Kenneth Forbus

Also published as: Kenneth D. Forbus


NICE: Neural Image Commenting with Empathy
Kezhen Chen | Qiuyuan Huang | Daniel McDuff | Xiang Gao | Hamid Palangi | Jianfeng Wang | Kenneth Forbus | Jianfeng Gao
Findings of the Association for Computational Linguistics: EMNLP 2021

Emotion and empathy are examples of human qualities lacking in many human-machine interactions. The goal of our work is to generate engaging dialogue grounded in a user-shared image with increased emotion and empathy while minimizing socially inappropriate or offensive outputs. We release the Neural Image Commenting with Empathy (NICE) dataset consisting of almost two million images and the corresponding human-generated comments, a set of human annotations, and baseline performance on a range of models. In-stead of relying on manually labeled emotions, we also use automatically generated linguistic representations as a source of weakly supervised labels. Based on these annotations, we define two different tasks for the NICE dataset. Then, we propose a novel pre-training model - Modeling Affect Generation for Image Comments (MAGIC) - which aims to generate comments for images, conditioned on linguistic representations that capture style and affect, and to help generate more empathetic, emotional, engaging and socially appropriate comments. Using this model we achieve state-of-the-art performance on one of our NICE tasks. The experiments show that the approach can generate more human-like and engaging image comments.


Neural Symbolic Machines: Learning Semantic Parsers on Freebase with Weak Supervision
Chen Liang | Jonathan Berant | Quoc Le | Kenneth D. Forbus | Ni Lao
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Harnessing the statistical power of neural networks to perform language understanding and symbolic reasoning is difficult, when it requires executing efficient discrete operations against a large knowledge-base. In this work, we introduce a Neural Symbolic Machine, which contains (a) a neural “programmer”, i.e., a sequence-to-sequence model that maps language utterances to programs and utilizes a key-variable memory to handle compositionality (b) a symbolic “computer”, i.e., a Lisp interpreter that performs program execution, and helps find good programs by pruning the search space. We apply REINFORCE to directly optimize the task reward of this structured prediction problem. To train with weak supervision and improve the stability of REINFORCE, we augment it with an iterative maximum-likelihood training process. NSM outperforms the state-of-the-art on the WebQuestionsSP dataset when trained from question-answer pairs only, without requiring any feature engineering or domain-specific knowledge.


NULEX: An Open-License Broad Coverage Lexicon
Clifton McFate | Kenneth Forbus
Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies


Analogical Dialogue Acts: Supporting Learning by Reading Analogies
David Barbella | Kenneth Forbus
Proceedings of the NAACL HLT 2010 First International Workshop on Formalisms and Methodology for Learning by Reading