Abstract
Anaphora resolution of Uyghur is a challenging task because of complex language structure and limited corpus. We propose a multi-attention based capsule network model for Uyghur personal pronouns resolution, which can obtain the multi-layer and implicit semantic information effectively. Independently recurrent neural network (IndRNN) is applied in this model to achieve the interdependent features with long distance. Moreover, the capsule network can extract richer textual information to improve expression ability. Compared with the single attention-based model which combines Long Short-Term Memory (LSTM), the multi-attention based capsule network can capture multi-layer semantic information through a multi-attention mechanism without using any external parsing results. Experimental results on Uyghur dataset show that our approach surpasses the state-of-the-art models and gets the highest F-score of 83.85%. Meanwhile, our experimental results demonstrate the proposed method can effectively improve the performance of Uyghur personal pronouns resolution.
Highlights
Anaphora, as a special linguistic phenomenon, is pervasive in the expression of natural language
The four models we propose are as follows: 1) IRCC: consists of Independently recurrent neural network (IndRNN), Capsule network and CNN. 2) CMAC: Consists of Capsule network, Multi-attention and CNN. 3) IMACN: Consists of IndRNN, Multi-attention and CNN. 4) IMAC: Consists of IndRNN, Multi-attention and Capsule network
For the ‘‘Overall’’ results, our model obtains a considerable improvement by 0.96% in F-score over the best baseline, which demonstrates the efficiency of the proposed technique. It can be seen from the analysis of the experimental results that the proposed model can effectively deal with the task of anaphora resolution
Summary
As a special linguistic phenomenon, is pervasive in the expression of natural language. Q. Yang et al.: Multi-Attention-Based Capsule Network for Uyghur Personal Pronouns Resolution models have demonstrated their capabilities to learn vectorspace semantics of pronouns and their pronoun-candidate antecedent, and substantially surpass classic models, obtaining state-of-the-art experiment results on the benchmark dataset. Yang et al.: Multi-Attention-Based Capsule Network for Uyghur Personal Pronouns Resolution models have demonstrated their capabilities to learn vectorspace semantics of pronouns and their pronoun-candidate antecedent, and substantially surpass classic models, obtaining state-of-the-art experiment results on the benchmark dataset Though these previous methods have achieved ideal performance, all of these studies are based on English or Chinese with large-scale corpus. A desirable model should be able to 1) take advantage cues of multi-layer semantic features to predict pronoun-candidate antecedent and 2) Analyze deep context semantics and mine word sequence dependencies and 3) identify the distance between personal pronouns and candidate antecedents To achieve these goals, we propose a multi-attention based capsule network for personal pronoun anaphora resolution. We design a position recognition algorithm, which can make effective use of position during model training
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