9,858 research outputs found
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
Collecting Diverse Natural Language Inference Problems for Sentence Representation Evaluation
We present a large-scale collection of diverse natural language inference
(NLI) datasets that help provide insight into how well a sentence
representation captures distinct types of reasoning. The collection results
from recasting 13 existing datasets from 7 semantic phenomena into a common NLI
structure, resulting in over half a million labeled context-hypothesis pairs in
total. We refer to our collection as the DNC: Diverse Natural Language
Inference Collection. The DNC is available online at https://www.decomp.net,
and will grow over time as additional resources are recast and added from novel
sources.Comment: To be presented at EMNLP 2018. 15 page
Analyzing and Interpreting Neural Networks for NLP: A Report on the First BlackboxNLP Workshop
The EMNLP 2018 workshop BlackboxNLP was dedicated to resources and techniques
specifically developed for analyzing and understanding the inner-workings and
representations acquired by neural models of language. Approaches included:
systematic manipulation of input to neural networks and investigating the
impact on their performance, testing whether interpretable knowledge can be
decoded from intermediate representations acquired by neural networks,
proposing modifications to neural network architectures to make their knowledge
state or generated output more explainable, and examining the performance of
networks on simplified or formal languages. Here we review a number of
representative studies in each category
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