Aligning Predictive Uncertainty with Clarification Questions in Grounded Dialog (2023)

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.

## introduction human language offers a natural interface between machine and human when solving a collaborative task. we wish to build an artificial agent that can not only ground natural language in its environment, but can also solicit more information when communication fails or the human's instruction was ambiguous. asking questions establishes twoway collaborative communication instead of silent following of instructions. endowing an instructionfollowing agent with the ability to prompt questions poses two related but different problems: when to initiate asking a question and what the content of the question should be. in this work, we focus on the first problem. the problem of deciding if a system should be asking clarification questions has been previously discussed in the context of conversational search and dialog systems. past approaches can be grouped into two paradigms, where a separate model decides if a follow-up is necessary @xcite , and where the need for clarification is based on a model that would eventually generate the response @xcite . in the former, a separate training set is needed with annotations that indicate ambiguity @xcite . in the latter, no such signal is needed -the system uses an estimate of quality of its own predictive performance to decide. the same problem can be posed in the context of visually grounded dialog. the domain of taskoriented communication in a visual environment is less mature, and the need for questions is often hard-coded in these systems @xcite @xcite . nevertheless, we can already find examples of both aforementioned paradigms, where a binary classifier is learned based on human-demonstrations @xcite , and systems that decide on the for questions based on its own uncertainty @xcite . previous works argued that a model that generates information-seeking questions should not be decoupled from the model that makes the final prediction for the task @xcite . the model uncertainty over class labels can be used to decide on the content of the clarification question. we use the same reasoning in our work, since the acting agent's uncertainty, used to decide when to ask a question, is more interpretable than a blackbox model that is trained to make a positive prediction if a human decided to ask a question. our setup involves a human architect agent and an artificial builder agent situated in a 3d gridworld environment. the human architect agent has access to a target structure and can direct the artificial builder agent with natural language. a natural way of evaluating if an instruction-following agent is asking questions at the right time is to use human evaluation, which is laborintensive. in this work, we perform evaluation using already existing datasets @xcite . we investigate if the builder model's uncertainty is higher for instructions that precede questions in these human-human dialogues. our contributions are: ## methodology we begin by defining ambiguous instructions and propose casting them as out-of-domain input in our modelling effort. we then describe our probabilistic model and the uncertainty metrics used for detecting the need for questions. in this study, we use unsupervised methods for uncertainty estimation: we do not use examples of human questions in order to create a model for uncertainty. instead, we obtain our measures from statistics that are obtained from the predictive probability distribution of our model. ## related work the need for clarification has received a lot of interest in the field of conversational search and dialog-systems. in spoken dialog-system, the confidence of the speech-recognition system is a popular starting point for prompting clarifications @xcite . the asr-confidence is often further refined by input from downstream processing such as the output of a natural language understanding module @xcite or structured discourse-modeling @xcite . these more structured semantic representations often allow for custom notions of ambiguity, such as too many possible referents @xcite , or contradictions in the discourse representation @xcite . in recent years, we have witnessed a rising popularity of neural sequence-to-sequence implementations of conversational (openai, 2023) and situated agents @xcite . given their end-to-end nature, more approaches model the need for clarification in these systems as a separate neural model, that is trained on data labelled with ambiguity tags @xcite @xcite . closest to our domain, the model for timing clarification questions introduced by @xcite also consists of a neural binary classifier trained using the occurrences of human clarification questions in dialog. they use the same 3d grid world and human data collection that we utilize in this work. ## ambiguous messages in a lewis' signaling game we start our analysis with a simple speaker-listener game. we use this setup to illustrate the relation between calibration and detection of ambiguous instructions. in this referential task, the speaker and the listener observe an ordered set of objects. the speaker chooses a message to refer to one of these objects and the listener has to correctly identify the referential target based on the message. in our setup, only the listener agent is learned, and we restrict the task to a choice between two objects: o 1 and o 2 . each object is described by @xmath0. in our visualization, the properties correspond to color and shape. the vocabulary is the set of all @xmath1. during training, the speaker sends a single word that identifies the target object, and the listener is rewarded if it picks the correct one. see figure 1 for an illustration of the setting. the listening agent estimates the following binary probability distribution of identification i: ## ambiguous instructions in grounded collaborative dialog for our grounded dialog setting, we focus on agents trained to build structures in 3d environments based natural language instructions from a human partner. the corresponding data was collected during a series of collaborative building tasks where a human architect was giving directions to a human builder in order to build a target structure. during training, we present the builder agent with: (a) the relevant dialog history, including utterances from both the architect and the builder, and (b) the state of the grid built in previous steps. the builder is trained to place and remove colored blocks using supervised signals from action sequences recorded from human-human interactions. ## analysis while for ambiguity minimal-pairs, the ensemble model can confidently differentiate between the corresponding instructions in 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 ). 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 of the 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. for significantly longer input sequences, the performance 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 input sequences under the length of 250. while the performance is close to constant in this range, the ## conclusion and future work in this work, we have investigated the alignment between predictive uncertainty and ambiguous instructions in visually grounded communication. we show that calibration can help increase the efficacy of unsupervised uncertainty metrics. however, our empirical analysis on the relationship between uncertainty and dialog history length also shows that varying input lengths can pose a problem for using uncertainty metrics for detection. at the same time, these metrics do exhibit the expected rise in uncertainty when applied to detecting targeted ambiguity changes. the task of detecting the need for questions in this environment has been posed in the frame of the 14995 2022 iglu nlp challenge @xcite . crucially, the evaluations of the builder agent and question asking module have been separated into two tracks. for future work, we can use the uncertainty metrics generated by the builder to detect the need for asking questions, removing the need for a bespoke model for the latter. highly varying input-lengths is an inherent feature 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-tagging @xcite , asr @xcite and @xcite . these applications do not exhibit the same magnitude of variance in the length of the model's input.