By the age of two, children tend to assume that new word categories are based on objects’ shape, rather than their color or texture; this assumption is called the shape bias. They are thought to learn this bias by observing that their caregiver’s language is biased towards shape based categories. This presents a chicken and egg problem: if the shape bias must be present in the language in order for children to learn it, how did it arise in language in the first place? In this paper, we propose that communicative efficiency explains both how the shape bias emerged and why it persists across generations. We model this process with neural emergent language agents that learn to communicate about raw pixelated images. First, we show that the shape bias emerges as a result of efficient communication strategies employed by agents. Second, we show that pressure brought on by communicative need is also necessary for it to persist across generations; simply having a shape bias in an agent’s input language is insufficient. These results suggest that, over and above the operation of other learning strategies, the shape bias in human learners may emerge and be sustained by communicative pressures.

This paper describes a French Spoken Dialogue System (SDS) named NASTIA (Negotiating Appointment SeTting InterfAce). Appointment scheduling is a hybrid task halfway between slot-filling and negotiation. NASTIA implements three different negotiation strategies. These strategies were tested on 1734 dialogues with 385 users who interacted at most 5 times with the SDS and gave a rating on a scale of 1 to 10 for each dialogue. Previous appointment scheduling systems were evaluated with the same experimental protocol. NASTIA is different from these systems in that it can adapt its strategy during the dialogue. The highest system task completion rate with these systems was 81% whereas NASTIA had an 88% average and its best performing strategy even reached 92%. This strategy also significantly outperformed previous systems in terms of overall user rating with an average of 8.28 against 7.40. The experiment also enabled highlighting global recommendations for building spoken dialogue systems.

This paper describes the DINASTI (DIalogues with a Negotiating Appointment SeTting Interface) corpus, which is composed of 1734 dialogues with the French spoken dialogue system NASTIA (Negotiating Appointment SeTting InterfAce). NASTIA is a reinforcement learning-based system. The DINASTI corpus was collected while the system was following a uniform policy. Each entry of the corpus is a system-user exchange annotated with 120 automatically computable features. The corpus contains a total of 21587 entries, with 385 testers. Each tester performed at most five scenario-based interactions with NASTIA. The dialogues last an average of 10.82 dialogue turns, with 4.45 reinforcement learning decisions. The testers filled an evaluation questionnaire after each dialogue. The questionnaire includes three questions to measure task completion. In addition, it comprises 7 Likert-scaled items evaluating several aspects of the interaction, a numerical overall evaluation on a scale of 1 to 10, and a free text entry. Answers to this questionnaire are provided with DINASTI. This corpus is meant for research on reinforcement learning modelling for dialogue management.