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Fourth International Workshop on Symbolic-Neural Learning (SNL-2020)
June 30-July 1, 2020
Osaka International Convention Center 12F (Osaka, Japan)
SNL-2021 will be held online.
The organizing and program committees have decided to cancel SNL-2020 considering the global hardships from the COVID-19 pandemic.
It was a hard decision, but we concluded that the cancellation is the best option for the safety and health of all workshop attendees.
We wish to have SNL-2021 around the same time next year when everything settles down.
We look forward to meeting you at SNL-2021 in Osaka.
With warmest wishes for your and your family's health,
SNL-2020 Organizing and Program committees
Symbolic-neural learning involves deep learning methods in combination with symbolic structures. A "deep learning method" is taken to be a learning process based on gradient descent on real-valued model parameters. A "symbolic structure" is a data structure involving symbols drawn from a large vocabulary; for example, sentences of natural language, parse trees over such sentences, databases (with entities viewed as symbols), and the symbolic expressions of mathematical logic or computer programs.
Symbolic-neural learning has an innovative feature that allows to model interactions between different modals: speech, vision, and language. Such multimodal information processing is crucial for realizing research outcomes in real-word.
For growing needs and attention to multimodal research, SNL workshop this year features researches on "Beyond modality: Researches across speech, vision, and language boundaries."
Topics of interests include, but are not limited to, the following areas:
- Speech, vision, and natural language interactions in robotics
- Multimodal and grounded language processing
- Multimodal QA and translation
- Dialogue systems
- Language as a mechanism to structure and reason about visual perception
- Image caption generation and image generation from text
- General knowledge question answering
- Reading comprehension
- Textual entailment
Deep learning systems across these areas share various architectural ideas. These include word and phrase embeddings, self-attention neural networks, recurrent neural networks (LSTMs and GRUs), and various memory mechanisms. Certain linguistic and semantic resources may also be relevant across these applications. For example, dictionaries, thesauri, WordNet, FrameNet, FreeBase, DBPedia, parsers, named entity recognizers, coreference systems, knowledge graphs and encyclopedias.
The workshop consists of invited oral presentations and contributed poster presentations.
Organizing Committee:
| Yasushi Yagi (Chair) |
Osaka University, Osaka, Japan |
| Yuki Arase |
Osaka University, Osaka, Japan |
Sadaoki Furui |
Toyota Technological Institute at Chicago, Chicago, USA |
| Tomoko Matsui |
The Institute of Statistical Mathematics, Tokyo, Japan |
| David McAllester |
Toyota Technological Institute at Chicago, Chicago, USA |
| Yutaka Sasaki (treasurer) |
Toyota Technological Institute, Nagoya, Japan |
| Koichi Shinoda |
Tokyo Institute of Technology, Tokyo, Japan |
| Masashi Sugiyama |
RIKEN Center for AIP and the University of Tokyo, Tokyo, Japan |
Jun'ichi Tsujii |
AIST AI Research Center, Tokyo, Japan and
the University of Manchester, Manchester, UK |
Program Committee:
| Yuki Arase (Chair) |
Osaka University, Osaka, Japan |
| Nakamasa Inoue |
Tokyo Institute of Technology, Tokyo, Japan |
Daichi Mochihashi |
The Institute of Statistical Mathematics, Tokyo, Japan |
| Greg Shakhnarovich |
Toyota Technological Institute at Chicago, Chicago, USA |
| Hiroya Takamura |
AIST AI Research Center, Tokyo, Japan and Tokyo Institute of Technology, Tokyo, Japan |
| Norimichi Ukita |
Toyota Technological Institute, Nagoya, Japan |
| Kazuyoshi Yoshii |
RIKEN Center for AIP and Kyoto University, Kyoto, Japan |
Local Arrangements Committee
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