Reference-Centric Models for Grounded Collaborative Dialogue (2021)

Abstract: We present a grounded neural dialogue model that successfully collaborates with people in a partially-observable reference game. We focus on a setting where two agents each observe an overlapping part of a world context and need to identify and agree on some object they share. Therefore, the agents should pool their information and communicate pragmatically to solve the task. Our dialogue agent accurately grounds referents from the partner’s utterances using a structured reference resolver, conditions on these referents using a recurrent memory, and uses a pragmatic generation procedure to ensure the partner can resolve the references the agent produces. We evaluate on the OneCommon spatial grounding dialogue task (Udagawa and Aizawa 2019), involving a number of dots arranged on a board with continuously varying positions, sizes, and shades. Our agent substantially outperforms the previous state of the art for the task, obtaining a 20% relative improvement in successful task completion in self-play evaluations and a 50% relative improvement in success in human evaluations.

## introduction in grounded dialogue settings involving high degrees of ambiguity, correctly interpreting and informatively generating language can prove challenging. consider the collaborative dialogue game shown in figure 1 . each player has a separate, but overlapping, view on an underlying context. they need to communicate to determine and agree on one dot that they share, and both players win if they choose the same dot. to succeed, each participant must-implicitly or explicitly-ground their partner's descriptions to their own context, maintain a history of what's been described and what their partner is likely to have, and informatively convey parts of their own context. ## setting we choose to focus on the onecommon task @xcite since it is a particularly challenging representative of a class of partially-observable collaborative reference dialogue games (e.g., @xcite . in this task, two players have different but overlapping views of a game board, which consists of dots of various positions, shades of gray and sizes. the players must coordinate to choose a single dot that both players can see, which is challenging because neither knows which dots the other can see. each player's world view, w, consists of a circular view on an underlying board containing between 8 and 10 randomly scattered dots, with continuously varying positions, shades, and sizes (figure 1 ). each player's view contains 7 dots, and the views of the players overlap so that there are between 4 and 6 dots which appear in both views. we focus on a turn-based version of the dialogue task. in a given turn t, a player may communicate with their partner by either sending an utterance u t or selecting a dot s. in the event of selection, the partner is notified but cannot see which dot the player has selected. once a player has selected a dot, they can no longer send messages. the dialogue ends once both players have selected a dot, and is successful if both selected the same one. ## model structure our approach is a modular neural dialogue model which factors the agent's generation process into a series of successive subtasks, all centered on grounding language into referents in the world context. in this section, we describe our model structure, which defines a neural module for each subtask. we then describe our reference-centric pragmatic generation procedure in section 4. an overview of the relationship between modules in our model is shown in figure 2 . each module can condition on neural encodings of the context (the world and past dialogue), as well as the outputs of other modules. we describe our system at a high-level here, then give task-specific implementation details about each component in section 5. ## pragmatic generation the modules as described above can be used to generate the next utterance u t+1 using the predictions of p m (r t+1 ) and p u (u t+1 |r t+1 ) (omitting other conditioning variables from the notation for brevity; see section 3 for the full conditioning contexts). this section describes an improvement, pragmatic generation, to this process. referents and their expressions should be relevant in the dialogue and world context, but they should also be discriminative @xcite : allowing the listener to easily understand which referents the speaker is intending to describe. our pragmatic generation approach, based on the rational speech acts (rsa) framework @xcite , uses the reference resolution module, p r (r t+1 |u t+1 ), to predict whether the partner can identify the intended referents. this encourages selecting referents that are easy for the partner to identify and describing them informatively in context. 5 we use the following objective over referents r and utterances u for a given turn: ## module implementations as described so far, our system is applicable to a range of partially-observable grounded collaborative referring expression dialogue tasks (e.g., @xcite . in this section, we describe implementations of our systems' modules, some of which are tailored to onecommon. ## experiments we compare our approach to past systems for the onecommon dataset. while our primary evaluation is to evaluate systems on their success rate on the full dialogue game when paired with human partners (section 6.4), we also compare our system to past work, and ablated versions of our full system, using the automatic evaluations of past work. ## related work goal-oriented dialog. the modular approach that we use reflects the pipelined approach often used in goal-oriented dialogue systems @xcite . recent work on neural systems has also used structured and memory-based approaches @xcite including tracking entities identified in text @xcite . we also find improvements from an entity-centric approach with a structured memory, although our domain involves more challenging entity resolution and generation due to the spatial grounding. ## conclusion we presented a modular, reference-centric approach to a challenging partially-observable grounded collaborative dialogue task. our approach is centered around a structured referent grounding module, which we use (1) to interpret a partner's utterances and (2) to enable a pragmatic generation procedure that encourages the agent's utterances to be able to be understood in context. we perform, for the first time, human evaluations on the full dialogue task, finding that our system cooperates with people substantially more successfully than a system from past work and-in aggregateachieves a success rate comparable to pairs of human partners. while our results are encouraging, there is still much room for improving all systems in their interactions with people on this challenging task. as the examples in appendix e illustrate, people use sophisticated conversational strategies to build common ground @xcite @xcite when they interact with each other, producing utterances that play multiple conversational roles and performing complex reasoning. to better plan utterances @xcite and more accurately infer the partner's state @xcite , we suspect it will be helpful to extend the single-step pragmatic utterance planning and implicit inference procedures that we use here: planning over longer time horizons, performing more explicit reasoning under uncertainty, and learning richer models of the full range of speech acts that people use. future work might continue to explore these directions on this task and other similarly challenging tests of collaborative grounding. partner refs. next refs model acc. ex. ex. f-mem-struc 87.3±0.3 41.8±0.9 4.8±1.0 f-mem 90.6±0.3 65.2±1.0 23.5±2.0 full 91.2±0.4 67.0±1.0 31.1±1.0