22,236 research outputs found
Recursive Neural Networks Can Learn Logical Semantics
Tree-structured recursive neural networks (TreeRNNs) for sentence meaning
have been successful for many applications, but it remains an open question
whether the fixed-length representations that they learn can support tasks as
demanding as logical deduction. We pursue this question by evaluating whether
two such models---plain TreeRNNs and tree-structured neural tensor networks
(TreeRNTNs)---can correctly learn to identify logical relationships such as
entailment and contradiction using these representations. In our first set of
experiments, we generate artificial data from a logical grammar and use it to
evaluate the models' ability to learn to handle basic relational reasoning,
recursive structures, and quantification. We then evaluate the models on the
more natural SICK challenge data. Both models perform competitively on the SICK
data and generalize well in all three experiments on simulated data, suggesting
that they can learn suitable representations for logical inference in natural
language
Is Structure Necessary for Modeling Argument Expectations in Distributional Semantics?
Despite the number of NLP studies dedicated to thematic fit estimation,
little attention has been paid to the related task of composing and updating
verb argument expectations. The few exceptions have mostly modeled this
phenomenon with structured distributional models, implicitly assuming a
similarly structured representation of events. Recent experimental evidence,
however, suggests that human processing system could also exploit an
unstructured "bag-of-arguments" type of event representation to predict
upcoming input. In this paper, we re-implement a traditional structured model
and adapt it to compare the different hypotheses concerning the degree of
structure in our event knowledge, evaluating their relative performance in the
task of the argument expectations update.Comment: conference paper, IWC
Improved Semantic Representations From Tree-Structured Long Short-Term Memory Networks
Because of their superior ability to preserve sequence information over time,
Long Short-Term Memory (LSTM) networks, a type of recurrent neural network with
a more complex computational unit, have obtained strong results on a variety of
sequence modeling tasks. The only underlying LSTM structure that has been
explored so far is a linear chain. However, natural language exhibits syntactic
properties that would naturally combine words to phrases. We introduce the
Tree-LSTM, a generalization of LSTMs to tree-structured network topologies.
Tree-LSTMs outperform all existing systems and strong LSTM baselines on two
tasks: predicting the semantic relatedness of two sentences (SemEval 2014, Task
1) and sentiment classification (Stanford Sentiment Treebank).Comment: Accepted for publication at ACL 201
"Not not bad" is not "bad": A distributional account of negation
With the increasing empirical success of distributional models of
compositional semantics, it is timely to consider the types of textual logic
that such models are capable of capturing. In this paper, we address
shortcomings in the ability of current models to capture logical operations
such as negation. As a solution we propose a tripartite formulation for a
continuous vector space representation of semantics and subsequently use this
representation to develop a formal compositional notion of negation within such
models.Comment: 9 pages, to appear in Proceedings of the 2013 Workshop on Continuous
Vector Space Models and their Compositionalit
Dynamic Compositional Neural Networks over Tree Structure
Tree-structured neural networks have proven to be effective in learning
semantic representations by exploiting syntactic information. In spite of their
success, most existing models suffer from the underfitting problem: they
recursively use the same shared compositional function throughout the whole
compositional process and lack expressive power due to inability to capture the
richness of compositionality. In this paper, we address this issue by
introducing the dynamic compositional neural networks over tree structure
(DC-TreeNN), in which the compositional function is dynamically generated by a
meta network. The role of meta-network is to capture the metaknowledge across
the different compositional rules and formulate them. Experimental results on
two typical tasks show the effectiveness of the proposed models.Comment: Accepted by IJCAI 201
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