Effective and Efficient Knowledge-Intensive NLP

[The talk has been cancelled]

Speaker: Prof. Meng Jiang
         Department of Computer Science and Engineering
         University of Notre Dame

Title:  "Effective and Efficient Knowledge-Intensive NLP"

Date:   Thursday, 17 August 2023

Time:   10am - 12 noon

Venue:  Room 4503 (via lift 25/26), HKUST


Abstract:

Knowledge-intensive NLP tasks are the tasks that humans could not
reasonably be expected to perform without access to external knowledge
sources such as search engines, Wikipedia, dictionaries, and knowledge
bases. They include open-domain question answering, commonsense reasoning,
fact checking, etc. The state-of-the-art performance on such kinds of
tasks is achieved by knowledge-augmented NLP solutions. They look for
useful knowledge to augment the input for learning and prediction.
However, the external data are heterogeneous and created independently
from the task input; also, indexing and retrieval are expensive in time
and space. In this talk, I will introduce our recent work in EMNLP 2022,
ICLR 2023, and ACL 2023 on effective and efficient knowledge augmentation.
Since three conference tutorials in ACL/EMNLP, a successful workshop at
AAAI 2023, and an incoming workshop at KDD 2023, this area of study has
established a growing and enduring community. Please join us!


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Biography:

Meng Jiang is an Associate Professor in the Department of Computer Science
and Engineering at the University of Notre Dame. He received B.E. and PhD
from Tsinghua University. He was a visiting PhD at CMU and a postdoc at
UIUC. He is interested in data mining and natural language processing. His
data science research focuses on graph and text data for applications such
as question answering, query understanding, online education, user
modeling, and mental healthcare. He was awarded with NSF CAREER.