Related Work
Clarification requests have long been studied as a key repair mechanism in human dialogue (Gabsdil, 2003 ; Purver, 2004 ; Schlangen, 2004 ; Rieser and Lemon, 2006 ; Schlöder and Fernández, 2014 ; Benotti and Blackburn, 2017 , 2021 ) .
Datasets of human conversations where spontaneous clarification questions are annotated have been an important resource for data-driven research on clarification strategies. For example, Rodríguez and Schlangen ( 2004 ) proposed an annotation scheme based on Clark’s four levels of communication Clark ( 1996 ) to identify the form and function of clarification requests.
These and similar annotation schemes have been applied to small datasets (Benotti, 2009 ; Gervits et al., 2021 ; Benotti and Blackburn, 2021 ) .

At a larger scale, the TEACh dataset (Padmakumar et al., 2022 ) has been annotated by Gella et al. ( 2022 ) with dialogue acts, including acts related to clarification, and the Minecraft Dialogue Corpus (Narayan-Chen et al., 2019 ) and its extensions (Shi et al., 2022 ; Kiseleva et al., 2022 ; Mohanty et al., 2022 ) have been annotated with different types of clarification requests. However, in the latter case the dataset was augmented with further clarification questions a-posteriori , introduced asynchronously by other annotators who were asked to decide whether clarifications were needed. Aliannejadi et al. ( 2021 ) and Gao et al. ( 2022 ) adopt similar techniques for including clarification questions in dialogue data.

Recently, Madureira and Schlangen ( 2023b , a ) released CoDraw-iCR (v1 and v2), a fine-grained annotation of spontaneous clarification requests in the CoDraw dataset (Kim et al., 2019 ) . This constitutes a worthwhile resource for analysing clarification behaviour in collaborative tasks. Madureira and Schlangen ( 2023b ) exploit these annotations to train a neural model for deciding when to clarify. In contrast, we shall use them to study the relation between human and model uncertainty in Section 5 and then, in
Section 6 , propose a model for the CoDraw Drawer agent that asks clarification questions on the basis of its own uncertainty.

Model uncertainty represents a valuable signal for Natural Language Generation models to deal with the different sources of ambiguity in language use (Baan et al., 2023 ) . Earlier on, when the speech recogniser used to be a common source of errors in spoken dialogue systems, ASR confidence scores
were exploited to decide on error handling strategies such as confirmation and clarification (Skantze, 2008 ; Stoyanchev et al., 2014 , among others) .
In more recent systems, where the focus is on the next action to take in a collaborative task, a common approach consists of using model uncertainty (as measured by the entropy over the model prediction on a set of actions) to signal ambiguity and trigger the generation of clarification questions (Nguyen and Daumé III, 2019 ; Zhu et al., 2021 ; Shen and Lourentzou, 2023 ) . Without explicitly estimating uncertainty, Testoni and Bernardi ( 2021 ) propose a beam search re-ranking algorithm guided by the model intermediate predictions about the target in a referential task. Similar approaches use the probability difference between the top two predicted actions to estimate uncertainty (Chi et al., 2020 ) . Recently, Naszadi et al. ( 2023 ) investigated the alignment between predictive uncertainty and ambiguous instructions in visually-grounded communication tasks. They show that well-calibrated prediction probabilities benefit the detection of ambiguous
instructions. Differently from our work, the authors do not investigate the effectiveness of model uncertainty to generate follow-up clarification questions.

Instead of relying on model uncertainty, other proposed methods involve training external neural networks or sub-modules with the objective of deciding when to ask for clarification Madureira and Schlangen ( 2023b ); Shi et al. ( 2022 ) . Khalid et al. ( 2020 ) propose to incorporate cognitive modeling in a Reinforcement Learning framework to generate context-sensitive clarification strategies in a referential game. While a few approaches generate clarification questions from scratch in an end-to-end fashion Khalid et al. ( 2020 ); Zhu et al. ( 2021 ) , heuristics are often applied at the generation stage (Shen and Lourentzou, 2023 ; Zhang and Zhu, 2021 ; Sekulić et al., 2021 ) .
In this paper, we propose an approach to generating clarification questions based on model uncertainty estimation and compare it to alternative methods for deciding when to clarify by training on human clarification decisions.