8.3.2 Mitigation Methods. While Balakrishnan et al. [6] propose to adopt tree-structured semantic
representations and add constraints on decoding, Li et al. [108] frame a reinforcement learning
problem to which they apply a bootstrapping algorithm to sample training instances and then
leverage a reward related to slot consistency. Recently, there has emerged another line of research in
task-oriented dialogue, which is to build a single end-to-end system rather than connecting several
modules (e.g., Eric and Manning [43], Madotto et al. [119, 122], Wu et al. [209]). As discussed in
previous sections of this paper, there is potential for such end-to-end systems to produce extrinsic
hallucinations, yet this remains less explored. For example, a model might generate a response
with an entity that appears out of nowhere. In the example of hotel recommendation in Hong Kong
given above, a model could generate a response such as “the hotel named raffles hotel is located in
SurveyofHallucinationinNatural Language Generation