149 research outputs found
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
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
Breaking NLI Systems with Sentences that Require Simple Lexical Inferences
We create a new NLI test set that shows the deficiency of state-of-the-art
models in inferences that require lexical and world knowledge. The new examples
are simpler than the SNLI test set, containing sentences that differ by at most
one word from sentences in the training set. Yet, the performance on the new
test set is substantially worse across systems trained on SNLI, demonstrating
that these systems are limited in their generalization ability, failing to
capture many simple inferences.Comment: 6 pages, short paper at ACL 201
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