| Kenneth W. Church |
1195 Bordeaux Drive
Sunnyvale, CA 94089, USA
Abstract: When Minsky and Chomsky were at Harvard in the 1950s, they started out their careers questioning a number of machine learning methods that have since regained popularity. Minsky's Perceptrons was a reaction to neural nets and Chomsky's Syntactic Structures was a reaction to ngram language models. Many of their objections are being ignored and forgotten (perhaps for good reasons, and perhaps not). Future work ought to characterize what deep nets are good for (and what they aren't good for). Can we come up with a theory of generative capacity for deep nets? How much more can we generate with more layers? In practice, deep nets have been effective in vision, speech and machine translation, where (1) we have lots of data, (2) representations and scale don't matter much, and (3) nothing else has been all that effective. Conversely, deep nets are probably less appropriate when representations have been reasonably effective (e.g., symbolic calculus), or for large problems beyond finite-state complexity (e.g., sorting large lists, multiplying large matrices).
Kenneth Church's BiographyKenneth Church has worked on many topics in computational linguistics including: web search, language modeling, text analysis, spelling correction, word-sense disambiguation, terminology, translation, lexicography, compression, speech (recognition, synthesis & diarization), OCR, as well as applications that go well beyond computational linguistics such as revenue assurance and virtual integration (using screen scraping and web crawling to integrate systems that traditionally don't talk together as well as they could such as billing and customer care). He enjoys working with large corpora such as the Associated Press newswire (1 million words per week) and even larger datasets such as telephone call detail (1-10 billion records per month) and web logs. He earned his undergraduate and graduate degrees from MIT, and has worked at AT&T, Microsoft, Hopkins and IBM. He was the president of ACL in 2012, and SIGDAT (the group that organizes EMNLP) from 1993 until 2011. He became an AT&T Fellow in 2001 and ACL Fellow in 2015.
Piek Vossen |
Vrije Universiteit (VU) Amsterdam
1081 HV Amsterdam
Authors: Piek Vossen, Selene Baez, Lenka Bajčetić, and Bram Kraaijeveld
All knowledge is relative and perceptions can be wrong. Our state of mind is based on personal experiences and on what other people tell us. This may result in conflicting information, uncertainty about the truth, and alternative facts that we need to handle when processing information. We present a robot that models this relativity of knowledge and perception within social communication following the principles of the theory of mind. Theory of mind states that children, at some stage of their development (up to 48 months), become fully aware that other people's knowledge, beliefs, and perceptions can be different from theirs. This awareness is the foundation for social interaction and communication, and hence for learning through social communication. In the past, Scassellati designed a humanoid robot following the theory of mind. We take his work as our starting point for implementing these principles in a Pepper robot, in order to drive social communication. The robot uses object recognition, face detection, face recognition, voice detection, speech recognition and speech generation for its interactions. Given these capabilities, we built an interaction model that results in knowledge and information from conversation and perception stored in an external Triple store. The robot, called Leolani, learns directly from what people tell her. For representing the acquired information, we make use of the Grounded Representation and Source Perspective (GRaSP) model that was developed in the NewsReader project. GRaSP allows to store statements, but also the provenance information on the statement in terms of the actual mention of the statement in the conversation, the source of the statement, and the perspective of the source towards the statement, e.g. the emotional (like/dislike/anger/sad/happy), deontic (must/should) and epistemic (belief/denial) state. We demonstrate how the social communication of the robot is driven by hunger to acquire more knowledge from and on people, to resolve uncertainties and conflicts, and to share its awareness of the perceived environment. Likewise, the robot can make reference to the world but also to knowledge about the world and the encounters with people that yielded this knowledge. The robot combines perception and awareness of the here and now with social learning in 1 to 1 communication.