5,036 research outputs found
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
The Neuro-Symbolic Concept Learner: Interpreting Scenes, Words, and Sentences From Natural Supervision
We propose the Neuro-Symbolic Concept Learner (NS-CL), a model that learns
visual concepts, words, and semantic parsing of sentences without explicit
supervision on any of them; instead, our model learns by simply looking at
images and reading paired questions and answers. Our model builds an
object-based scene representation and translates sentences into executable,
symbolic programs. To bridge the learning of two modules, we use a
neuro-symbolic reasoning module that executes these programs on the latent
scene representation. Analogical to human concept learning, the perception
module learns visual concepts based on the language description of the object
being referred to. Meanwhile, the learned visual concepts facilitate learning
new words and parsing new sentences. We use curriculum learning to guide the
searching over the large compositional space of images and language. Extensive
experiments demonstrate the accuracy and efficiency of our model on learning
visual concepts, word representations, and semantic parsing of sentences.
Further, our method allows easy generalization to new object attributes,
compositions, language concepts, scenes and questions, and even new program
domains. It also empowers applications including visual question answering and
bidirectional image-text retrieval.Comment: ICLR 2019 (Oral). Project page: http://nscl.csail.mit.edu
Non-distributional Word Vector Representations
Data-driven representation learning for words is a technique of central
importance in NLP. While indisputably useful as a source of features in
downstream tasks, such vectors tend to consist of uninterpretable components
whose relationship to the categories of traditional lexical semantic theories
is tenuous at best. We present a method for constructing interpretable word
vectors from hand-crafted linguistic resources like WordNet, FrameNet etc.
These vectors are binary (i.e, contain only 0 and 1) and are 99.9% sparse. We
analyze their performance on state-of-the-art evaluation methods for
distributional models of word vectors and find they are competitive to standard
distributional approaches.Comment: Proceedings of ACL 201
Sparse Overcomplete Word Vector Representations
Current distributed representations of words show little resemblance to
theories of lexical semantics. The former are dense and uninterpretable, the
latter largely based on familiar, discrete classes (e.g., supersenses) and
relations (e.g., synonymy and hypernymy). We propose methods that transform
word vectors into sparse (and optionally binary) vectors. The resulting
representations are more similar to the interpretable features typically used
in NLP, though they are discovered automatically from raw corpora. Because the
vectors are highly sparse, they are computationally easy to work with. Most
importantly, we find that they outperform the original vectors on benchmark
tasks.Comment: Proceedings of ACL 201
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