3 research outputs found
Punny Captions: Witty Wordplay in Image Descriptions
Wit is a form of rich interaction that is often grounded in a specific
situation (e.g., a comment in response to an event). In this work, we attempt
to build computational models that can produce witty descriptions for a given
image. Inspired by a cognitive account of humor appreciation, we employ
linguistic wordplay, specifically puns, in image descriptions. We develop two
approaches which involve retrieving witty descriptions for a given image from a
large corpus of sentences, or generating them via an encoder-decoder neural
network architecture. We compare our approach against meaningful baseline
approaches via human studies and show substantial improvements. We find that
when a human is subject to similar constraints as the model regarding word
usage and style, people vote the image descriptions generated by our model to
be slightly wittier than human-written witty descriptions. Unsurprisingly,
humans are almost always wittier than the model when they are free to choose
the vocabulary, style, etc.Comment: NAACL 2018 (11 pages
Neural approaches to spoken content embedding
Comparing spoken segments is a central operation to speech processing.
Traditional approaches in this area have favored frame-level dynamic
programming algorithms, such as dynamic time warping, because they require no
supervision, but they are limited in performance and efficiency. As an
alternative, acoustic word embeddings -- fixed-dimensional vector
representations of variable-length spoken word segments -- have begun to be
considered for such tasks as well. However, the current space of such
discriminative embedding models, training approaches, and their application to
real-world downstream tasks is limited. We start by considering ``single-view"
training losses where the goal is to learn an acoustic word embedding model
that separates same-word and different-word spoken segment pairs. Then, we
consider ``multi-view" contrastive losses. In this setting, acoustic word
embeddings are learned jointly with embeddings of character sequences to
generate acoustically grounded embeddings of written words, or acoustically
grounded word embeddings.
In this thesis, we contribute new discriminative acoustic word embedding
(AWE) and acoustically grounded word embedding (AGWE) approaches based on
recurrent neural networks (RNNs). We improve model training in terms of both
efficiency and performance. We take these developments beyond English to
several low-resource languages and show that multilingual training improves
performance when labeled data is limited. We apply our embedding models, both
monolingual and multilingual, to the downstream tasks of query-by-example
speech search and automatic speech recognition. Finally, we show how our
embedding approaches compare with and complement more recent self-supervised
speech models.Comment: PhD thesi