131,883 research outputs found
Bilateral Multi-Perspective Matching for Natural Language Sentences
Natural language sentence matching is a fundamental technology for a variety
of tasks. Previous approaches either match sentences from a single direction or
only apply single granular (word-by-word or sentence-by-sentence) matching. In
this work, we propose a bilateral multi-perspective matching (BiMPM) model
under the "matching-aggregation" framework. Given two sentences and ,
our model first encodes them with a BiLSTM encoder. Next, we match the two
encoded sentences in two directions and . In
each matching direction, each time step of one sentence is matched against all
time-steps of the other sentence from multiple perspectives. Then, another
BiLSTM layer is utilized to aggregate the matching results into a fix-length
matching vector. Finally, based on the matching vector, the decision is made
through a fully connected layer. We evaluate our model on three tasks:
paraphrase identification, natural language inference and answer sentence
selection. Experimental results on standard benchmark datasets show that our
model achieves the state-of-the-art performance on all tasks.Comment: To appear in Proceedings of IJCAI 201
Memory-Based Shallow Parsing
We present memory-based learning approaches to shallow parsing and apply
these to five tasks: base noun phrase identification, arbitrary base phrase
recognition, clause detection, noun phrase parsing and full parsing. We use
feature selection techniques and system combination methods for improving the
performance of the memory-based learner. Our approach is evaluated on standard
data sets and the results are compared with that of other systems. This reveals
that our approach works well for base phrase identification while its
application towards recognizing embedded structures leaves some room for
improvement
Extended pipeline for content-based feature engineering in music genre recognition
We present a feature engineering pipeline for the construction of musical
signal characteristics, to be used for the design of a supervised model for
musical genre identification. The key idea is to extend the traditional
two-step process of extraction and classification with additive stand-alone
phases which are no longer organized in a waterfall scheme. The whole system is
realized by traversing backtrack arrows and cycles between various stages. In
order to give a compact and effective representation of the features, the
standard early temporal integration is combined with other selection and
extraction phases: on the one hand, the selection of the most meaningful
characteristics based on information gain, and on the other hand, the inclusion
of the nonlinear correlation between this subset of features, determined by an
autoencoder. The results of the experiments conducted on GTZAN dataset reveal a
noticeable contribution of this methodology towards the model's performance in
classification task.Comment: ICASSP 201
Homogenous Ensemble Phonotactic Language Recognition Based on SVM Supervector Reconstruction
Currently, acoustic spoken language recognition (SLR) and phonotactic SLR systems are widely used language recognition systems. To achieve better performance, researchers combine multiple subsystems with the results often much better than a single SLR system. Phonotactic SLR subsystems may vary in the acoustic features vectors or include multiple language-specific phone recognizers and different acoustic models. These methods achieve good performance but usually compute at high computational cost. In this paper, a new diversification for phonotactic language recognition systems is proposed using vector space models by support vector machine (SVM) supervector reconstruction (SSR). In this architecture, the subsystems share the same feature extraction, decoding, and N-gram counting preprocessing steps, but model in a different vector space by using the SSR algorithm without significant additional computation. We term this a homogeneous ensemble phonotactic language recognition (HEPLR) system. The system integrates three different SVM supervector reconstruction algorithms, including relative SVM supervector reconstruction, functional SVM supervector reconstruction, and perturbing SVM supervector reconstruction. All of the algorithms are incorporated using a linear discriminant analysis-maximum mutual information (LDA-MMI) backend for improving language recognition evaluation (LRE) accuracy. Evaluated on the National Institute of Standards and Technology (NIST) LRE 2009 task, the proposed HEPLR system achieves better performance than a baseline phone recognition-vector space modeling (PR-VSM) system with minimal extra computational cost. The performance of the HEPLR system yields 1.39%, 3.63%, and 14.79% equal error rate (EER), representing 6.06%, 10.15%, and 10.53% relative improvements over the baseline system, respectively, for the 30-, 10-, and 3-s test conditions
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