15,043 research outputs found
What Is One Grain of Sand in the Desert? Analyzing Individual Neurons in Deep NLP Models
Despite the remarkable evolution of deep neural networks in natural language
processing (NLP), their interpretability remains a challenge. Previous work
largely focused on what these models learn at the representation level. We
break this analysis down further and study individual dimensions (neurons) in
the vector representation learned by end-to-end neural models in NLP tasks. We
propose two methods: Linguistic Correlation Analysis, based on a supervised
method to extract the most relevant neurons with respect to an extrinsic task,
and Cross-model Correlation Analysis, an unsupervised method to extract salient
neurons w.r.t. the model itself. We evaluate the effectiveness of our
techniques by ablating the identified neurons and reevaluating the network's
performance for two tasks: neural machine translation (NMT) and neural language
modeling (NLM). We further present a comprehensive analysis of neurons with the
aim to address the following questions: i) how localized or distributed are
different linguistic properties in the models? ii) are certain neurons
exclusive to some properties and not others? iii) is the information more or
less distributed in NMT vs. NLM? and iv) how important are the neurons
identified through the linguistic correlation method to the overall task? Our
code is publicly available as part of the NeuroX toolkit (Dalvi et al. 2019).Comment: AAA 2019, pages 10, AAAI Conference on Artificial Intelligence (AAAI
2019
Exposing the Functionalities of Neurons for Gated Recurrent Unit Based Sequence-to-Sequence Model
The goal of this paper is to report certain scientific discoveries about a
Seq2Seq model. It is known that analyzing the behavior of RNN-based models at
the neuron level is considered a more challenging task than analyzing a DNN or
CNN models due to their recursive mechanism in nature. This paper aims to
provide neuron-level analysis to explain why a vanilla GRU-based Seq2Seq model
without attention can achieve token-positioning. We found four different types
of neurons: storing, counting, triggering, and outputting and further uncover
the mechanism for these neurons to work together in order to produce the right
token in the right position.Comment: 9 pages (excluding reference), 10 figure
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