Gated Convolutional Neural Network for Sentence Matching

Chen, Peixin; Guo, Wenbin; Chen, Zhi; Sun, Jian; You, Lanhua

doi:10.21437/interspeech.2018-70

Cited by 85 publications

(91 citation statements)

References 18 publications

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“…These neurons are responsible for covering small sub-areas of the visual area, named the receptive area. Then, the receptive areas are tiled to detect the overall visual area [49]. Hence, receptive areas are deemed as filters in the CNN deep learning model.…”

Section: A Convolutional Neural Networkmentioning

confidence: 99%

Sentence-Level Classification Using Parallel Fuzzy Deep Learning Classifier

et al. 2021

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At present, with the growing number of Web 2.0 platforms such as Instagram, Facebook, and Twitter, users honestly communicate their opinions and ideas about events, services, and products. Owing to this rise in the number of social platforms and their extensive use by people, enormous amounts of data are produced hourly. However, sentiment analysis or opinion mining is considered as a useful tool that aims to extract the emotion and attitude from the user-posted data on social media platforms by using different computational methods to linguistic terms and various Natural Language Processing (NLP). Therefore, enhancing text sentiment classification accuracy has become feasible, and an interesting research area for many community researchers. In this study, a new Fuzzy Deep Learning Classifier (FDLC) is suggested for improving the performance of data-sentiment classification. Our proposed FDLC integrates Convolutional Neural Network (CNN) to build an effective automatic process for extracting the features from collected unstructured data and Feedforward Neural Network (FFNN) to compute both positive and negative sentimental scores. Then, we used the Mamdani Fuzzy System (MFS) as a fuzzy classifier to classify the outcomes of the two used deep (CNN+FFNN) learning models in three classes, which are: Neutral, Negative, and Positive. Also, to prevent the long execution time taking by our hybrid proposed FDLC, we have implemented our proposal under the Hadoop cluster. An experimental comparative study between our FDLC and some other suggestions from the literature is performed to demonstrate our offered classifier's effectiveness. The empirical result proved that our FDLC performs better than other classifiers in terms of true positive rate, true negative rate, false positive rate, false negative rate, error rate, precision, classification rate, kappa statistic, F1-score and time consumption, complexity, convergence, and stability.

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Section: A Convolutional Neural Networkmentioning

confidence: 99%

Sentence-Level Classification Using Parallel Fuzzy Deep Learning Classifier

et al. 2021

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“…Ferreira et al (2018) feed sentence similarity measured with hand-crafted features to machine learning algorithms. Convolutional neural networks have been introduced by Yin and Schütze (2015) and Chen et al (2018), and further augmented with LSTMs (Kubal and Nimkar, 2018) and attention mechanisms (Fan et al, 2018).…”

Section: Leveraging Paraphrases In Nlpmentioning

confidence: 99%

Large-Scale, Diverse, Paraphrastic Bitexts via Sampling and Clustering

Hu¹,

Singh²,

Holzenberger³

et al. 2019

Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)

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Producing diverse paraphrases of a sentence is a challenging task. Natural paraphrase corpora are scarce and limited, while existing large-scale resources are automatically generated via back-translation and rely on beam search, which tends to lack diversity. We describe PARABANK 2, a new resource that contains multiple diverse sentential paraphrases, produced from a bilingual corpus using negative constraints, inference sampling, and clustering. We show that PARABANK 2 significantly surpasses prior work in both lexical and syntactic diversity while being meaningpreserving, as measured by human judgments and standardized metrics. Further, we illustrate how such paraphrastic resources may be used to refine contextualized encoders, leading to improvements in downstream tasks.

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“…In this section, we will employ the gating mechanisms for the xvector system in two ways. As depicted in Figure 1, we first use four GCNN [15] layers to replace the first four TDNN layers for extracting the frame-level representations. On the other hand, we propose the gated-attention statistics pooling as an alternative attention method for aggregating the frame-level vectors.…”

Section: Dnn With Gating Mechanismsmentioning

confidence: 99%

Deep Neural Network Embeddings with Gating Mechanisms for Text-Independent Speaker Verification

et al. 2019

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In this paper, gating mechanisms are applied in deep neural network (DNN) training for x-vector-based text-independent speaker verification. First, a gated convolution neural network (GCNN) is employed for modeling the frame-level embedding layers. Compared with the time-delay DNN (TDNN), the GCNN can obtain more expressive frame-level representations through carefully designed memory cell and gating mechanisms. Moreover, we propose a novel gated-attention statistics pooling strategy in which the attention scores are shared with the output gate. The gated-attention statistics pooling combines both gating and attention mechanisms into one framework; therefore, we can capture more useful information in the temporal pooling layer. Experiments are carried out using the NIST SRE16 and SRE18 evaluation datasets. The results demonstrate the effectiveness of the GCNN and show that the proposed gated-attention statistics pooling can further improve the performance.

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Gated Convolutional Neural Network for Sentence Matching

Cited by 85 publications

References 18 publications

Sentence-Level Classification Using Parallel Fuzzy Deep Learning Classifier

Sentence-Level Classification Using Parallel Fuzzy Deep Learning Classifier

Large-Scale, Diverse, Paraphrastic Bitexts via Sampling and Clustering

Deep Neural Network Embeddings with Gating Mechanisms for Text-Independent Speaker Verification

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