21 research outputs found
Information-Theoretic Text Hallucination Reduction for Video-grounded Dialogue
Video-grounded Dialogue (VGD) aims to decode an answer sentence to a question
regarding a given video and dialogue context. Despite the recent success of
multi-modal reasoning to generate answer sentences, existing dialogue systems
still suffer from a text hallucination problem, which denotes indiscriminate
text-copying from input texts without an understanding of the question. This is
due to learning spurious correlations from the fact that answer sentences in
the dataset usually include the words of input texts, thus the VGD system
excessively relies on copying words from input texts by hoping those words to
overlap with ground-truth texts. Hence, we design Text Hallucination Mitigating
(THAM) framework, which incorporates Text Hallucination Regularization (THR)
loss derived from the proposed information-theoretic text hallucination
measurement approach. Applying THAM with current dialogue systems validates the
effectiveness on VGD benchmarks (i.e., AVSD@DSTC7 and AVSD@DSTC8) and shows
enhanced interpretability.Comment: 12 pages, Accepted in EMNLP 202