Communicating with Speakers and Listeners of Different Pragmatic Levels (2024)

Abstract: This paper explores the impact of variable pragmatic competence on communicative success through simulating language learning and conversing between speakers and listeners with different levels of reasoning abilities. Through studying this interaction, we hypothesize that matching levels of reasoning between communication partners would create a more beneficial environment for communicative success and language learning. Our research findings indicate that learning from more explicit, literal language is advantageous, irrespective of the learner’s level of pragmatic competence. Furthermore, we find that integrating pragmatic reasoning during language learning, not just during evaluation, significantly enhances overall communication performance. This paper provides key insights into the importance of aligning reasoning levels and incorporating pragmatic reasoning in optimizing communicative interactions.

## introduction in everyday conversations there is a trade-off between clarity and conciseness. efficient messages might appear under-specified or ambiguous under a literal interpretation but can be successfully resolved using pragmatic reasoning about the speaker's intentions and the context of the communication @xcite @xcite . if the speaker trusts the listener to make the right inferences, they can choose to be more concise. being able to infer the intended meaning of an utterance beyond its literal content allows us to communicate efficiently. the process of how people attain pragmatic interpretations using a model of the speaker's intentions has long been studied. there is also plenty of evidence from psycho-linguistic studies that individuals have different levels of pragmatic competence @xcite . more importantly, people have been shown to keep track figure 1 : the speaker is asking for the red object. for a literal listener, this is ambiguous. a reasoning listener considers alternative messages about shape and color features and concludes that the speaker is asking for the red circle, as "square" would have been a more informative message for the other red object. the pragmatic reasoning modeled in this work involves counterfactual reasoning about alternative sentences that the speaker could have uttered . the interaction in figure 1 depicts an instance of such pragmatic reasoning about alternatives within our simple environment. according to pragmatic theory @xcite the same process accounts for the interpretation "they are in the office for the rest of the week", when we hear the sentence "we are not in the office on mondays". in this work, we investigate the impact of varying pragmatic competence on communicative success. we pair literal and pragmatic listeners with speakers of different levels of pragmatic competence. we study the interaction between such speakers and listeners not only during inference, where both partners have an already learned lexicon, but also during language learning. this way we gain insight into optimal levels of pragmatic inference for teachers and language learners. we hypothesise that matching levels of reasoning between partners benefits communicative success and language learning. our simulations reveal that with a lexicon that doesn't perfectly match that of the speaker's, sophisticated pragmatic listeners still significantly benefit from explicit literal language use. we also show that language learners that do not model pragmatic inference, struggle when learning from a speaker who uses pragmatic communication, while language learners that integrate a model of the speaker are significantly more successful. ## background we situate our listener in an image-based version of lewis's signaling game @xcite . imagereferential games are commonly used to study the benefit of speakers and listeners reasoning about each other in context @xcite @xcite . at each turn a collection of n images is provided as context @xmath0, ..., o n ), with the speaker having knowledge of a specific target image o t , where 1 ≤ @xmath1. the listener's objective is to correctly identify the target image index t given the speaker's message w . the messages may contain multiple words by combining words from a fixed vocabulary. ## data to investigate the impact of the pragmatic competence of speakers and listeners on communicative success, it is necessary to establish a controlled setting that allows for manipulation of the reasoning abilities of participants. we create a new environment based on the shapeworld dataset @xcite . instead of the rule based method of @xcite , we use an exact implementation of the rational speaker defined in equation 3. this way we can create speakers with different depth of recursive reasoning. our speakers are not learned, they are knowledgeable users of the language: they have access to the underlying true lexicon which indicates the mapping between color and shape words and image properties. each game consists of a target image and a variable number of n -1 distractor images. images are described by one out of six different colors and a shape that can take five different values. the location, size and rotation of the objects is randomized on a 64x64 grid which creates a large variation of candidate pictures. we parameterize the process that generates the image tuples for each game by four probability distributions: the priors over the shapes p (s) and colors p (c), the probability that controls the correlations between colors p (c|c) and the conditional defining the co-occurrence of shapes p (s|s). we sample these distributions from different dirichletdistributions. we create two sets of concentration parameters: in the first version of the game, all sampled distributions are close to uniform (@xmath2, while in the second version introduces correlations in the shape and color conditionals (@xmath3. this way the sampled image tuples share more features, creating higher likelihood for pragmatic messaging that differentiates s 1 and s 3 . for training, we sample only one instance of each distribution. at test time, we sample different p (s), p (c), p (s|s) and p (c|c) instances 10 times. from each of these constellations we sample 3200 games. the random seed is fixed across all experiments and is reset for the learning and evaluation of each learner. this ensures that each listener sees the exact same examples in all environments. ## experiments the fact that we have full control over the speaker's messaging strategy and the data generating process allows us to alter the level of the speakers that the listeners learn from and create image tuples that highlight the contrast between higher level pragmatic and lower level literal messaging strategies. we train train l 0 literal listeners and l 2 pragmatic listeners. we create two different levels of speakers to pair them with our learning listeners: s 1 has an internal model of a competent l 0 , while s 3 anticipates l 2 -behavior. implementation for training and evaluating all models can be found at https://github.com/naszka/rsa_backward/ . ## conclusions humans exploit pragmatic reasoning in order to reduce the effort of speaking. for artificial agents to understand humans, it is critical to correctly resolve ambiguities. by recursively modeling the conversational partner, pragmatic listeners can arrive at the interpretations intended by pragmatic speakers. in this work, we introduced speaker-listener pairs with matching or misaligned levels of pragmatic competence. we examined the benefits of integrating pragmatics not only during evaluation but already during language learning. our results show that learning from more explicit, literal language is always beneficial, regardless of the pragmatic capacity of the learner. at the same time, we conclude that language learners need to apply reasoning about the context and the speaker when learning from data that was generated pragmatically.