speech comprehension mechanism.Comment: 19 pages, LaTe

Syntactic Computation as Labelled Deduction: WH a case study

This paper addresses the question "Why do WH phenomena occur with the particular cluster of properties observed across languages -- long-distance dependencies, WH-in situ, partial movement constructions, reconstruction, crossover etc." These phenomena have been analysed by invoking a number of discrete principles and categories, but have so far resisted a unified treatment. The explanation proposed is set within a model of natural language understanding in context, where the task of understanding is taken to be the incremental building of a structure over which the semantic content is defined. The formal model is a composite of a labelled type-deduction system, a modal tree logic, and a set of rules for describing the process of interpreting the string as a set of transition states. A dynamic concept of syntax results, in which in addition to an output structure associated with each string (analogous to the level of LF), there is in addition an explicit meta-level description of the process whereby this incremental process takes place. This paper argues that WH-related phenomena can be unified by adopting this dynamic perspective. The main focus of the paper is on WH-initial structures, WH in situ structures, partial movement phenomena, and crossover phenomena. In each case, an analysis is proposed which emerges from the general characterisatioan of WH structures without construction-specific stipulation.Articl