421 research outputs found
Correlating neural and symbolic representations of language
Analysis methods which enable us to better understand the representations and
functioning of neural models of language are increasingly needed as deep
learning becomes the dominant approach in NLP. Here we present two methods
based on Representational Similarity Analysis (RSA) and Tree Kernels (TK) which
allow us to directly quantify how strongly the information encoded in neural
activation patterns corresponds to information represented by symbolic
structures such as syntax trees. We first validate our methods on the case of a
simple synthetic language for arithmetic expressions with clearly defined
syntax and semantics, and show that they exhibit the expected pattern of
results. We then apply our methods to correlate neural representations of
English sentences with their constituency parse trees.Comment: ACL 201
Evaluating the Representational Hub of Language and Vision Models
The multimodal models used in the emerging field at the intersection of
computational linguistics and computer vision implement the bottom-up
processing of the `Hub and Spoke' architecture proposed in cognitive science to
represent how the brain processes and combines multi-sensory inputs. In
particular, the Hub is implemented as a neural network encoder. We investigate
the effect on this encoder of various vision-and-language tasks proposed in the
literature: visual question answering, visual reference resolution, and
visually grounded dialogue. To measure the quality of the representations
learned by the encoder, we use two kinds of analyses. First, we evaluate the
encoder pre-trained on the different vision-and-language tasks on an existing
diagnostic task designed to assess multimodal semantic understanding. Second,
we carry out a battery of analyses aimed at studying how the encoder merges and
exploits the two modalities.Comment: Accepted to IWCS 201
Learning semantic sentence representations from visually grounded language without lexical knowledge
Current approaches to learning semantic representations of sentences often
use prior word-level knowledge. The current study aims to leverage visual
information in order to capture sentence level semantics without the need for
word embeddings. We use a multimodal sentence encoder trained on a corpus of
images with matching text captions to produce visually grounded sentence
embeddings. Deep Neural Networks are trained to map the two modalities to a
common embedding space such that for an image the corresponding caption can be
retrieved and vice versa. We show that our model achieves results comparable to
the current state-of-the-art on two popular image-caption retrieval benchmark
data sets: MSCOCO and Flickr8k. We evaluate the semantic content of the
resulting sentence embeddings using the data from the Semantic Textual
Similarity benchmark task and show that the multimodal embeddings correlate
well with human semantic similarity judgements. The system achieves
state-of-the-art results on several of these benchmarks, which shows that a
system trained solely on multimodal data, without assuming any word
representations, is able to capture sentence level semantics. Importantly, this
result shows that we do not need prior knowledge of lexical level semantics in
order to model sentence level semantics. These findings demonstrate the
importance of visual information in semantics
Fast and Deep Graph Neural Networks
We address the efficiency issue for the construction of a deep graph neural
network (GNN). The approach exploits the idea of representing each input graph
as a fixed point of a dynamical system (implemented through a recurrent neural
network), and leverages a deep architectural organization of the recurrent
units. Efficiency is gained by many aspects, including the use of small and
very sparse networks, where the weights of the recurrent units are left
untrained under the stability condition introduced in this work. This can be
viewed as a way to study the intrinsic power of the architecture of a deep GNN,
and also to provide insights for the set-up of more complex fully-trained
models. Through experimental results, we show that even without training of the
recurrent connections, the architecture of small deep GNN is surprisingly able
to achieve or improve the state-of-the-art performance on a significant set of
tasks in the field of graphs classification.Comment: Pre-print of 'Fast and Deep Graph Neural Networks', accepted for AAAI
2020. This document includes the Supplementary Materia
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