Incorporating Pragmatic Reasoning Communication into Emergent Language
Table 2: Pragmatics communication accuracy using virtual interlocutors.
['[BOLD] Virtual Opponent', '[BOLD] SFide', '[BOLD] LFide', '[BOLD] ArgmaxL', '[BOLD] RSA', '[BOLD] IBR', '[BOLD] GT', '[BOLD] GTs']
In classic pragmatic frameworks, it is often assumed that interlocutors know about each other very well. For example, the prior probabilites PS0 and PL0 are common knowledge to each other. However, in practice, game information may be incomplete and one’s assumptions about the other interlocutor may diverge from reality. Each person learns their own language model and reasons about others based on their own mental model. To check how this affects pragmatics, we assume agents S and L first respectively learn to speak and listen as before, so we obtain PS0 and PL0 , S has its own listener model PL′0 and L has its own speaker model PS′0. We then train PS′0 and PL′0 by communicating with and simulating outputs of PS0 and PL0 regarding game instances. In the end, PS′0 is similar to PS0 but not exactly the same, and similarly for the listener model. This amounts to policy reconstruction methods from the perspective of theory of mind and opponent modeling. During testing, S and L try to communicate using pragmatic frameworks, each using their own model as the virtual interlocutor model. “Fide" here describes the average fidelity when a virtual model simulates the real one, which is computed as the cosine similarity between the output distributions of the models on the same input. We observe that although the fidelities are fairly high after training, their minor differences substantially hamper the pragmatic performance. GameTable is most susceptible to this, while IBR and GameTable-sequential are fairly robust.