4,321 research outputs found
SEQ^3: Differentiable Sequence-to-Sequence-to-Sequence Autoencoder for Unsupervised Abstractive Sentence Compression
Neural sequence-to-sequence models are currently the dominant approach in
several natural language processing tasks, but require large parallel corpora.
We present a sequence-to-sequence-to-sequence autoencoder (SEQ^3), consisting
of two chained encoder-decoder pairs, with words used as a sequence of discrete
latent variables. We apply the proposed model to unsupervised abstractive
sentence compression, where the first and last sequences are the input and
reconstructed sentences, respectively, while the middle sequence is the
compressed sentence. Constraining the length of the latent word sequences
forces the model to distill important information from the input. A pretrained
language model, acting as a prior over the latent sequences, encourages the
compressed sentences to be human-readable. Continuous relaxations enable us to
sample from categorical distributions, allowing gradient-based optimization,
unlike alternatives that rely on reinforcement learning. The proposed model
does not require parallel text-summary pairs, achieving promising results in
unsupervised sentence compression on benchmark datasets.Comment: Accepted to NAACL 201
Language as a Latent Variable: Discrete Generative Models for Sentence Compression
In this work we explore deep generative models of text in which the latent
representation of a document is itself drawn from a discrete language model
distribution. We formulate a variational auto-encoder for inference in this
model and apply it to the task of compressing sentences. In this application
the generative model first draws a latent summary sentence from a background
language model, and then subsequently draws the observed sentence conditioned
on this latent summary. In our empirical evaluation we show that generative
formulations of both abstractive and extractive compression yield
state-of-the-art results when trained on a large amount of supervised data.
Further, we explore semi-supervised compression scenarios where we show that it
is possible to achieve performance competitive with previously proposed
supervised models while training on a fraction of the supervised data.Comment: EMNLP 201
Adapting End-to-End Speech Recognition for Readable Subtitles
Automatic speech recognition (ASR) systems are primarily evaluated on
transcription accuracy. However, in some use cases such as subtitling, verbatim
transcription would reduce output readability given limited screen size and
reading time. Therefore, this work focuses on ASR with output compression, a
task challenging for supervised approaches due to the scarcity of training
data. We first investigate a cascaded system, where an unsupervised compression
model is used to post-edit the transcribed speech. We then compare several
methods of end-to-end speech recognition under output length constraints. The
experiments show that with limited data far less than needed for training a
model from scratch, we can adapt a Transformer-based ASR model to incorporate
both transcription and compression capabilities. Furthermore, the best
performance in terms of WER and ROUGE scores is achieved by explicitly modeling
the length constraints within the end-to-end ASR system.Comment: IWSLT 202
Multilingual Unsupervised Sentence Simplification
Progress in Sentence Simplification has been hindered by the lack of
supervised data, particularly in languages other than English. Previous work
has aligned sentences from original and simplified corpora such as English
Wikipedia and Simple English Wikipedia, but this limits corpus size, domain,
and language. In this work, we propose using unsupervised mining techniques to
automatically create training corpora for simplification in multiple languages
from raw Common Crawl web data. When coupled with a controllable generation
mechanism that can flexibly adjust attributes such as length and lexical
complexity, these mined paraphrase corpora can be used to train simplification
systems in any language. We further incorporate multilingual unsupervised
pretraining methods to create even stronger models and show that by training on
mined data rather than supervised corpora, we outperform the previous best
results. We evaluate our approach on English, French, and Spanish
simplification benchmarks and reach state-of-the-art performance with a totally
unsupervised approach. We will release our models and code to mine the data in
any language included in Common Crawl
The Unsupervised Acquisition of a Lexicon from Continuous Speech
We present an unsupervised learning algorithm that acquires a
natural-language lexicon from raw speech. The algorithm is based on the optimal
encoding of symbol sequences in an MDL framework, and uses a hierarchical
representation of language that overcomes many of the problems that have
stymied previous grammar-induction procedures. The forward mapping from symbol
sequences to the speech stream is modeled using features based on articulatory
gestures. We present results on the acquisition of lexicons and language models
from raw speech, text, and phonetic transcripts, and demonstrate that our
algorithm compares very favorably to other reported results with respect to
segmentation performance and statistical efficiency.Comment: 27 page technical repor
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