357 research outputs found
Multi-turn Inference Matching Network for Natural Language Inference
Natural Language Inference (NLI) is a fundamental and challenging task in
Natural Language Processing (NLP). Most existing methods only apply one-pass
inference process on a mixed matching feature, which is a concatenation of
different matching features between a premise and a hypothesis. In this paper,
we propose a new model called Multi-turn Inference Matching Network (MIMN) to
perform multi-turn inference on different matching features. In each turn, the
model focuses on one particular matching feature instead of the mixed matching
feature. To enhance the interaction between different matching features, a
memory component is employed to store the history inference information. The
inference of each turn is performed on the current matching feature and the
memory. We conduct experiments on three different NLI datasets. The
experimental results show that our model outperforms or achieves the
state-of-the-art performance on all the three datasets
Dynamic Compositional Neural Networks over Tree Structure
Tree-structured neural networks have proven to be effective in learning
semantic representations by exploiting syntactic information. In spite of their
success, most existing models suffer from the underfitting problem: they
recursively use the same shared compositional function throughout the whole
compositional process and lack expressive power due to inability to capture the
richness of compositionality. In this paper, we address this issue by
introducing the dynamic compositional neural networks over tree structure
(DC-TreeNN), in which the compositional function is dynamically generated by a
meta network. The role of meta-network is to capture the metaknowledge across
the different compositional rules and formulate them. Experimental results on
two typical tasks show the effectiveness of the proposed models.Comment: Accepted by IJCAI 201
Neural Natural Language Inference Models Enhanced with External Knowledge
Modeling natural language inference is a very challenging task. With the
availability of large annotated data, it has recently become feasible to train
complex models such as neural-network-based inference models, which have shown
to achieve the state-of-the-art performance. Although there exist relatively
large annotated data, can machines learn all knowledge needed to perform
natural language inference (NLI) from these data? If not, how can
neural-network-based NLI models benefit from external knowledge and how to
build NLI models to leverage it? In this paper, we enrich the state-of-the-art
neural natural language inference models with external knowledge. We
demonstrate that the proposed models improve neural NLI models to achieve the
state-of-the-art performance on the SNLI and MultiNLI datasets.Comment: Accepted by ACL 201
A Generalized Recurrent Neural Architecture for Text Classification with Multi-Task Learning
Multi-task learning leverages potential correlations among related tasks to
extract common features and yield performance gains. However, most previous
works only consider simple or weak interactions, thereby failing to model
complex correlations among three or more tasks. In this paper, we propose a
multi-task learning architecture with four types of recurrent neural layers to
fuse information across multiple related tasks. The architecture is
structurally flexible and considers various interactions among tasks, which can
be regarded as a generalized case of many previous works. Extensive experiments
on five benchmark datasets for text classification show that our model can
significantly improve performances of related tasks with additional information
from others
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