There has been a lot of work on predicting the timing of feedback in conversational systems. However, there has been less focus on predicting the prosody and lexical form of feedback given their communicative function. Therefore, in this paper we present our preliminary annotations of the communicative functions of 1627 short feedback tokens from the Switchboard corpus and an analysis of their lexical realizations and prosodic characteristics. Since there is no standard scheme for annotating the communicative function of feedback we propose our own annotation scheme. Although our work is ongoing, our preliminary analysis revealed lexical tokens such as “yeah” are ambiguous and therefore lexical forms alone are not indicative of the function. Both the lexical form and prosodic characteristics need to be taken into account in order to predict the communicative function. We also found that feedback functions have distinguishable prosodic characteristics in terms of duration, mean pitch, pitch slope, and pitch range.

An idealized, though simplistic, view of the referring expression production and grounding process in (situated) dialogue assumes that a speaker must merely appropriately specify their expression so that the target referent may be successfully identified by the addressee. However, referring in conversation is a collaborative process that cannot be aptly characterized as an exchange of minimally-specified referring expressions. Concerns have been raised regarding assumptions made by prior work on visually-grounded dialogue that reveal an oversimplified view of conversation and the referential process. We address these concerns by introducing a collaborative image ranking task, a grounded agreement game we call “A Game Of Sorts”. In our game, players are tasked with reaching agreement on how to rank a set of images given some sorting criterion through a largely unrestricted, role-symmetric dialogue. By putting emphasis on the argumentation in this mixed-initiative interaction, we collect discussions that involve the collaborative referential process. We describe results of a small-scale data collection experiment with the proposed task. All discussed materials, which includes the collected data, the codebase, and a containerized version of the application, are publicly available.

Turn-taking is a fundamental aspect of human communication and can be described as the ability to take turns, project upcoming turn shifts, and supply backchannels at appropriate locations throughout a conversation. In this work, we investigate the role of prosody in turn-taking using the recently proposed Voice Activity Projection model, which incrementally models the upcoming speech activity of the interlocutors in a self-supervised manner, without relying on explicit annotation of turn-taking events, or the explicit modeling of prosodic features. Through manipulation of the speech signal, we investigate how these models implicitly utilize prosodic information. We show that these systems learn to utilize various prosodic aspects of speech both on aggregate quantitative metrics of long-form conversations and on single utterances specifically designed to depend on prosody.

Open-domain chatbots are supposed to converse freely with humans without being restricted to a topic, task or domain. However, the boundaries and/or contents of open-domain conversations are not clear. To clarify the boundaries of “openness”, we conduct two studies: First, we classify the types of “speech events” encountered in a chatbot evaluation data set (i.e., Meena by Google) and find that these conversations mainly cover the “small talk” category and exclude the other speech event categories encountered in real life human-human communication. Second, we conduct a small-scale pilot study to generate online conversations covering a wider range of speech event categories between two humans vs. a human and a state-of-the-art chatbot (i.e., Blender by Facebook). A human evaluation of these generated conversations indicates a preference for human-human conversations, since the human-chatbot conversations lack coherence in most speech event categories. Based on these results, we suggest (a) using the term “small talk” instead of “open-domain” for the current chatbots which are not that “open” in terms of conversational abilities yet, and (b) revising the evaluation methods to test the chatbot conversations against other speech events.

The ability to take turns in a fluent way (i.e., without long response delays or frequent interruptions) is a fundamental aspect of any spoken dialog system. However, practical speech recognition services typically induce a long response delay, as it takes time before the processing of the user’s utterance is complete. There is a considerable amount of research indicating that humans achieve fast response times by projecting what the interlocutor will say and estimating upcoming turn completions. In this work, we implement this mechanism in an incremental spoken dialog system, by using a language model that generates possible futures to project upcoming completion points. In theory, this could make the system more responsive, while still having access to semantic information not yet processed by the speech recognizer. We conduct a small study which indicates that this is a viable approach for practical dialog systems, and that this is a promising direction for future research.

Syntactic and pragmatic completeness is known to be important for turn-taking prediction, but so far machine learning models of turn-taking have used such linguistic information in a limited way. In this paper, we introduce TurnGPT, a transformer-based language model for predicting turn-shifts in spoken dialog. The model has been trained and evaluated on a variety of written and spoken dialog datasets. We show that the model outperforms two baselines used in prior work. We also report on an ablation study, as well as attention and gradient analyses, which show that the model is able to utilize the dialog context and pragmatic completeness for turn-taking prediction. Finally, we explore the model’s potential in not only detecting, but also projecting, turn-completions.

In dialogue, speakers continuously adapt their speech to accommodate the listener, based on the feedback they receive. In this paper, we explore the modelling of such behaviours in the context of a robot presenting a painting. A Behaviour Tree is used to organise the behaviour on different levels, and allow the robot to adapt its behaviour in real-time; the tree organises engagement, joint attention, turn-taking, feedback and incremental speech processing. An initial implementation of the model is presented, and the system is evaluated in a user study, where the adaptive robot presenter is compared to a non-adaptive version. The adaptive version is found to be more engaging by the users, although no effects are found on the retention of the presented material.

Referring to entities in situated dialog is a collaborative process, whereby interlocutors often expand, repair and/or replace referring expressions in an iterative process, converging on conceptual pacts of referring language use in doing so. Nevertheless, much work on exophoric reference resolution (i.e. resolution of references to entities outside of a given text) follows a literary model, whereby individual referring expressions are interpreted as unique identifiers of their referents given the state of the dialog the referring expression is initiated. In this paper, we address this collaborative nature to improve dialogic reference resolution in two ways: First, we trained a words-as-classifiers logistic regression model of word semantics and incrementally adapt the model to idiosyncratic language between dyad partners during evaluation of the dialog. We then used these semantic models to learn the general referring ability of each word, which is independent of referent features. These methods facilitate accurate automatic reference resolution in situated dialog without annotation of referring expressions, even with little background data.

Previous models of turn-taking have mostly been trained for specific turn-taking decisions, such as discriminating between turn shifts and turn retention in pauses. In this paper, we present a predictive, continuous model of turn-taking using Long Short-Term Memory (LSTM) Recurrent Neural Networks (RNN). The model is trained on human-human dialogue data to predict upcoming speech activity in a future time window. We show how this general model can be applied to two different tasks that it was not specifically trained for. First, to predict whether a turn-shift will occur or not in pauses, where the model achieves a better performance than human observers, and better than results achieved with more traditional models. Second, to make a prediction at speech onset whether the utterance will be a short backchannel or a longer utterance. Finally, we show how the hidden layer in the network can be used as a feature vector for turn-taking decisions in a human-robot interaction scenario.