20 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-task Learning of Pairwise Sequence Classification Tasks Over Disparate Label Spaces
We combine multi-task learning and semi-supervised learning by inducing a
joint embedding space between disparate label spaces and learning transfer
functions between label embeddings, enabling us to jointly leverage unlabelled
data and auxiliary, annotated datasets. We evaluate our approach on a variety
of sequence classification tasks with disparate label spaces. We outperform
strong single and multi-task baselines and achieve a new state-of-the-art for
topic-based sentiment analysis.Comment: To appear at NAACL 2018 (long
LCT-MALTAs submission to RepEval 2017 shared task
We present in this paper our team LCTMALTAâs
submission to the RepEval 2017
Shared Task on natural language inference.
Our system is a simple system
based on a standard BiLSTM architecture,
using as input GloVe word embeddings
augmented with further linguistic information.
We use max pooling on the
BiLSTM outputs to obtain embeddings for
sentences. On both the matched and the
mismatched test sets, our system clearly
beats the shared taskâs BiLSTM baseline
model.peer-reviewe
Compare, Compress and Propagate: Enhancing Neural Architectures with Alignment Factorization for Natural Language Inference
This paper presents a new deep learning architecture for Natural Language
Inference (NLI). Firstly, we introduce a new architecture where alignment pairs
are compared, compressed and then propagated to upper layers for enhanced
representation learning. Secondly, we adopt factorization layers for efficient
and expressive compression of alignment vectors into scalar features, which are
then used to augment the base word representations. The design of our approach
is aimed to be conceptually simple, compact and yet powerful. We conduct
experiments on three popular benchmarks, SNLI, MultiNLI and SciTail, achieving
competitive performance on all. A lightweight parameterization of our model
also enjoys a times reduction in parameter size compared to the
existing state-of-the-art models, e.g., ESIM and DIIN, while maintaining
competitive performance. Additionally, visual analysis shows that our
propagated features are highly interpretable.Comment: EMNLP 2018 CRC and Update CAFE + ELMo result on SNL
On the Evaluation of Semantic Phenomena in Neural Machine Translation Using Natural Language Inference
We propose a process for investigating the extent to which sentence
representations arising from neural machine translation (NMT) systems encode
distinct semantic phenomena. We use these representations as features to train
a natural language inference (NLI) classifier based on datasets recast from
existing semantic annotations. In applying this process to a representative NMT
system, we find its encoder appears most suited to supporting inferences at the
syntax-semantics interface, as compared to anaphora resolution requiring
world-knowledge. We conclude with a discussion on the merits and potential
deficiencies of the existing process, and how it may be improved and extended
as a broader framework for evaluating semantic coverage.Comment: To be presented at NAACL 2018 - 11 page