775 research outputs found
Jointly Multiple Events Extraction via Attention-based Graph Information Aggregation
Event extraction is of practical utility in natural language processing. In
the real world, it is a common phenomenon that multiple events existing in the
same sentence, where extracting them are more difficult than extracting a
single event. Previous works on modeling the associations between events by
sequential modeling methods suffer a lot from the low efficiency in capturing
very long-range dependencies. In this paper, we propose a novel Jointly
Multiple Events Extraction (JMEE) framework to jointly extract multiple event
triggers and arguments by introducing syntactic shortcut arcs to enhance
information flow and attention-based graph convolution networks to model graph
information. The experiment results demonstrate that our proposed framework
achieves competitive results compared with state-of-the-art methods.Comment: accepted by EMNLP 201
NAS-VAD: Neural Architecture Search for Voice Activity Detection
Various neural network-based approaches have been proposed for more robust
and accurate voice activity detection (VAD). Manual design of such neural
architectures is an error-prone and time-consuming process, which prompted the
development of neural architecture search (NAS) that automatically design and
optimize network architectures. While NAS has been successfully applied to
improve performance in a variety of tasks, it has not yet been exploited in the
VAD domain. In this paper, we present the first work that utilizes NAS
approaches on the VAD task. To effectively search architectures for the VAD
task, we propose a modified macro structure and a new search space with a much
broader range of operations that includes attention operations. The results
show that the network structures found by the propose NAS framework outperform
previous manually designed state-of-the-art VAD models in various noise-added
and real-world-recorded datasets. We also show that the architectures searched
on a particular dataset achieve improved generalization performance on unseen
audio datasets. Our code and models are available at
https://github.com/daniel03c1/NAS_VAD.Comment: Submitted to Interspeech 202
Improving vision-inspired keyword spotting using dynamic module skipping in streaming conformer encoder
Using a vision-inspired keyword spotting framework, we propose an
architecture with input-dependent dynamic depth capable of processing streaming
audio. Specifically, we extend a conformer encoder with trainable binary gates
that allow us to dynamically skip network modules according to the input audio.
Our approach improves detection and localization accuracy on continuous speech
using Librispeech top-1000 most frequent words while maintaining a small memory
footprint. The inclusion of gates also reduces the average amount of processing
without affecting the overall performance. These benefits are shown to be even
more pronounced using the Google speech commands dataset placed over background
noise where up to 97% of the processing is skipped on non-speech inputs,
therefore making our method particularly interesting for an always-on keyword
spotter
- …