22,297 research outputs found
BattRAE: Bidimensional Attention-Based Recursive Autoencoders for Learning Bilingual Phrase Embeddings
In this paper, we propose a bidimensional attention based recursive
autoencoder (BattRAE) to integrate clues and sourcetarget interactions at
multiple levels of granularity into bilingual phrase representations. We employ
recursive autoencoders to generate tree structures of phrases with embeddings
at different levels of granularity (e.g., words, sub-phrases and phrases). Over
these embeddings on the source and target side, we introduce a bidimensional
attention network to learn their interactions encoded in a bidimensional
attention matrix, from which we extract two soft attention weight distributions
simultaneously. These weight distributions enable BattRAE to generate
compositive phrase representations via convolution. Based on the learned phrase
representations, we further use a bilinear neural model, trained via a
max-margin method, to measure bilingual semantic similarity. To evaluate the
effectiveness of BattRAE, we incorporate this semantic similarity as an
additional feature into a state-of-the-art SMT system. Extensive experiments on
NIST Chinese-English test sets show that our model achieves a substantial
improvement of up to 1.63 BLEU points on average over the baseline.Comment: 7 pages, accepted by AAAI 201
Bridging Low-level Geometry to High-level Concepts in Visual Servoing of Robot Manipulation Task Using Event Knowledge Graphs and Vision-Language Models
In this paper, we propose a framework of building knowledgeable robot control
in the scope of smart human-robot interaction, by empowering a basic
uncalibrated visual servoing controller with contextual knowledge through the
joint usage of event knowledge graphs (EKGs) and large-scale pretrained
vision-language models (VLMs). The framework is expanded in twofold: first, we
interpret low-level image geometry as high-level concepts, allowing us to
prompt VLMs and to select geometric features of points and lines for motor
control skills; then, we create an event knowledge graph (EKG) to conceptualize
a robot manipulation task of interest, where the main body of the EKG is
characterized by an executable behavior tree, and the leaves by semantic
concepts relevant to the manipulation context. We demonstrate, in an
uncalibrated environment with real robot trials, that our method lowers the
reliance of human annotation during task interfacing, allows the robot to
perform activities of daily living more easily by treating low-level
geometric-based motor control skills as high-level concepts, and is beneficial
in building cognitive thinking for smart robot applications
Attentive Convolution: Equipping CNNs with RNN-style Attention Mechanisms
In NLP, convolutional neural networks (CNNs) have benefited less than
recurrent neural networks (RNNs) from attention mechanisms. We hypothesize that
this is because the attention in CNNs has been mainly implemented as attentive
pooling (i.e., it is applied to pooling) rather than as attentive convolution
(i.e., it is integrated into convolution). Convolution is the differentiator of
CNNs in that it can powerfully model the higher-level representation of a word
by taking into account its local fixed-size context in the input text t^x. In
this work, we propose an attentive convolution network, ATTCONV. It extends the
context scope of the convolution operation, deriving higher-level features for
a word not only from local context, but also information extracted from
nonlocal context by the attention mechanism commonly used in RNNs. This
nonlocal context can come (i) from parts of the input text t^x that are distant
or (ii) from extra (i.e., external) contexts t^y. Experiments on sentence
modeling with zero-context (sentiment analysis), single-context (textual
entailment) and multiple-context (claim verification) demonstrate the
effectiveness of ATTCONV in sentence representation learning with the
incorporation of context. In particular, attentive convolution outperforms
attentive pooling and is a strong competitor to popular attentive RNNs.Comment: Camera-ready for TACL. 16 page
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