212 research outputs found
Energy-based Self-attentive Learning of Abstractive Communities for Spoken Language Understanding
Abstractive community detection is an important spoken language understanding
task, whose goal is to group utterances in a conversation according to whether
they can be jointly summarized by a common abstractive sentence. This paper
provides a novel approach to this task. We first introduce a neural contextual
utterance encoder featuring three types of self-attention mechanisms. We then
train it using the siamese and triplet energy-based meta-architectures.
Experiments on the AMI corpus show that our system outperforms multiple
energy-based and non-energy based baselines from the state-of-the-art. Code and
data are publicly available.Comment: Update baseline
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
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