3,512 research outputs found
Understanding language-elicited EEG data by predicting it from a fine-tuned language model
Electroencephalography (EEG) recordings of brain activity taken while
participants read or listen to language are widely used within the cognitive
neuroscience and psycholinguistics communities as a tool to study language
comprehension. Several time-locked stereotyped EEG responses to
word-presentations -- known collectively as event-related potentials (ERPs) --
are thought to be markers for semantic or syntactic processes that take place
during comprehension. However, the characterization of each individual ERP in
terms of what features of a stream of language trigger the response remains
controversial. Improving this characterization would make ERPs a more useful
tool for studying language comprehension. We take a step towards better
understanding the ERPs by fine-tuning a language model to predict them. This
new approach to analysis shows for the first time that all of the ERPs are
predictable from embeddings of a stream of language. Prior work has only found
two of the ERPs to be predictable. In addition to this analysis, we examine
which ERPs benefit from sharing parameters during joint training. We find that
two pairs of ERPs previously identified in the literature as being related to
each other benefit from joint training, while several other pairs of ERPs that
benefit from joint training are suggestive of potential relationships.
Extensions of this analysis that further examine what kinds of information in
the model embeddings relate to each ERP have the potential to elucidate the
processes involved in human language comprehension.Comment: To appear in Proceedings of the 2019 Conference of the North American
Chapter of the Association for Computational Linguistic
A task control architecture for autonomous robots
An architecture is presented for controlling robots that have multiple tasks, operate in dynamic domains, and require a fair degree of autonomy. The architecture is built on several layers of functionality, including a distributed communication layer, a behavior layer for querying sensors, expanding goals, and executing commands, and a task level for managing the temporal aspects of planning and achieving goals, coordinating tasks, allocating resources, monitoring, and recovering from errors. Application to a legged planetary rover and an indoor mobile manipulator is described
Inferring Interpersonal Relations in Narrative Summaries
Characterizing relationships between people is fundamental for the
understanding of narratives. In this work, we address the problem of inferring
the polarity of relationships between people in narrative summaries. We
formulate the problem as a joint structured prediction for each narrative, and
present a model that combines evidence from linguistic and semantic features,
as well as features based on the structure of the social community in the text.
We also provide a clustering-based approach that can exploit regularities in
narrative types. e.g., learn an affinity for love-triangles in romantic
stories. On a dataset of movie summaries from Wikipedia, our structured models
provide more than a 30% error-reduction over a competitive baseline that
considers pairs of characters in isolation
Where People Get Information About Restaurants and Other Local Businesses
Presents survey findings about the demographics and technology profiles of those who seek information about restaurants, bars, and local businesses and their sources of information, including the Internet, newspapers, word of mouth, and local television
Digital Advertising and News: Who Advertises on News Sites and How Much Those Ads Are Targeted
Analyzes trends in advertising in twenty-two news operations, including shifts to digital advertising, use of consumer data to target ads, types of ads, and industries represented among advertisers by media type
Contextual Parameter Generation for Universal Neural Machine Translation
We propose a simple modification to existing neural machine translation (NMT)
models that enables using a single universal model to translate between
multiple languages while allowing for language specific parameterization, and
that can also be used for domain adaptation. Our approach requires no changes
to the model architecture of a standard NMT system, but instead introduces a
new component, the contextual parameter generator (CPG), that generates the
parameters of the system (e.g., weights in a neural network). This parameter
generator accepts source and target language embeddings as input, and generates
the parameters for the encoder and the decoder, respectively. The rest of the
model remains unchanged and is shared across all languages. We show how this
simple modification enables the system to use monolingual data for training and
also perform zero-shot translation. We further show it is able to surpass
state-of-the-art performance for both the IWSLT-15 and IWSLT-17 datasets and
that the learned language embeddings are able to uncover interesting
relationships between languages.Comment: Published in the proceedings of Empirical Methods in Natural Language
Processing (EMNLP), 201
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