Information-Theoretic Text Hallucination Reduction for Video-grounded Dialogue (2022)

Abstract: 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.

## introduction achieving a natural conversational agent that can do 'look' (i.e., understand what they are seeing) and 'tell' (i.e., converse what they are thinking) is desiderata in our vision-language community. by the broad application of conversational agent, it can potentially assist various subsections of our environment including education, entertainment, security, and visual or other impairments. for the natural conversation between humans and computers, a video-grounded dialogue (vgd) task @xcite has been introduced to generate adequate conversational responses to the queries of humans, while following up on video and dialogue context, which gives more challenging than traditional image-grounded or text-grounded * corresponding author dialogue tasks. to be specific, given video v , video caption c, dialogue history of past q&a pairs: @xmath0, a 1 ), ..., (q r-1 , a r-1 )}, and current r-th round question q r , vgd system is expected to make free-form answer sentence a r to given question. despite recent advancements in multimodal interactions including transformer @xcite , current vgd systems still suffer text hallucination problem, which denotes indiscriminate text-copying from input texts (i.e., question, caption, and dialogue history) to decode answer tokens, but the generated answer sentences are rather inadequate and not related to the question. this is because current vgd systems learn spurious correlations from the fact that many ground-truth answers in the dataset include partial input texts, thus they perform incorrect text-copy from input texts, namely text hallucination, even in answers where input texts are unnecessary. figure 1 gives two indiscriminate text hallucinating cases confounded by spurious correlations in vgd. as shown in figure 1 (a), for the given question 'does he place the towel and clothes anywhere?', we human identify where the man placed the towel and clothes, and if it cannot be confirmed, we give a sentence meaning 'unknown'. however, in many cases, vgd systems are optimized in situations where they could find clues in video and dialogue, so for a case that they can not find clues, they simply pretend to know the answer by copying texts from input sentences without reasoning why the question is not answerable. thus, the vgd systems depend on indiscriminate text hallucination, copying input sentences (i.e., questions, caption, dialogue), hoping the copied answer words to overlap the ground-truth words. figure 1(b ) presents another dependence on this text hallucination even in the answerable question. given the question of 'does the man wear glasses?', the current vgd system provides incorrect answer without referring to the video and focuses on pretending to know the c: a smiling person grabs a towel , and fresh clothes . then the person begins undressing. our manual studies in figure 2 give experimental evidence that the answer sentences predicted by current vgd systems @xcite are dependent on indiscriminate text hallucination. figure 2(a ) presents sentence similarity score, bleu @xcite , which computes word overlapping between (1) predicted answers and input texts (i.e., caption, dialogue and question), and (2) ground-truth answers and input texts from avsd foot_0 validation dataset. the higher scores between predicted answers and input texts explain the reliance on input texts for decoding answer tokens. we may take this for granted, but as shown in figure 2(b) , the problem gets distinguishable when collecting all the 'incorrect' 2 predictions. many failure cases (i.e., incorrect predictions) include that the predicted answers are more similar to input texts, which proves indiscriminate text hallucination without the understanding of given questions and videos. one straightforward solution to mitigate this indiscriminate text hallucination is to extend the dataset using augmentations or modulating answer descriptions to be more stereoscopic. however, the augmentation has limitations in terms of diversity and the modulated descriptions can be sometimes ad-hoc and unnecessarily extravagant. intrigued by the current overconfidence in text hallucination of vgd systems, we contrive to build text hallucination mitigating (tham) framework that mitigates feature-level hallucination effects via introducing information-theoretic regularization. tham framework incorporates text hallucination regularization (thr) loss derived from the mutual information between the response language model and the proposed hallucination lan-guage model. minimizing thr loss contributes to reducing indiscriminate text copying and boosting dialogue performances. tham validates effectiveness with steady performance gain on top of the current several runner models @xcite @xcite via a model-agnostic approach. experimental results show state-of-the-art performances on two vgd benchmarks (i.e., avsd@dstc7 and avsd@dstc8) and enhanced interpretability. ## text hallucination mitigating framework in figure 3 , to build text hallucination mitigating (tham) framework, we prepare three different language models composed of encoder-decoder pairs: