16861729
Normalization of non-standard words
In addition to ordinary words and names, real text contains non-standard “words" (NSWs), including numbers, abbreviations, dates, currency amounts and acronyms. Typically, one cannot find NSWs in a dictionary, nor can one find their pronunciation by an application of ordinary “letter-to-sound" rules. Non-standard words also have a greater propensity than ordinary words to be ambiguous with respect to their interpretation or pronunciation. In many applications, it is desirable to “normalize" text by replacing the NSWs with the contextually appropriate ordinary word or sequence of words. Typical technology for text normalization involves sets of ad hoc rules tuned to handle one or two genres of text (often newspaper-style text) with the expected result that the techniques do not usually generalize well to new domains. The purpose of the work reported here is to take some initial steps towards addressing deficiencies in previous approaches to text normalization. We developed a taxonomy of NSWs on the basis of four rather distinct text types?news text, a recipes newsgroup, a hardware-product-specific newsgroup, and real-estate classified ads. We then investigated the application of several general techniques including n-gram language models, decision trees and weighted finite-state transducers to the range of NSW types, and demonstrated that a systematic treatment can lead to better results than have been obtained by the ad hoc treatments that have typically been used in the past. For abbreviation expansion in particular, we investigated both supervised and unsupervised approaches. We report results in terms of word-error rate, which is standard in speech recognition evaluations, but which has only occasionally been used as an overall measure in evaluating text normalization systems.
11706155
Minimum Bayes-Risk Decoding for Statistical Machine Translation
Abstract : We present Minimum Bayes-Risk (MBR) decoding for statistical machine translation. This statistical approach aims to minimize expected loss of translation errors under loss functions that measure translation performance. We describe a hierarchy of loss functions that incorporate different levels of linguistic information from word strings, word-to-word alignments from an MT system, and syntactic structure from parse-trees of source and target language sentences. We report the performance of the MBR decoders on a Chinese-to-English translation task. Our results show that MBR decoding can be used to tune statistical MT performance for specific loss functions.
1288560
A Weighted Finite State Transducer Implementation of the Alignment Template Model for Statistical Machine Translation
We present a derivation of the alignment template model for statistical machine translation and an implementation of the model using weighted finite state transducers. The approach we describe allows us to implement each constituent distribution of the model as a weighted finite state transducer or acceptor. We show that bitext word alignment and translation under the model can be performed with standard FSM operations involving these transducers. One of the benefits of using this framework is that it obviates the need to develop specialized search procedures, even for the generation of lattices or N-Best lists of bitext word alignments and translation hypotheses. We evaluate the implementation of the model on the French-to-English Hansards task and report alignment and translation performance.
6244213
A Smorgasbord of Features for Statistical Machine Translation
We describe a methodology for rapid experimentation in statistical machine translation which we use to add a large number of features to a baseline system exploiting features from a wide range of levels of syntactic representation. Feature values were combined in a log-linear model to select the highest scoring candidate translation from an n-best list. Feature weights were optimized directly against the BLEU evaluation metric on held-out data. We present results for a small selection of features at each level of syntactic representation.
6958332
A hierarchical field framework for unified context-based classification
We present a two-layer hierarchical formulation to exploit different levels of contextual information in images for robust classification. Each layer is modeled as a conditional field that allows one to capture arbitrary observation-dependent label interactions. The proposed framework has two main advantages. First, it encodes both the short-range interactions (e.g., pixelwise label smoothing) as well as the long-range interactions (e.g., relative configurations of objects or regions) in a tractable manner. Second, the formulation is general enough to be applied to different domains ranging from pixelwise image labeling to contextual object detection. The parameters of the model are learned using a sequential maximum-likelihood approximation. The benefits of the proposed framework are demonstrated on four different datasets and comparison results are presented
14954871
Hierarchical Fusion of Multiple Classifiers for Hyperspectral Data Analysis
Abstract: Many classification problems involve high dimensional inputs and a large number of classes. Multiclassifier fusion approaches to such difficult problems typically centre around smart feature extraction, input resampling methods, or input space partitioning to exploit modular learning. In this paper, we investigate how partitioning of the output space (i.e. the set of class labels) can be exploited in a multiclassifier fusion framework to simplify such problems and to yield better solutions. Specifically, we introduce a hierarchical technique to recursively decompose a C-class problem into C_1 two-(meta) class problems. A generalised modular learning framework is used to partition a set of classes into two disjoint groups called meta-classes. The coupled problems of finding a good partition and of searching for a linear feature extractor that best discriminates the resulting two meta-classes are solved simultaneously at each stage of the recursive algorithm. This results in a binary tree whose leaf nodes represent the original C classes. The proposed hierarchical multiclassifier framework is particularly effective for difficult classification problems involving a moderately large number of classes. The proposed method is illustrated on a problem related to classification of landcover using hyperspectral data: a 12-class AVIRIS subset with 180 bands. For this problem, the classification accuracies obtained were superior to most other techniques developed for hyperspectral classification. Moreover, the class hierarchies that were automatically discovered conformed very well with human domain experts’ opinions, which demonstrates the potential of using such a modular learning approach for discovering domain knowledge automatically from data.
3185097
Towards a fault-tolerant multi-agent system architecture
Multi-agent systems are prone to failures typical of any distributed system. Agents and resources may become unavailable due to machine crashes, communication breakdowns, process failures, and numerous other hardware and software failures. Most of the work done in fault handling in multi-agent systems deals with detection and recovery from faults such as state-inconsistencies, relying on the traditional techniques for recovering from other distributed systems failure. However, the traditional fault-tolerance techniques are designed for specific situations and they require special infrastructural support. We argue for fault-tolerance techniques that can be readily implemented using a generic agent shell with minimal or no modification to the agent infrastructure. We propose that theories from multi-agent systems literature can be effectively combined with basic fault-tolerance principles to design robust multi-agent systems. In particular, we argue that (1) teamwork may be used to create a robust brokered architecture that can recover a multi-agent system from broker failures without incurring undue overheads, (2) teamwork may also be used to guarantee a specified number of brokers in a large multi-agent system, and (3) agent autonomy can be used to prevent thrashing and guarantee acceptable levels of quality of service by an agent. We also describe the Adaptive Agent Architecture (AAA), a fault-tolerant brokered multi-agent system architecture, and present experimental evidence using the AAA to validate our approach.
127631
Semantics of Agent Communication Languages for Group Interaction
Group communication is the core of societal interactions. Therefore, artificial agents should be able to communicate with groups as well as individuals. However, most contemporary agent communication languages, notably FIPA and KQML, have either no provision or no well-defined semantics for group communication. We give semantics for group communication that we believe can profitably enrich the agent communication languages. In our semantics, individual communication is a special case of group communication wherein each communicating group consists of a single agent.
34159357
A P1500 compliant programmable BistShell for embedded memories
We describe the design and implementation of an IEEE P1500 compliant programmable BIST for embedded memories. The proposed design can be embedded in other cores or systems with minimum test generation or test application overhead. The programmability of our BIST is useful when the algorithm is being refined while the memory architecture is under production. A variety of test algorithms can be implemented with the programmability provided in our design with no change to the BIST hardware. As an example we demonstrate the implementation of an algorithm to detect open decoder faults. This example is shown for its didactic content as it brings out the programmable axis of our design. Our design also offers means to perform dedicated delay tests as well as scan tests for diagnosis. We show by synthesis experiments that the extra area cost for the BIST hardware is relatively small for medium to large memories.