Past Events

Qualifying Exam Can Document Statistics Disambiguate Grounded Language: The Case of PP attachment
× Export Event Save iCal Send to Google Calendar Send to Yahoo Calendar Send to Outlook Live Send to Microsoft Outlook Save iCal Preserve formatting in description (only supported in some calendar applications) Close Thursday, May 27, 2021, 10:00am - 11:30am

Speaker: John Blackmore

Location : Remote via Webex

Committee: 

Prof. Matthew Stone (advisor)

Prof. Martin Farach-Colton

Prof. Karl Santos

Prof. Yongfeng Zhang

Event Type: Qualifying Exam

Abstract: Advancements in the coverage and efficacy of pre-trained language models have paved the way for future research in natural language processing by making it much easier generalize from small hand-annotated datasets. In cases that require inputs from situational context, however, pre-trained models may not reliably learn to disambiguate grounded language. Using the case of PP attachment in image captions, I assess the learning potential of pre-trained statistical language models toward resolving ambiguity in image captions. My results show that pre-trained models may lack the context required to resolve the ambiguity in many cases, but traditional NLP approaches can be combined with statistical models for grounded learning.

: 

https://rutgers.webex.com/rutgers/j.php?MTID=m893d97c7b3cd0c092a8d8a5360666aaf