944 research outputs found
Learning weakly supervised multimodal phoneme embeddings
Recent works have explored deep architectures for learning multimodal speech
representation (e.g. audio and images, articulation and audio) in a supervised
way. Here we investigate the role of combining different speech modalities,
i.e. audio and visual information representing the lips movements, in a weakly
supervised way using Siamese networks and lexical same-different side
information. In particular, we ask whether one modality can benefit from the
other to provide a richer representation for phone recognition in a weakly
supervised setting. We introduce mono-task and multi-task methods for merging
speech and visual modalities for phone recognition. The mono-task learning
consists in applying a Siamese network on the concatenation of the two
modalities, while the multi-task learning receives several different
combinations of modalities at train time. We show that multi-task learning
enhances discriminability for visual and multimodal inputs while minimally
impacting auditory inputs. Furthermore, we present a qualitative analysis of
the obtained phone embeddings, and show that cross-modal visual input can
improve the discriminability of phonological features which are visually
discernable (rounding, open/close, labial place of articulation), resulting in
representations that are closer to abstract linguistic features than those
based on audio only
Encoding of phonology in a recurrent neural model of grounded speech
We study the representation and encoding of phonemes in a recurrent neural
network model of grounded speech. We use a model which processes images and
their spoken descriptions, and projects the visual and auditory representations
into the same semantic space. We perform a number of analyses on how
information about individual phonemes is encoded in the MFCC features extracted
from the speech signal, and the activations of the layers of the model. Via
experiments with phoneme decoding and phoneme discrimination we show that
phoneme representations are most salient in the lower layers of the model,
where low-level signals are processed at a fine-grained level, although a large
amount of phonological information is retain at the top recurrent layer. We
further find out that the attention mechanism following the top recurrent layer
significantly attenuates encoding of phonology and makes the utterance
embeddings much more invariant to synonymy. Moreover, a hierarchical clustering
of phoneme representations learned by the network shows an organizational
structure of phonemes similar to those proposed in linguistics.Comment: Accepted at CoNLL 201
Scientific Information Extraction with Semi-supervised Neural Tagging
This paper addresses the problem of extracting keyphrases from scientific
articles and categorizing them as corresponding to a task, process, or
material. We cast the problem as sequence tagging and introduce semi-supervised
methods to a neural tagging model, which builds on recent advances in named
entity recognition. Since annotated training data is scarce in this domain, we
introduce a graph-based semi-supervised algorithm together with a data
selection scheme to leverage unannotated articles. Both inductive and
transductive semi-supervised learning strategies outperform state-of-the-art
information extraction performance on the 2017 SemEval Task 10 ScienceIE task.Comment: accepted by EMNLP 201
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