ASER: A Large-scale Eventuality Knowledge graph Yangqiu song Department of CSE, HKUST, Hong Kong Summer 2019 香港科技大學 THE HONG KONG UNIVERSITY OF SCIENCE AND TECHNOLOGY Contributors to related works: Hongming Zhang Xin Liu, Haojie Pan, Cane Leung, Hantian Ding
ASER: A Large-scale Eventuality Knowledge Graph Yangqiu Song Department of CSE, HKUST, Hong Kong Summer 2019 Contributors to related works: Hongming Zhang, Xin Liu, Haojie Pan, Cane Leung, HantianDing 1
Outline Motivation: NLP and commonsense knowledge Consideration: selectional preference New proposal: large-scale and higher-order selectional preference Evaluation and Applications
Outline • Motivation: NLP and commonsense knowledge • Consideration: selectional preference • New proposal: large-scale and higher-order selectional preference • Evaluation and Applications 2
Understanding humans language requires complex knowledge Crucial to comprehension is the knowledge that the reader brings to the text. The construction of meaning depends on the reader's knowledge of the language the structure of texts, a knowledge of the subject of the reading, and a broad-based background or world knowledge, Day and Bamford, 1998) Contexts and knowledge contributes to the meanings https://www.thoughtco.com/world-knowledge-language-studies-1692508
Understanding human’s language requires complex knowledge • "Crucial to comprehension is the knowledge that the reader brings to the text. The construction of meaning depends on the reader's knowledge of the language, the structure of texts, a knowledge of the subject of the reading, and a broad-based background or world knowledge.” (Day and Bamford, 1998) • Contexts and knowledge contributes to the meanings https://www.thoughtco.com/world-knowledge-language-studies-1692508 3
Knowledge is crucial to Nlu · Linguistic knowledge The task is part-of-speech(PoS)tagging with limited or no training data Suppose we know that each sentence should have at least one verb and at least one noun and would like our model to capture this constraint on the unlabeled sentences. Example from Posterior Regularization, Ganchev et al 2010,MLR) Contextual background knowledge: conversational implicature A: Is the player wearing a uniform? B: Ye A: Do he have baseball gear? B:Yes,a glove and a ball is in his hand. A: They are in the basehallfiel B:Yes A: He is wearing a B: Yes A: How the weather A Do you see a baseball ball? B: Yes it's in his hand A: The umpire is in the picture? A: The batter is in the picture? Xample taking from VisDi Do you see the fans? Ground Truth I a pitcher is leaning back about to throw a ball B:No (Das et al., 2017)4
Knowledge is Crucial to NLU • Linguistic knowledge: • “The task is part-of-speech (POS) tagging with limited or no training data. Suppose we know that each sentence should have at least one verb and at least one noun, and would like our model to capture this constraint on the unlabeled sentences.” (Example from Posterior Regularization, Ganchev et al., 2010, JMLR) • Contextual/background knowledge: conversational implicature Example taking from VisDial (Das et al., 2017) 4
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When you are asking Siri… Interacting with human involves a lot of commonsense knowledge • Space • Time • Location • State • Causality • Color • Shape • Physical interaction • Theory of mind • Human interactions • … Judy Kegl, The boundary between word knowledge and world knowledge, TINLAP3, 1987 Ernie Davis, Building AIs with Common Sense, Princeton Chapter of the ACM, May 16, 2019 5