Conferences, Lectures, & Seminars
Events for August
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NL Seminar-MODELLING THE INTERPLAY OF METAPHOR AND EMOTION, AND A PEEK AT THE UNDERLYING COGNITIVE MECHANISMS
Thu, Aug 08, 2019 @ 11:00 AM - 12:00 PM
Information Sciences Institute
Conferences, Lectures, & Seminars
Speaker: Ekaterina Shutova, Univ of Amsterdan
Talk Title: MODELLING THE INTERPLAY OF METAPHOR AND EMOTION, AND A PEEK AT THE UNDERLYING COGNITIVE MECHANISMS
Series: Natural Language Seminar
Abstract: Besides making our thoughts more vivid and filling our communication with richer imagery, metaphor plays a fundamental structural role in our cognition, helping us to organise and project knowledge. For example, when we say "a well-oiled political machine", we view the concept of political system in terms of a mechanism and transfer inferences from the domain of mechanisms onto our reasoning about political processes. Much previous research on metaphor in linguistics and psychology suggests that metaphorical phrases tend to be more emotionally evocative than their literal counterparts. In this talk, I will present our recent work investigating the relationship between metaphor and emotion within a computational framework, by proposing the first joint model of these phenomena. We experiment with several multitask learning architectures for this purpose and demonstrate that metaphor identification and emotion prediction mutually benefit from joint learning, advancing the state of the art in both of these tasks.
In the second half of the talk, I will discuss how general-purpose semantic representations can be used to better understand metaphor processing in the human brain. In a series of experiments, we evaluate a range of semantic models word embeddings, compositional models, visual and multimodal models in their ability to decode brain activity associated with reading of literal and metaphoric sentences. Our results point to interesting differences in the processing of metaphorical and literal language.
Biography: Ekaterina Shutova is an Assistant Professor at the Institute for Logic, Language and Computation at the University of Amsterdam. Her research is in the area of natural language processing with a specific focus on computational semantics, figurative language processing, multilingual NLP and cognitively driven semantics. Previously, she worked at the University of Cambridge Computer Laboratory and the International Computer Science Institute and the Institute for Cognitive and Brain Sciences at the University of California, Berkeley. She received her PhD in Computer Science from the University of Cambridge in 2011.
Host: Xusen Yin
More Info: https://nlg.isi.edu/nl-seminar
Webcast: https://bluejeans.com/s/Mws0S/Location: Information Science Institute (ISI) - CR #1014-Multi Purpose Rm
WebCast Link: https://bluejeans.com/s/Mws0S/
Audiences: Everyone Is Invited
Contact: Peter Zamar
Event Link: https://nlg.isi.edu/nl-seminar
This event is open to all eligible individuals. USC Viterbi operates all of its activities consistent with the University's Notice of Non-Discrimination. Eligibility is not determined based on race, sex, ethnicity, sexual orientation, or any other prohibited factor. -
NL Seminar-COMPREHENSIBLE CONTEXT-DRIVEN TEXT GAME PLAYING
Thu, Aug 15, 2019 @ 11:00 AM - 12:00 PM
Information Sciences Institute
Conferences, Lectures, & Seminars
Speaker: Xusen Yin, USC/ISI
Talk Title: COMPREHENSIBLE CONTEXT-DRIVEN TEXT GAME PLAYING
Series: Natural Language Seminar
Abstract: In order to train a computer agent to play a text based computer game, we must represent each hidden state of the game. A Long Short Term Memory LSTM model running over observed texts is a common choice for state construction. However, a normal Deep Q learning Network DQN for such an agent requires millions of steps of training or more to converge. As such, an LSTM based DQN can take tens of days to finish the training process. Though we can use a Convolutional Neural Network CNN as a text encoder to construct states much faster than the LSTM, doing so without an understanding of the syntactic context of the words being analyzed can slow convergence. In this paper, we use a fast CNN to encode position and syntax-oriented structures extracted from observed texts as states. We additionally augment the reward signal in a universal and practical manner. Together, we show that our improvements can not only speed up the process by one order of magnitude but also learn a superior agent.
Biography: Xusen Yin is a 3rd-year Ph.D. student in USC ISI, advised by Dr. Jonathan May.
Host: Xusen Yin and Jon May
More Info: https://nlg.isi.edu/nl-seminar
Webcast: https://bluejeans.com/s/qXurzLocation: Information Science Institute (ISI) - CR #689
WebCast Link: https://bluejeans.com/s/qXurz
Audiences: Everyone Is Invited
Contact: Peter Zamar
Event Link: https://nlg.isi.edu/nl-seminar
This event is open to all eligible individuals. USC Viterbi operates all of its activities consistent with the University's Notice of Non-Discrimination. Eligibility is not determined based on race, sex, ethnicity, sexual orientation, or any other prohibited factor.
