Attentive Convolution: Equipping CNNs with RNN-style Attention Mechanisms

Yin, Wenpeng und Schütze, Hinrich (2018): Attentive Convolution: Equipping CNNs with RNN-style Attention Mechanisms. In: Transactions of the Association for Computational Linguistics, Bd. 6: S. 687-702

Abstract

In NLP, convolutional neural networks (CNNs) have benefited less than recurrent neural networks (RNNs) from attention mechanisms. We hypothesize that this is because the attention in CNNs has been mainly implemented as attentive pooling (i.e., it is applied to pooling) rather than as attentive convolution (i.e., it is integrated into convolution). Convolution is the differentiator of CNNs in that it can powerfully model the higher-level representation of a word by taking into account its local fixed-size context in the input text t˟. In this work, we propose an attentive convolution network, ATTCONV. It extends the context scope of the convolution operation, deriving higherlevel features for a word not only from local context, but also from information extracted from nonlocal context by the attention mechanism commonly used in RNNs. This nonlocal context can come (i) from parts of the input text t˟ that are distant or (ii) from extra (i.e., external) contexts ty. Experiments on sentence modeling with zero-context (sentiment analysis), singlecontext (textual entailment) and multiplecontext (claim verification) demonstrate the effectiveness of ATTCONV in sentence representation learning with the incorporation of context. In particular, attentive convolution outperforms attentive pooling and is a strong competitor to popular attentive RNNs.

• Dokumententyp: Zeitschriftenartikel
• EU Funded Grant Agreement Number: 740516
• EU-Projekte: Horizon 2020 > ERC Grants > ERC Advanced Grant > ERC Grant 740516: NonSequeToR - Non-sequence models for tokenization replacement
• Publikationsform: Publisher's Version
• Fakultätsübergreifende Einrichtungen: Centrum für Informations- und Sprachverarbeitung (CIS)
• Themengebiete: 000 Informatik, Informationswissenschaft, allgemeine Werke > 000 Informatik, Wissen, Systeme ; 000 Informatik, Informationswissenschaft, allgemeine Werke > 004 Informatik ; 400 Sprache > 400 Sprache ; 400 Sprache > 410 Linguistik
• ISSN: 2307-387X
• Sprache: Englisch