Aligning Predictive Uncertainty with Clarification Questions in Grounded Dialog
Authors: Kata Naszadi
Putra Manggala
Christof Monz
Venue: EMNLP 2023 Findings (2023)
Keywords: predictive uncertainty
calibration
grounded dialog
clarification question
instruction following
collaborative dialog

Abstract: Asking for clarification is fundamental to effective collaboration. 
An interactive artificial agent must know when to ask a human instructor for more information in order to ascertain their goals. 
Previous work bases the timing of questions on supervised models learned from interactions between humans. 
Instead of a supervised classification task, we wish to ground the need for questions in the acting agent's predictive uncertainty.  In this work, we investigate if ambiguous linguistic instructions can be aligned with uncertainty in neural models. We train an agent using the T5 encoder-decoder architecture to solve the Minecraft Collaborative Building Task and identify uncertainty metrics that achieve better distributional separation between clear and ambiguous instructions.  We further show that well-calibrated prediction probabilities benefit the detection of ambiguous instructions.  Lastly, we provide a novel empirical analysis on the relationship between uncertainty and dialog history length and highlight an important property that poses a difficulty for detection.

Aligning Predictive Uncertainty with Clarification Questions in Grounded Dialog
Kata Naszádi and Putra Manggala² and Christof Monz!.
'Language Technology Lab 2 University of Amsterdam
k naszadieuva..n
Abstract
search and dialog systems. Past approaches can be grouped into two paradigms, where a separate model decides et al., 2021; Song et al., 2009), and where the need for clarification is based on a model that would eventually generate the response (Arabzadeh et al., 2022). In the former, separate training set is needed with annotations that indicate ambigu- ity (Aliannejadi al., 2021). In the latter, no such signal is needed the system uses an estimate of quality of its own predictive performance to decide.
Asking for clarification fundamental to ef- fective collaboration. An interactive artificial agent must know when to ask human instruc- tor for more information order to ascertain their goals. Previous work bases the timing of questions on supervised models learned from interactions between humans. Instead of su- pervised classification task, we wish to ground the need for questions in the acting agent's pre- dictive uncertainty. In this work, investi- gate if ambiguous linguistic instructions can be aligned with uncertainty neural models. We train agent using the T5 encoder-decode architecture to solve the Minecraft Collabo- rative Building Task and identify uncertainty metrics that achieve better distributional separa- tion between clear and ambiguous instructions. We further show that well-calibrated prediction probabilities benefit the detection of ambiguous instructions. Lastly, we provide novel empiri- cal analysis the relationship between uncer- tainty and dialog history length and highlight an important property that poses a difficulty detection.
follow-up is necessary (Kim
The same problem can be posed in the context of visually grounded dialog. The domain of task- oriented communication in visual environment is less mature, and the need for questions is of- ten hard-coded in these systems (Das al., 2017; Greco et al., 2022; Lee et al., 2018). Neverthe- less, we can already find examples of both afore- mentioned paradigms, where a binary classifier is learned based on human-demonssrations (Shekhar et al., 2019), and systems that decide on the for questions based on its uncertainty (Abbasne- jad al., 2019; Chi et al., 2020).
1 Introduction
Previous works argued that a model that gener- ates information-secking questions should not be decoupled from the model that makes the final pre- diction for the task (Testoni and Bernardi, 2021; Abbasnejad et al., 2019). The model uncertainty over class labels be used to decide on the con- tent of the clarification question.
Human language offers a natural interface between machine and human when solving collaborative task. We wish to build an artificial agent that can not only ground natural language in its environ- ment, but can also solicit more information when communication fails or the human's 's instruction was ambiguous. Asking questions establishes two- way collaborative communication instead of silent following of instructions. Endowing an instruction- following agent with the ability to prompt questions poses two related but different problems: when to initiate asking question and what the content of the question should be. In this work, we focus on the first problem.
We use the same reasoning in our work, since the acting agent's uncertainty, used to decide when to ask question, is more interpretable than a black- box model that is trained to make positive predic- tion if human decided to ask a question.
Our setup involves a human architect agent and an artificial builder agent situated in a 3D Grid- world environment. The human architect agent has access to target structure and can direct the artificial builder agent with natural language.
The problem of deciding if system should be asking clarification questions has been previ- ously discussed in the context of conversational
A natural way of evaluating if an instruction-

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following agent is asking questions at the right time is to use human evaluation, which is labor- intensive. In this work, we perform evaluation us- ing already existing datasets (Narayan- et al., 2019; Kiseleva et al., 2022a). We investigate the builder model's uncertainty is higher for instruc- tions that precede questions in these human-human dialogues.
dominant source of communication errors in this task is under-speciffcation, e-g., the architect does not specify the color 3, or number of blocks. Other errors stem from mistakes in referring expressions that apply to multiple structures on grid, or none of them, e.g., using the wrong color. We subsume all of these cases that lead to clarification requests under "ambiguity".
Our contributions are:
A Ambiguous instructions are out-of domain data. In our model, we cast ambiguous instructions as out- out-of-domain data and treat clear instructions as in-domain. To achieve this, our agent learns from demonstrations where the linguistic input deemed actionable by humans. At test time, we present the agent with both clear and ambiguous utterances and measure its predictive uncertainty over the action space (Arora et al., 2021). Section 4 discusses a simple single-word messaging- game to illustrate how ambiguous messages can be viewed as out-of-domain examples in grounded commu- nicative setting.
1.
We identify the uncertainty metrics that achieve the largest distributional separation between ambiguous and non-ambiguous in- structions.
2. We show that calibration helps increase the statistical separation between the uncertainty metrics of ambiguous and non-ambiguous in- structions.
3. We provide novel empirical analysis on the relationship between uncertainty and dialog history length. We show uncertainty increases as length increases, confounding the statistical separation that is important for detecting the need for questions. This poses an inherent difficulty as in collaborative dialogues, the history grows over the course of interaction.
2.2 Instruction following as prediction
In our first experimental setting (Section 4), we model our agent using binary classification model. We use negative log- liglleilihood (NLL) and the prob- ability of the predicted label (one-best probability) as uncertainty metrics.
2 Methodology
We begin by defining ambiguous instructions and propose casting them as out-of-domain input in our modelling effort. We then describe our proba- bilistic model and the uncertainty metrics used for detecting the need for questions.
In our second experimental setting (Section 5), model our agent using structured prediction (autoregressive) model. Using beam-decoding, the builder agent produces a sequence of actions that involves placing and removing colored blocks.
In this study, we use unsupervised methods for uncertainty estimation: We do not examples of human questions in order to create model for uncertainty. Instead, we obtain our measures from statistics that are obtained from the predictive prob- ability distribution of our model.
After each sequence prediction, we have access to multiple statistics from the beam: the likelihood of all the hypothesis in the beam, the likelihood of each token and the probability distribution over the whole vocabulary for each token in the beam. Previous work has investigated a number of ways to compute uncertainty measures from these beam- statistics (Malinin and Gales, 2021).
2.1 What are ambiguous instructions?
Ambiguous sentences allow for multiple distinct in- terpretations. Depending on the domain, previous research has further refined this notion by contrast- ing sunder-specification with "ambiguity" (Clarke et al., 2009) "ambiguity" with "inaccuracy" (Zuk- erman al., 2015).
We formulate an ensemble-based uncertainty es- timation framework which produces ensemble joint probabilities that tend to be well-calibrated (Lak- shminarayanan et al., 2017). We consider an en- semble of M models (Pr (y x; ((m))) /M where each model (m) captures the mapping between variable-length instruction x e X' and sequence of actions E y. The ensemble joint probabilitics are obtained via a token-level ensemble:
Our definition of ambiguity is based on utter- ances that prompted instruction-level clarification questions during a collaborative building game be- tween humans in a gorl-world environment. A

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Pr(y x: x:0)= = II Σ [-1 M
1
Pr (x
(1) KL-divergence--asee metries. Following Lak- shminarayanan et al. (2017). we compute the fol- lowing "disagreement" metric (1-best):
m=1
where I, the length of the output sequence and 0 = (0(1), ...,, O(m)). Our agent learns from demonstrations where linguistic imput was acted on by humans. At test time, we evaluate the model on ambiguous instructions that are not present dur- ing training. We quantify predictive uncertainty over the action space Y and investigate the distribu- tional separation between the predictive uncertainty of ambiguous and ambiguuus instructions. We use beam-search the inference stage to provide high-quality hypotheses and compute uncertainty metrics based on the top hypotheses. We indicate the number of hypotheses used by the metrics be- low by the designation N-best, where N e (1,2,5) We next present the uncertainty measures used in our second experiment.
1 M M
Σ KL (Pr(y] x; ((0) Pr(y x; xx))) m=1
(6)
We compute LL and ENT metrics for the en- semble model Pr(y x; 0) and the most accurate member of the ensemble.
Related Work
The need for clarification has received a lot of interest in the field of conversational search and dialog-systems.. In spoken dence of the speech-recognition system is popular starting point for prompting clarifications (Ayan et al., 2013; Bohus and Rudnicky, 2005). The ASR-confidence is often further refined by in- put from downstrcam processing such the out- put of natural language understanding module (Stoyanchev and Johnston, 2015; Komatani and Kawahara, 2000) or structured discourse-modedt (Schlangen, 2004). These more structured seman- tic representations often allow for custom notions of ambiguity, such as too many possible referents (Zukerman et al., 2015), contradictions in the discourse representation (Skantze, 2008).
the confi-
Likelihood-based metrics (LL). Negative log- likelihood (NLL). - log Pr(y x; 0). , is a proper scoring rule (Gneiting and Raftery, 2007), where an optimal score corresponds to perfect prediction. We compute two metrics based on NLL:
1. NLL of the top hypothesis (1-best).
2. NLL difference between the top two hypothe- ses (2-best). This has been used guide question-asking for agents in Chi al. (2020).
In recent years, we have witnessed rising pop- ularity of neural seuueence-t--sqqeeee implemen- tations of conversational (OpenAI, 2023) and situ- ated agents (Padmakumar et al., 2022; Wang et al., 2023). Given their end-to-end nature, more ap- proaches model the need for clarification in these systems as separate neural model, that is trained data labelled with ambiguity tags (Kim et al., 2021; Aliannejadi al., 2019, 2021). Closest our domain, the model for timing clarification ques- tions introduced by (Shi al., 2022) also consists of a neural binary classifier trained using the occur- rences of human clarification questions in dialog. They use the same 3D grid world and human data collection that we utilize in this work.
Entropy-based metrics (ENT). Following Ma- linin and Gales (2021), compute sequence- level Monte-Carlo approximations of entropy (5-
best):
B
Hs(Pr(y x 0))
Th Σ b=1
log Pr(y(") (b) x: 0),
((0]
where the log conditional is scaled by:
exp log Pr(((() 0)
bb
ΣE exp + log (r(((k) x; 0)
a calibration tempera- ture that is useful to adjust the contribution of lower- probability hypotheses to the entropy estimate. We also compute a token-level approximation (5-best):
The hyperparameter
Ambiguous Messages in a Lewis' Signaling Game
(4) We start our analysis with simple speaker- game. We use this setup to illustrate the relation between calibration and detection of ambiguous
(Pr(y 0))
B 1,6b)
Σ Σ Th
H (Pr(y) Yat, 0))xx) L()
(5) instructions. In this referential task, the speaker and
M 1

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clear
ambiguous
NLL
Accuracy 1.0
NLL
Accuracy
0
BASE
0.005
1.71
0.5
FEN
FEN
ENS
0.006
1.0
0.71
0.5
"triangle"
"yellow"
Table I: Average negative log- log-likelihood (NIL.) and
curacy of the baseline model (BASE) and the ensemble model (ENS) evaluated on in-domain clear instruc- tions and the out-of-domain ambiguous instructions
(b) Ambiguous message "yel- low"
Clear message "triangle"
Figure I: Examples clear and an ambiguous mes- sage in the referential game. The possible referents are yellow circle and yellow triangle
Evaluating ambiguous instructions We create ambiguous instructions for the listener by choosing message describing a property that is shared by both possible referents. See Figure for an exam- ple of a clear (a) and an example of an ambiguous (b) message. The desired model behaviour for am- biguous instructions is to have maximum predictive uncertainty. In this case, this means that the model should predict probability of 0.5 for both possible outcomes.
the listener observe ordered set of objects. The speaker chooses message to refer of these objects and the listener has correctly identify the referential target based on the message.
In our setup, only the listener agent is learned, and we restrict the task to choice between two objects: Oj and O2. Each object is described by = 2 properties and each property is one-hot vector of f 4 different values. In our visualization, the properties correspond to color and shape. The vocabulary the set of all c f properties. During training, the speaker sends a single word that identifies the target object, and the listener is rewarded it picks the correct one. See Figure for an illustration of the setting.
We evaluate the baseline (BASE) and the en- semble model (ENS) on both the clear in-domain and the ambiguous examples. The ambiguous in- structions should be considered out-of-domain, as no such examples were presented during training. As shown in Table 1, both models have the same accuracy under both conditions, but the ensemble model (ENS) has lower negative log-likelihood than the baseline (BASE) on the ambiguous ex- amples, indicating an improvement in uncertainty quantification. Figure 2, we observe that the ensemble model achieves a much better statistical separation (blue vs orange) between the one-best predicted probabilities of the in- and out-domain- data. The ensemble model's predicted probabilities also lie much closer to the expected value of 0.5.
The listening agent estimates the following bi- nary probability distribution of identification I:
((1((O,,0)),M)
where message M is one-hot vector of size v and each object is described by the concatenation of two one-hot vectors = S), where C' and S are both of size f.
This indicates that the well-calibrated uncer- tainty estimates of the ensemble model increases the efficacy of using uncertainty metrics to detect ambiguity in the message.
Implementation details of the listener In this simple simulated data experiment, we uniformly sample shapes, colors, and messages, rejecting cases where the message is ambiguous. The dataset has 960 unique unambiguous examples and we re- serve of these for evaluation.
Ambiguous Instructions in Grounded Collaborative Dialog
The listener is parameterized with a feed- forward neural network that makes a binary de- cision over the indices of the two potential target objects. We train ten models with different random seeds in order to create deep-ensemble model. As baseline (BASE), we randomly pick one of the ensemble member models since all models achieve perfect accuracy on an unseen combination of prop- erties and instructions.
For our grounded dialog setting, we focus on agents trained to build structures in 3D environments based natural language instructions from human partner. The corresponding data was collected dur- ing series of collaborative building tasks where human architect was giving directions builder in order build a target structure. During training, we present the builder agent with: (a) the relevant dialog history, including utterances from
human

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1.0
for instruction-level clarification questions. While these annotations are helpful for evaluating overall ambiguity detection performance, they preclude a fine- ineeggained analysis of agent uncertainty.
0.9
One-best probabilty
0.8
Ambiguous
To this end, we create new artificial dataset (IGLU-MINI) by manipulating the clear portion of the IGLU-MULTI dataset. We construct biguous examples using three main categories that we identified as reasons for questions in the origi- nal dataset: The color not specified, the number of blocks not specified, or there is an error when referencing the already built blocks.
No
0.7
Yes
0.5
Ensemble
Baseline
Model
Figure 2: One-best probabilities of the predicted class for the ambiguous and annammbgguuu cases for each model (ensemble vs single model baseline).
For the first two categories, we manipulate the instruction by removing the color or the number of blocks. The third type of ambiguity is created by corrupting the intended referent of referring ex- pressions by changing the color the referenced blocks. We identify instructions that contain a ref- erence to the already built grid by matching strings containing referring expressions starting with def- inite article followed by a color in the first sentence of the instruction ("the red ones" in Figure 4).
both the architect and the builder, and (b) the state of the grid built in previous steps. The builder is trained place and remove colored blocks using supervised signals from action sequences recorded from human- human interactions.
5.1 Datasets
We present examples of the resulting minimal pairs with their corresponding model output in Fig- ure 4. This way we can generate pairs of clear and ambiguous instructions: We start with clear in- struction then perform the minimal edit to make it defective. We manually checked the generated ex- amples to make sure that they remain grammatical.
We conduct our experiments on three existing datasets, and create a novel evaluation set that al- lows for fine-grained analysis of ambiguity detec- tion performance.
The Minecraft Dialogue Corpus (MDC) (Narayan- ChennnnCeeeen et al., 2019) a collection of 509 game-plays between two humans. This dataset was released with standard splits. We use the train portion of the data to train our builder agent and the test portion for evaluation. The test-set contains 1,827 clear dialog- dialog-turns turns and 177 questions.
5.2 Implementation
The builder's task is formulated sequence-to- sequence prediction task. This is achieved by con- verting the grid-state into textual description. The grid described by the sequence of actions that are necessary to build it. For example, building two green blocks diagonally corresponds to the string "put initial green block. put green I left and be- fore.", ". Actions are separated by a dot. All locations are expressed by their relative distance to the first building block. There are 6 tokens corresponding to moving in the negative or positive direction (left, right), y (lower,upper) and (before, af- ter) axes. This parameterization of grid traversal is based on the approach of Kiseleva et al. (2022b)
The 2022 IGLU NLP task data collection (IGLU-NLP) (Mohanty et al., 2022) contains single-turn interactions between two humans. The architect is presented with an already built grid state and can add a single instruction to modify the grid which is then implemented by the builder. As we are interested in phaoom-p we use this collection only for training.
IGLU-MULTI (Kiseleva et al., 2022a) is also a multi-turn data-collection similar to MDC. As is a smaller dataset with total of 667 dialog- and building turns across 120 game-plays, we reserve it for evaluation. The dataset contains 584 dialog turns for building and 83 clarification questions.
We train a 10-member ensemble model using the T5- (Raffel et al., 2020) transformer architec-
Creating ambiguous instructions
'See https///itlab..icroow http://hiiaaaioow..oooo///oredde www///iroww/challlggggeeeee challenge- 2022nneg22222/luuu 2ww.2222-n--2222221----oo 2022-r1-mhh--aasee mhhh-baseline for the starter code
Both IGLU-MULTI and MDC (Shi et al., 2022) have been annotated previous work with labels
and the GridWorld environment (Zholus et al., 2022).

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(A): start with blue
Excelenn
(A): put Time of Uhroe down
[B) puls down bine blokk at (2,4,2), (1,,0,)) (1,)))
(A): now parallel to that with one blank seld anviher Tine
( Same color?
Grehites nou Panallel t5 thst uth one blank, Space add Oulldrr) Sane color?
AA). sep
potss down blocks (-1,1,).
(-L1,0) (-1,1-1)
Figure 3: Example interaction from the IGLU-MULTI dataset with clarification question.
original
F1
NLL
ENS
Single
ENS
Single
IGLU-MULTI
0.34
0.35
0.35
0.4
MDC
0.36
0.34
0.30
0.359
Table 2: Fl defined using block accuracy and recall of the ensemble (ENS) and its strongest member (Single). While performance comparable for both models, ENS achieves lower negative log- likelihood on both datasets.
(a) Original example from the dataset,
color
ture. Five members of the ensemble are fine-tuned from a pretrained T5 small with different random seeds. In order to create higher diversity within the ensemble, we train five further T5-small models from scratch with different random initialization. Fine-tuned models achieve better accuracy, while randomly initialized models introduce greater vari- ance within the ensemble, which in turn leads to better calibration on the evaluation set. All models are trained for ten epochs, and we select the check- points with the best performance on the validation portion of the MDC dataset. We pick the best per- forming single and ensemble model respectively for further analysis 2.
stack
the east the
(h) The color of the blocks is removed from the instruction.
history
stack
blocke
(c) The referenced structure recolored from to purple.
5.3 Experiments
5.3.1 Performance clear instructions
number
Table 2 shows the performance of the ensemble and the baseline model. The performance on the block accuracy and recall matches F1 scores reported in previous work (Shekhar et al., 2019; Kiseleva et al.. 2022b). We observe a similar picture as in the messaging game in Section 4:
(d) The size of the column is removed from the instruction.
5.3.2 Detecting ambiguity at a dataset level To compare the builder's 's predictive uncertainty in the clear and ambiguous conditions, we evaluate
Figure 4: Three ambiguous instructions created with minimal edits. The starting grid presented on the left and the corresponding model output is the right.
"The implementation for the builder agents in this paper is available at https://githhh..o wtwpttthwww.ooomsu///aaaacca /anoorrtaannnnnnnnnn.. builder.

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Position
Mensure
Level
Aggr.
N-hest
First act.
ENS
Single
IGLU
MDC
Measure
Level
Agg:
N-best
ENS
Single
ENS
Single
all
ENT
ENT
SRQ
agg
5-hest
5-hest
No.
0.9211
0.92
0.80
ENT
SEQ
5-best
0.57
0.54"
0.54
0.49
SRQ
1-hest
No
0.gger
0,79er
ENT
TOK
arg
5-best
0.56"
0.50
0.63"*
0.59"
color
ENT
TOK
max
5-hest
No
0.95
2
KL
TOK
arg
5-best
1.63"*
0.59"
color
ENT
TOK
agg.
5-bost
No
0.94
0,92
3
LL
SEQ
1-hest
0.56"
0.53
0.62
0.55*
color
LL
TOK
min
5-boxt
No
0,94*
0 0.894
LL
SEQ
bebs
0.53
0.51
0.57
(a) Color-ambiguity pairs.
Position
Mensure
Level Aggr.
N-hest
First act.
ENS
Single
Table 3: Area under the ROC curve on the IGLU-
ENT
TDK
agg
5-hest
No
0.7ger
0.62
all
ENT
SRQ
5-hest
No
0.7ger
0.53
MULTI and MDC datasets.
SRQ
1-hest
No
0.7g
0.60ez
pas
ENT
TOK
1-host
Yes
0.88
0.63
agg
5-bast
0.63**
the model on all builder-turns: when the builder de- cided build a sequence of blocks in the original
eas
LL
1OK
aeg
5 hess
No
0.84
0.649
(b) aumber-ambiguity pairs.
dataset, and also when the builder asked a ques-
Position
Measure
Level
Ager.
N-bost
act.
ENS
Singlo
0 all
ENT
TOK
S-best
No
0.86*
0.7ge
tion. We compute the area under the receiver oper- ating characteristic curve to show how well each uncertainty measure separates the clear from the ambiguous architect instructions. The significance thresholds (* for p 0.05, for p < 0.01) were computed with a permutation test. The null hypoth- esis associated with this test is that the observed uncertainties come from the same distribution for the ambiguous and non-ambigun -on-ambig examples. Ta- ble 3 shows that while the ensemble model achieves slightly better separation across all uncertainty mea- sures, both models struggle to separate cases where the human participants asked question and where they decided to carry out the instruction.
ENT
SEQ
5-best
2 all
LL
SEQ
1-best
No
0.66
0,674
ENT
avg
5-hest
0,33*
4 all
ENT
TOK
5-hest
Yes
0.724
0.65
agg
5-hest
(c) History-ambiguit pairs.
Table Proportion of examples in the IGLU-MINI dataset where the ambiguous instruction has higher un- certainty than the corresponding pair according to the more calibrated ENS and the baseline Single models. Rows 0-2 show the most commonly used structured pre- diction metrics. In rows 3-5, the three best performing metrics are displayed. The ensemble model outperforms the uncalibrated single model in detecting the hike in semantic difficulty.
compute different statisties only considering these positions of the sequence.
5.3.3 Inducing ambiguity via minimal edits
When we leave out the number of blocks to be used, we expect the model to struggle with deciding if it should start new action after the end-of-action symbol. To detect this, we focus the measurements on positions where there could be an end-of of-sequence token, namely positions right after each end-of-action token.
We would like to gain better understanding what drives the changes in predictive uncertainty of our builder. The IGLU-MINI dataset offers a con- trolled environment to investigate this question. Here we have pairs of instructions: the original one can be found the human-human recordings and the builder deemed it clear enough to act upon it. The corresponding instruction has been minimally edited to increase linguistic confusion. In Figure 4 we illustrate each group of minimal changes we in- troduced. In this setup, we can ask a more targeted question: Does higher linguistic ambiguity trans- late to higher uncertainty in the builder? Instead of comparing all clear instruction with all ambiguous ones, here we can examine the uncertainty changes within each pair. In this setting, both the number of prior dialog turns and the size of the already built structure are kept constant.
We also compute all our measurements limited to only the first action of the predicted action- sequence. We expect that where the main reference point on the grid has been altered (history), most of the uncertainty change will be observed on where to place the first block.
In Table 4, we compare how many times the utterance with increased semantic difficulty has re- sulted in higher uncertainty. The table also shows the result for paired permutation test, which indi- cates if the change in the uncertainty measurements significant.
We expect each type of minimal-edit to translate differently in the predictive uncertainties. We in- troduce some new features to better capture these effects. Where we omit the color for the original instruction, we expect the change of uncertainty to concentrate on the predicted color tokens. Hence,
For each category of semantic error, different measures of uncertainty perform best. For color- ambiguity, the top performing measures only con- sider the color-predictions. In case of number- obfuscation, all best performing metrics consider

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positions where the end of sentence (BOS) could be predicted (after each action). The best perform- ing measure the entropy of the token-level dis- tribution after the very first action in each 5-best hypothesis. This shows that the models struggle to decide if a second action should be started. When we alter the color of the referenced structure. the best performing metric considers the entropy of all the color-predictions. This is explained by the model's 's reliance on the color of built structures to decide on the color.
Pearson's -0.02
Pearson's 0.32
1.0
0.07
0.06
11.
Beam entropy
0.05
0.04
0.03
0.02
0
100 200
100 200
Input length
Input length
6 Analysis
Figure 6: Correlation of the input length with the FI score (left) and the entropy of the prediction (right) for all sequences under a length of 250. While the performance is constant, the entropy of the predictions increases with the input length.
While for ambiguity minimal -aaiis,l-pairs, the ensemble model can confidently differentiate between the corresponding instructions terms of uncertainty, the within-class variance remains high, rendering it very difficult to find an overall threshold that separates clear- from ambiguous cases (Fig. 5).
entropy still increases with the input length.
1.75
1,50
0.5
Bearn entropy
1,25
0.3
1,00
0.75
0.3
0.50
0.25
0.00
0.1
No
Ambiguous
Input
Figure 7: Correlation of input length with the prediction entropy in ensemble (left) and the baseline model (right). The input length consists of token length of the dialog history and the size of the already built structures.
Figure 5: Uncertainty of the original (blue) and the corresponding ambiguous instruction (orange) for the ensemble model. While uncertainty is higher than the corresponding baseline utterance within pairs, within- class variance is very high.
7 Conclusion and Future Work
The main driver of predictive uncertainty is the length of the input sequence. Figure 7 illustrates the correlation between input length and uncertainty for both models. The input length consists of the token length of the dialog history and the size already built structures in the grid, As a result, the uncertainty of the builder steadily increases over the dialog, even if each step was understood well by the builder and no questions were asked.
In this work, we have investigated the alignment between predictive uncertainty and ambiguous in- structions in visually grounded communication. We show that calibration can help increase the effi- cacy unsupervised uncertainty metrics. However, our empirical analysis on the relationship between uncertainty and dialog history length also shows that varying input lengths can pose problem for using uncertainty metrics for detection. At the same time, these metrics do exhibit the expected rise in uncertainty when applied to detecting tar- geted ambiguity changes.
For significantly longer input sequences, the per- formance of the builder model decreases, for these cases, it is expected that uncertainty of the model rises. In Figure 6, we focus our attention on in- put sequences under the length of 250. While the performance is close to constant in this range, the
The task of detecting the need for questions this environment has been posed the frame of the

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2022 IGLU NLP challenge (Kiseleva et al., 2022b), Crucially, the evaluations of the builder agent and question asking module have been separated into two tracks. For future work, we can use the uncer- tainty metrics generated by the builder to detect the need for asking questions, removing the need for a bespoke model for the latter.
as a guide to asking goal-oriented questions. In Pro- ceedings of the IEEE/CVE Conference on Computer Vision and Pattern Recognition, pages 4155-4164.
Mohammad Aliannejadi, Julia Kiseleva, Aleksandr Chuklin, Jeff Dalton, and Mikhail Burtsev. 2021. Building and evaluating open-domain dialogue cor- pora with clarifying questions. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 4473 4484, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.
Highly varying input-lengths is an inherent fea- ture of dialogue. The uncertainty measures that we utilised in this paper, have been previously shown to be effective in detecting out-of-domain samples in POS- POS-tagging (Ulmer et al., 2022), ASR (Tu et al., 2022) and NMT (Malinin and Gales, 2021). These applications do not exhibit the same magnitude of variance in the length of the model's input.
Mohammad Aliannejadi, Hamed Zamani, Fabio Crestani, and W Bruce Croft. 2019. Asking clari- fying questions in open-domain information-seekinn conversations. In Proceedings of the 42nd interna- tional ACM SIGIR Conference on Research and De- velopment in Information Retrieval, pages 475-484,
8 Limitations
Negar Arabzadeh, Mahsa Seifikar, and Charles LA Clarke. 2022. Unsupervised question clarity pre- diction through retrieved item coherency. In Pro- ceedings of the s1 ACM International Conference on Information & Knowledge Management, pages 3811-3818
We examined one specific way of presenting the IGLU builder task data to a sequence-to-sequenee transformer, introduced in prior work. There are potentially many alternative methods for casting this data sequence-to-sequene problem, and our observations might not hold for other data- transformations or architectures.
Udit Arora, William Huang, and He He. 2021. Types of duttof-distribution texts and how to detect them. In Proceedings of the 2021 Conference on Empiri- cal Methods in Natural Language Processing, pages 10687-10701, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.
The data-collections for our considered tasks are very small compared to the scale of data used for state-of-the art conversational systems (OpenAl, 2023). It has been pointed out in related work that the calibration properties of large DNNs improve greatly with higher in-domain accuracy and more training data (Desai and Durrett, 2020).
Necip Fazil Ayan, Arindam Mandal, Michael Frandsen, Jing Zheng. Peter Blasco, Andreas Kathol, Frédéric Béchet, Benoit Favre, Alex Marin, Tom Kwiatkowski, et al. 2013. "can you give me another word for hyper- baric?": Improving speech translation using targeted clarification questions. In 2013 IEEE International Conference on Acoustics, Speech and Signal Process- ing. pages 839 1391-. IEEE.
9 Acknowledgements
We thank Taewoon Kim and Michiel van der Meer for early brainstorming on this project. We are also grateful to Alberto Testoni, Mohamed Om- and the anonymous reviewers for their valu- able feedback. This rescarch was funded in part by the Netherlands Organization for Scientific Re- search (NWO) under project number ALC.192.08) We also received funding from the Hybrid Intelli- gence Center, a 10-year programme funded by the Dutch Ministry of Education, Culture and Science through the Netherlands Organisation for Scientific Research with grant number 024.004.022.
Dan Bohus and Alexander I. Rudnicky. 2005. A prin- cipled approach for rejection threshold optimization in spoken dialog systems. In Interspeech 2005, 9th European Conference on Speech Communication and Technology, pages 2781-277 2781-2784.
Ta- Chung Chi, Minmin Shen, Mihail Eric, Scokhwan Kim, and Dilek Hakkani-tur. 2020. Just ask: An in- teractive learning framework for vision and language navigation. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 34, pages 2459- 2466.
Charles LA Clarke, Maheedhar Kolla, and Olga Vech- tomova. 2009. An effectiveness measure for am- biguous and underspecified queries. In Advances Information Retrieval Theory: Second International Conference on the Theory of Information Retrieval, ICTIR 2009 Cambridge, UK, September 12-12, 2009 Proceedings 2, pages 188-199. Springer.
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Reviews:
[{"review_id": "S87EYal7nA", "reviewer": "Reviewer_zqDm", "summary": "", "questions": "A. What was the rationale behind choosing an ensemble size of ten? Did you evaluate how the size of the ensemble impacts its performance?\nB. What is architecture of neural network and hyper-parameters used to train it?\nC. Did you consider embedding the instructions before and after the minimal edits to see how changes in their semantic similarity relate to uncertainty in the builder agent?", "limitations": "", "rating": 4, "confidence": 2, "soundness": 4, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for your valuable feedback zqDm.  We are happy to hear that you find our **empirical analysis useful**, **well-written** and **easy to follow**.  We address your questions below.\n\n\n**Question A:**\nWe use ensembles to improve the calibration[1] of our output probabilities and choose ten as our ensemble size as that is the maximum number of T5-small models that we could fit on our device for decoding.  Please see the performance of different ensemble sizes in the next answer.\n\n\n**Question B:**\nFor the Signalling game, we use an RNN with 2 hidden layers and a hidden size of 256. This setup is not sensitive to the choice of hyper-parameters – the results are substantially the same across different learning rates, optimizers, and model sizes. \nFor the IGLU building task, we use a pretrained T5-small and fine tune it five times with different random seeds. The choice of seed determines an ordering of the training data. In order to increase diversity in the ensemble, we also trained five models from scratch. We use the AdamW optimizer with a starting learning rate of 1e-5. All models are trained for 10 epochs and we use the checkpoints with the best performance on the validation set. For validation, we use a concatenation of the IGLU-MULTI dataset  and the validation portion of the MDC dataset.\n\nWe examine the performance of different ensemble sizes below.\n\n|                                        | IGLU-MULTI avg NLL |\n|----------------------------------------|------------------------|\n| 1 fine-tuned model                     | 0.41                   |\n| 3 fine-tuned models                    | 0.367                  |\n| 5 fine-tuned models                    | 0.354                  |\n| 5 fine-tuned and 5 from scratch models | 0.231                  |\n\n\nWe will  include these details on the ensemble member creation in the final manuscript. We will also release code for reproduction and the trained model checkpoints.\n\n**Question C:**\nBoth the ambiguous and unambiguous instructions are embedded with a T5-model where we prepend the dialog history and the built state. We have not examined the embedding space itself, only the shape of the output distributions in the two contrasting cases.\n\n\n[1] Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles. Lakshminarayanan et. al., 2017", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "The paper presents a useful empirical analysis of the factors that lead to ambiguity in dialog instructions and uncertainty in following them, especially how this scales with the length of the dialog history. It is well-written and easy to follow.", "reasons_to_reject": "No major reasons to reject.", "questions_to_authors": "A. What was the rationale behind choosing an ensemble size of ten? Did you evaluate how the size of the ensemble impacts its performance?\nB. What is architecture of neural network and hyper-parameters used to train it?\nC. Did you consider embedding the instructions before and after the minimal edits to see how changes in their semantic similarity relate to uncertainty in the builder agent?", "paper_topic_and_main_contributions": "This paper focuses on the issue of dealing with uncertainty in visually grounded dialog between two agents, where underspecification in instructions can lead to ambiguity in following them and accomplishing a task. The authors alter examples from the IGLU-MULTI dataset to remove certain information from the instructions and study the effect this has on the receiving agent’s understanding. It presents metrics to quantify uncertainty to help identify ambiguous instructions, shows how ambiguous and unambiguous instructions can be statistically separated, and provides an analysis on how uncertainty increases with the duration of the dialog.", "excitement": 4, "reproducibility": 3, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "JebHbYcZcV", "reviewer": "Reviewer_d5nS", "summary": "", "questions": "", "limitations": "", "rating": 2, "confidence": 3, "soundness": 3, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for your valuable feedback d5nS.  We are happy to hear that you find our work well-written, self-contained, and can potentially improve the quality of generated dialogs in practice.  We address your remarks below.\n\n\nThe main focus of this paper is not to propose a new method for detecting when to ask clarification questions, but to investigate the alignment between ambiguous linguistic inputs and the model’s predictive uncertainty.  The uncertainty metrics we use are well-studied and we seek to identify the ones that can potentially be useful for detecting when to ask clarification questions (Table 3 and Table 5) and whether calibration is important (Table 4).  To the best of our knowledge, in the grounded, multi-turn dialog setting (such as in IGLU-MULTI and MDC), this has not been investigated. \n\n\nIn this paper, we also show that the uncertainty increases with length (Figure 8), confounding the statistical separation that is important for detecting the need for questions.  This inherent difficulty is more apparent in collaborative dialogues, compared to related domains such as machine translation and automatic speech recognition. Additionally, our IGLU-MINI dataset allows analysis to be performed for each type of semantic ambiguity (Table 5a, 5b, and 5c) and we show that different measures of uncertainty attain the best performance. \n\nLastly, our analysis provides principled yet simple baselines that give a probabilistic interpretation of linguistic ambiguity in a grounded environment using sequence-to-sequence models.\nWe note that similar points have been raised by pdGz, so we will make it clear in our contributions list (Page 2, right before Section 2) that we do not seek to propose a new and concrete method, but to investigate the usefulness and behavior of these uncertainty metrics for detecting ambiguity  in the grounded, multi-turn dialog setting.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "The paper is clearly written and self-contained. The authors describe the methodology, a preliminary experiment, experiments with inconsistent results, and further in-depth study and analysis. It's very easy for readers to follow the clear storyline.\n\nThe problem the authors try to study, detecting ambiguous dialog turns, has good practical potentials in helping improve the quality of generated dialogs if solved.", "reasons_to_reject": "- The paper is not able to give a solution to the problem it tries to solve. The proposed method works in the preliminary experiment, but there are still gaps for it to work in a synthetic dataset authors create with minimal variations, let along on the original task.\n\n- The paper is using off-the-shelf uncertainty metrics and models, and the problem it trying to study is transformed into an OOD detection problem, but the paper doesn't bring any innovative ideas of solving the problem.  ", "questions_to_authors": "", "paper_topic_and_main_contributions": "The paper studies whether neural model prediction uncertainty can be used to detect ambiguous instructions in grounded collaborative dialogs. \n\nThe authors first define a set of metrics quantifying prediction uncertainties, then they model the problem as a domain detection problem, with clear instructions in-domain data which model is trained on, ambiguous ones out-of-domain data. \n\nIn experiments, the authors demonstrate in a simple synthetic binary classification task, the prediction uncertainties from a FFN well correlates with whether the instruction is ambiguous, and the uncertainty from the FFN ensemble better detects the ambiguity.  Next the authors study two visually grounded dialog datasets, using whether the listener asks a clarification question as the label of ambiguous speaker instruction, but found the finetuned T5 model (and the ensemble) can't separate the two cases. Then the authors create a new synthetic dataset by making minimal edits of unambiguous instructions to create controllable ambiguous ones, and report that it's significant that the model's prediction uncertainty on an unambiguous instruction is lower than the corresponding ambiguous one, but one can still not use uncertainty to detect ambiguous instructions due to the high within class variance. Finally the authors argue that it's because the uncertainty is highly correlated with dialog history length.\n\n", "excitement": 2, "reproducibility": 4, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "yjecWi7Y8k", "reviewer": "Reviewer_pdGz", "summary": "", "questions": "* The \"Lewis signaling game\" part seems to be redundant the complexity of the task is too limited compared to the original tasks. Why should we analyze the behavior in this toy setting ? Do you have any special observation here?\n\n* Do you have any experimental results varying the size of the training samples to see when do the uncertainty metrics become invalid? \n\n* Do you have any results with different hyper-parameters like N ∈ {1, 2, 5}?\n\n\nPost-rebuttal comments:\nThank you for providing the additional results and details. The questions are all resolved. I will update the reproducibility score.", "limitations": "", "rating": 2, "confidence": 4, "soundness": 3, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for your valuable feedback pdGz.  We are happy to hear that you find our **empirical findings useful** and our **objective important for human-machine collaboration**. We address your remarks and questions below.\n\n> The metrics are already very popular\n> the concrete method on how to use the metrics for classifying if a user request is vague is not suggested.\n\n\nThe main focus of this paper is not to propose a new method for detecting when to ask clarification questions, but to investigate the alignment between ambiguous linguistic inputs and the model’s predictive uncertainty.  The uncertainty metrics we use are well-studied and we seek to identify the ones that can potentially be useful for detecting when to ask clarification questions (Table 3) and whether calibration is important (Table 4).  To the best of our knowledge, in the structured data setting (such as in IGLU-MULTI and MDC), this has not been investigated.\n\nIn this paper, we also show that the uncertainty increases with length (Figure 8), confounding the statistical separation that is important for detecting the need for questions.  This inherent difficulty is more apparent in collaborative dialogues, compared to related domains such as machine translation and automatic speech recognition.\n\nLastly, our analysis provides principled yet simple baselines that give a probabilistic interpretation of linguistic ambiguity in a grounded environment using sequence-to-sequence models.\n\nWe note that similar points have been raised by d5nS, so we will make it clear in our contributions list (Page 2, right before Section 2) that we do not seek to propose a new and concrete method, but to investigate the usefulness and behaviour of these uncertainty metrics for detecting clarification questions in the grounded, multi-turn dialog setting.\n\n> should be more clear and the example of the manipulation should be described in the paper\n\nRegarding the IGLU-MINI data, we create the minimal pairs by string manipulations. We remove the first word that is an exact match.  For example, for a given input `Stack three red blocks on top of each red block. Put one yellow block.` The first colour (red)  is removed when the colour entity is manipulated and the first number (three) is removed when the number entity is manipulated.\n\nFor history manipulation, we first check if the instruction refers to the past by a regular expression that looks for definite articles in referring expressions in the first sentence of the last instruction. For example, “Place a green block on top of the red tower.”  refers to the built grid because “the red tower” is a referring expression with a definite article.\n\nAfter finding instructions that refer to the past, we replace all mentions of a colour with another colour in both the dialog history and the  built grid description. We will release the implementation of the data-generator for IGLU-MINI.\n\nAs mentioned in the manuscript, we manually checked our examples to make sure they are grammatical. Lastly, both baseline (Single) and the ensemble (ENS) models use the same examples, so if the dataset manipulation creates artificial advantages for detecting ambiguity, both models would benefit from it.\n\n> More details are required to reproduce the results\n\nFor the Signalling game, we use an RNN with 2 hidden layers and a hidden size of 256. This setup is not sensitive to the choice of hyper-parameters – the results are substantially the same across different learning rates, optimizers, and model sizes. \nFor the IGLU building task, we use a pretrained T5-small and fine tune it five times with different random seeds. The choice of seed determines an ordering of the training data. In order to increase diversity in the ensemble, we also trained five models from scratch. We use the AdamW optimizer with a starting learning rate of 1e-5. All models are trained for 10 epochs and we use the checkpoints with the best performance on the validation set. For validation, we use a concatenation of the IGLU-MULTI dataset  and the validation portion of the MDC dataset.\nWe will  include these details in the final manuscript. We will also release code for reproduction and the trained model checkpoints.\n\n\n# Answers to questions:\n\n>The \"Lewis signaling game\" part seems to be redundant the complexity of the task is too limited compared to the original tasks. Why should we analyze the behavior in this toy setting ? Do you have any special observation here?\n\nWe use this example as a warm-up exercise about how OOD-detection and ambiguous messages relate to each other, this helps build a clear storyline as pointed out by reviewer 2. \nFurther, the Lewis signalling game is  a popular setup for investigating basic patterns of grounded communication ([1, 2, 3]), but previous work never considered evaluation under ambiguous messages. It’s this paper's contribution to introduce this angle to the game.\n\n> Do you have any experimental results varying the size of the training samples to see when do the uncertainty metrics become invalid?\n\nOur analysis in Figure 7 shows that the influence of the input length already obfuscates the uncertainty measures for shorter inputs. We agree with the reviewer that investigating calibration properties of transformer models in different regions of input-length is an interesting and actionable next step based on our work.\n\n> Do you have any results with different hyper-parameters like N ∈ {1, 2, 5}?\n\nYes. The Single baseline model corresponds to N=1. We also evaluated the average negative log-likelihood for fewer models on the IGLU-MULTI dataset, please see the table below. The final ensemble contains 5  fine-tuned T5-small models and 5 models trained from scratch with the same architecture. The models that are trained from scratch increase diversity ensemble and improves calibration.\n\n|                                        | IGLU-MULTI avg NLL |\n|----------------------------------------|------------------------|\n| 1 fine-tuned model                     | 0.41                   |\n| 3 fine-tuned models                    | 0.367                  |\n| 5 fine-tuned models                    | 0.354                  |\n| 5 fine-tuned and 5 from scratch models | 0.231                  |\n\n\n\n[1]  Julia White, Jesse Mu, Noah D. Goodman: Learning to refer informatively by amortizing pragmatic reasoning. CogSci 2020\n\n[2] Athul Paul Jacob, Mike Lewis, Jacob Andreas: Multitasking Inhibits Semantic Drift. NAACL-HLT 2021: 5351-5366\n\n[3] Ryan Lowe, Abhinav Gupta, Jakob N. Foerster, Douwe Kiela, Joelle Pineau: On the interaction between supervision and self-play in emergent communication. ICLR 2020", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "* Detecting when to ask for clarification to the user is an important research topic since human-machine collaboration, especially with chatbots like ChatGPT is becoming very popular in the society.", "reasons_to_reject": "* The contribution of the paper is very limited. The metrics are already very popular way of measuring uncertainty in other fields like active learning and the concrete method on how to use the metrics for classifying if a user request is vague is not suggested. \n\n* The process on how to build the artificial dataset (IGLU-MINI) should be more clear and the example of the manipulation should be described in the paper since one of the main result (figure 5) can be biased when the examples in the dataset have some dataset artifacts.\n\n* More details are required to reproduce the results, such as hyper-parameters used.\n\nPost-rebuttal comments:\nI've carefully read the authors' comments and I appreciate that some details are now clarified with the comments. However, there are critical concerns remaining still. The main empirical finding based on their artificial scenarios, namely the alignment between ambiguous linguistic inputs and the model’s predictive uncertainty, sounds less likely to be transferrable to real-world setup as other reviewers also mentioned. Also, in abstract of the paper, the authors said they \"investigate if ambiguous linguistic instructions can be **detected** by uncertainty in neural models.\", but I don't think we can make significant progress regarding the topic since the paper does not suggest concrete method to **detect** when the linguistic instructions are vague. We could just partially conclude that ambiguous linguistic instructions might be **related to** the uncertainty to some extent.", "questions_to_authors": "* The \"Lewis signaling game\" part seems to be redundant the complexity of the task is too limited compared to the original tasks. Why should we analyze the behavior in this toy setting ? Do you have any special observation here?\n\n* Do you have any experimental results varying the size of the training samples to see when do the uncertainty metrics become invalid? \n\n* Do you have any results with different hyper-parameters like N ∈ {1, 2, 5}?\n\n\nPost-rebuttal comments:\nThank you for providing the additional results and details. The questions are all resolved. I will update the reproducibility score.", "paper_topic_and_main_contributions": "This paper studies unsupervised metrics that can be used to measure the uncertainty of the requests. Based on preliminary analysis and experiments on human-machine collaboration tasks where the goal is to successfully follow the instruction of users and collaboratively build a target structure, the authors show that the proposed metrics can be used to identify when to ask clarification to the user. Also, the paper presents useful empirical findings that the better calibration leads to better detection and the length of the dialogue context is correlated with the uncertainty of model predictions.", "excitement": 2, "reproducibility": 4, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"Typos_Grammar_Style_and_Presentation_Improvements": "Line 505 : Table 5 -> Figure 5."}, "extra_text": {}}, {"review_id": "ns4OxJUN05", "reviewer": "Reviewer_tDZx", "summary": "", "questions": "(1) For the ensemble model, how do you ensure the fine-tuned T5 models are different--is this just by changing the random seed?\n\nPost-rebuttal edit:\n\nI thank the authors for answering the question around implementation of the ensemble.", "limitations": "", "rating": 4, "confidence": 3, "soundness": 4, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for your valuable feedback tDZx. We are happy to hear that you appreciate the **advantages of unsupervised techniques in semantic ambiguity detection** and **find our empirical analysis extensive**. We address your remarks and questions below.\n\n**Remark 1 and Question 1:**\n\n> I would have liked to see more detail on how an ensemble is implemented with the T5 pretrained model, \n\n> (1) For the ensemble model, how do you ensure the fine-tuned T5 models are different--is this just by changing the random seed?\n\nWe fine tune the T5 model five times with different random seeds. The choice of seed determines an ordering of the training data. In order to increase diversity in the ensemble, we also trained five models from scratch. We use the AdamW optimizer with a starting learning rate of $1e-5$ . All models are trained for 10 epochs and we use the checkpoints with the best performance on the validation set. For validation, we use a concatenation of the IGLU-MULTI dataset  and the validation portion of the MDC dataset. As the table below indicates, this is crucial for better calibration.\n\nWe will add more details about the ensemble creation in the final manuscript. The code with all the random seeds and model checkpoints necessary to exactly reproduce the results will be released.\n\n|                                        | IGLU-MULTI avg NLL |\n|----------------------------------------|------------------------|\n| 1 fine-tuned model                     | 0.41                   |\n| 3 fine-tuned models                    | 0.367                  |\n| 5 fine-tuned models                    | 0.354                  |\n| 5 fine-tuned and 5 from scratch models | 0.231                  |\n\n**Remark 2:**\n\n> much reference made to existing techniques on detecting out-of-domain inputs outside of the introduction, so it is hard to compare how novel / effective the proposed metrics are.\n\nWe are not aware of other datasets with a combination of properties of grounded dialog task that we consider in this paper: free-form, multi-turn natural language dialog containing questions, with grounding from another modality, and with a well-defined task goal. Hence, the probabilistic measures we investigate for the purpose of detecting ambiguity have not been previously evaluated in such a setting. However, they have been used for OOD-detection in other scenarios with varying results depending on the nature of distribution shift in the data ([1], [2], [3]). We currently have these works in the future work section, but we will make sure to discuss them in the related work of the manuscript.\n\n\n**Remark 3:**\n\nWe agree with the reviewer that there can be varying degrees of ambiguity in each dialog turn. Some might be easier to resolve with further pragmatic reasoning than others. In the unaltered natural language dialogs (MDC and IGLU-MULTI) there might be an inherent variation between human preferences about what degree of ambiguity prompts them to ask questions. We do not currently address this problem in this paper and assume that on average, clarification questions indicate ambiguous inputs. This also applies for the artificial manipulations in the IGLU-MINI dataset. Some of the introduced ambiguities present more difficulty than others depending on the context. We briefly discuss, for example, that the model learns to use the most recent colour when the colour is not mentioned in the last dialog turn. We do not attempt to break down the dataset based on levels of ambiguity, but we assume that on average, we introduce higher levels of ambiguity. This is confirmed by the uncertainty estimates of the ensemble model.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "(1) The proposed method has greater interpretability than using a separate classifier to predict uncertainty, as the uncertainty is derived from the generative model's own probability distribution.\n\n(2) The proposed method is unsupervised, so does not require labelled examples of uncertainty.\n\n(3) The proposed method leverages properties that can be readily computed on most off-the-shelf generative transformers (e.g. NLL), so could easily be applied to many existing use cases.\n\n(4) There is extensive empirical detail provided to support the paper's claims.", "reasons_to_reject": "(1) I would have liked to see more detail on how an ensemble is implemented with the T5 pretrained model, i.e. given each model starts from the same set of weights, how do we ensure the finetuned models are different?\n\n(2) There isn't much reference made to existing techniques on detecting out-of-domain inputs outside of the introduction, so it is hard to compare how novel / effective the proposed metrics are.\n\n(3) The tasks seem somewhat artificial due to the clear boundary between ambiguous versus unambiguous instructions. Most real life instructions are not entirely unambiguous and rely on a system selecting the most plausible hypothesis, e.g. \"Book the restaurant tomorrow at 6\" could mean 6pm or 6am, but contextually is probably 6pm. In these types of cases, it is unclear how robust the proposed metrics would be.\n\nPost-rebuttal edit:\n\nIssue (1) was resolved by the author response (random seeds changing order that training data is shown).  For issue (2) the authors say this is due to limited work addressing the same problem as the authors--unfortunately I am not well enough versed in the specific literature to verify this, so I leave this point to other reviewers. Issue (3) is perhaps the largest issue, as other reviewers have raised similar points--namely, that the scenario being presented may be too artificial and therefore it is unclear how well the techniques may transfer on to the original task.\n\nI have left my rating at 4/4, although I agree with the concerns from other reviewers about the setting being artificial and that the results in even in that setting are not decisive. The fact that there is no clear line between ambiguous and non-ambiguous in Figure 6, even though the dataset is artificial suggests that the method would struggle even more in real-life datasets, where the boundary between ambiguous and non-ambiguous is less clear cut. It may be possible to introduce various normalisation techniques to obtain better separation, such as accounting for dialogue history length, which empirically seems to be a confounding factor; however, this may undermine the point of interpretability, which is one of the paper's current strengths.", "questions_to_authors": "(1) For the ensemble model, how do you ensure the fine-tuned T5 models are different--is this just by changing the random seed?\n\nPost-rebuttal edit:\n\nI thank the authors for answering the question around implementation of the ensemble.", "paper_topic_and_main_contributions": "This paper addresses the problem of when to ask clarifying questions in grounded dialog, i.e. due to some ambiguity in the user's instruction / goal. The paper introduces several unsupervised methods that can be used to compute the model's uncertainty, and performs a series of experiments to see how well this is aligned with genuinely ambiguous instructions. Empirically, they demonstrate this first with a toy example via Lewis' signalling game, then with the Minecraft Collaborative Building Task. Overall, they show the metrics are generally well-correlated with uncertainty, although they also identify input length as a confounding factor which seems to cause uncertainty to increase.", "excitement": 4, "reproducibility": 3, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"Typos_Grammar_Style_and_Presentation_Improvements": "466 ... [of] what drives ..."}, "extra_text": {}}]

Author rebuttal:

Meta-review: Improving collaboration using clarification questions and thus reducing uncertainty in contained information. There are some concerns that reviewers raised concerns that have not been addressed, and some reviews have questions about its novelty.
Decision: Accept-Findings