318 research outputs found
A Deep Architecture for Semantic Parsing
Many successful approaches to semantic parsing build on top of the syntactic
analysis of text, and make use of distributional representations or statistical
models to match parses to ontology-specific queries. This paper presents a
novel deep learning architecture which provides a semantic parsing system
through the union of two neural models of language semantics. It allows for the
generation of ontology-specific queries from natural language statements and
questions without the need for parsing, which makes it especially suitable to
grammatically malformed or syntactically atypical text, such as tweets, as well
as permitting the development of semantic parsers for resource-poor languages.Comment: In Proceedings of the Semantic Parsing Workshop at ACL 2014
(forthcoming
Fixed-Point Performance Analysis of Recurrent Neural Networks
Recurrent neural networks have shown excellent performance in many
applications, however they require increased complexity in hardware or software
based implementations. The hardware complexity can be much lowered by
minimizing the word-length of weights and signals. This work analyzes the
fixed-point performance of recurrent neural networks using a retrain based
quantization method. The quantization sensitivity of each layer in RNNs is
studied, and the overall fixed-point optimization results minimizing the
capacity of weights while not sacrificing the performance are presented. A
language model and a phoneme recognition examples are used
Scaling Recurrent Neural Network Language Models
This paper investigates the scaling properties of Recurrent Neural Network
Language Models (RNNLMs). We discuss how to train very large RNNs on GPUs and
address the questions of how RNNLMs scale with respect to model size,
training-set size, computational costs and memory. Our analysis shows that
despite being more costly to train, RNNLMs obtain much lower perplexities on
standard benchmarks than n-gram models. We train the largest known RNNs and
present relative word error rates gains of 18% on an ASR task. We also present
the new lowest perplexities on the recently released billion word language
modelling benchmark, 1 BLEU point gain on machine translation and a 17%
relative hit rate gain in word prediction
Text segmentation with character-level text embeddings
Learning word representations has recently seen much success in computational
linguistics. However, assuming sequences of word tokens as input to linguistic
analysis is often unjustified. For many languages word segmentation is a
non-trivial task and naturally occurring text is sometimes a mixture of natural
language strings and other character data. We propose to learn text
representations directly from raw character sequences by training a Simple
recurrent Network to predict the next character in text. The network uses its
hidden layer to evolve abstract representations of the character sequences it
sees. To demonstrate the usefulness of the learned text embeddings, we use them
as features in a supervised character level text segmentation and labeling
task: recognizing spans of text containing programming language code. By using
the embeddings as features we are able to substantially improve over a baseline
which uses only surface character n-grams.Comment: Workshop on Deep Learning for Audio, Speech and Language Processing,
ICML 201
Generating Steganographic Text with LSTMs
Motivated by concerns for user privacy, we design a steganographic system
("stegosystem") that enables two users to exchange encrypted messages without
an adversary detecting that such an exchange is taking place. We propose a new
linguistic stegosystem based on a Long Short-Term Memory (LSTM) neural network.
We demonstrate our approach on the Twitter and Enron email datasets and show
that it yields high-quality steganographic text while significantly improving
capacity (encrypted bits per word) relative to the state-of-the-art.Comment: ACL 2017 Student Research Worksho
- …