Why do objects have many names? A study on word informativeness in language use and lexical systems (2024)

Abstract: Human lexicons contain many different words that speakers can use to refer to the same object, e.g., *purple* or *magenta* for the same shade of color. On the one hand, studies on language use have explored how speakers adapt their referring expressions to successfully communicate in context, without focusing on properties of the lexical system. On the other hand, studies in language evolution have discussed how competing pressures for informativeness and simplicity shape lexical systems, without tackling in-context communication. We aim at bridging the gap between these traditions, and explore why a soft mapping between referents and words is a good solution for communication, by taking into account both in-context communication and the structure of the lexicon. We propose a simple measure of informativeness for words and lexical systems, grounded in a visual space, and analyze color naming data for English and Mandarin Chinese. We conclude that optimal lexical systems are those where multiple words can apply to the same referent, conveying different amounts of information. Such systems allow speakers to maximize communication accuracy and minimize the amount of information they convey when communicating about referents in contexts.

## introduction a pervasive property of human lexical systems is that many names can be assigned to the same object. in other words, our semantic system allows for a soft mapping between referents and words @xcite @xcite . for instance, speakers can call the same chip purple or magenta @xcite , and the same animal dog or dalmatian @xcite . at the same time, a large body of literature has claimed that human lexicons are optimized for effi-* currently at apple. figure 1 : to allow successful identification of a target color chip (in the black frame) within a grid of candidates, a general term like purple is sufficient when the context is not challenging (above). a more specific name like magenta is needed when the distractors compete more with the target (bottom) -data from @xcite . indeed, communication exchanges between interlocutors take place in rich visual contexts. the dynamic nature of our environment and of speakers' goals constrains naming choices. in situations where it's essential to distinguish one item from context objects, some names can be better than others @xcite @xcite : for instance, when we want our florist to hand us a bouquet of our favourite flowers, the word flowers does not provide enough information, while the word daisies does. @xcite collected experimental data on the phenomenon, which we analyze in the current study. they asked pairs of speakers and listeners to communicate about target color chips appearing in a grid surrounded by distractor chips -see figure 1 . when the target chip is easily distinguishable from the distractors (top), a general term like purple might suffice. however, in a more challenging context, where target and distractors are similar (bottom), a more precise term like magenta might be necessary to ensure successful communication. what are the consequences of this on the structure of lexical systems? good lexical systems need to be simple, which minimizes cognitive load, and informative, which maximizes communicative effectiveness @xcite . studies have formalized this principle within an information-theoretical framework, showing that human systems optimize a trade-off between the amount of information provided and system complexity @xcite @xcite . while these studies often account for flexible semantic mappings @xcite , they do not study communication as situated, with speakers and listeners interacting in an always changing environment. in this work, we explore why a soft mapping between referents and names is a good solution for in-context communication. note that most research on the semantic properties of the lexicon has so far focused on a different aspect of the soft mapping between language and its use, complementary to the one we study here: ambiguity and polysemy, or the fact that most words have multiple meanings @xcite @xcite . we reverse the question, asking why a referent can be described with different words @xcite . this phenomenon entails that similar, overlapping meanings can be denoted by different words. our method introduces a measure of word informativeness based on word denotations and grounded in a visual space, which can also be used to measure the information provided by lexical systems as a whole. with it, we analyze the color naming systems of english and mandarin chinese, and claim that their structure is key to achieve successful communication in context, with interlocu-tors communicating in differently challenging situations. we first replicate findings from previous studies, showing how speakers adjust their lexical choices to context pressures, leveraging a flexible mapping between referents and words (i and language use). we then move to the system level, and show that alternative systems with no such flexible mapping are sub-optimal (i and language systems). foot_0 ## related work studies modeling language use have explored how speakers adapt their referring expressions and naming choices to the local context in which target referents appear @xcite @xcite or to their communicative goal @xcite . these patterns have been formalized in the unified quantitative framework of rational speech act theory (rsa; frank and @xcite @xcite @xcite . 2 rsa models focus on the contextual informativeness of referring expressions and utterances: the information that a word provides is measured in context, factoring in similarities and differences between a target referent and context objects. if a target object, e.g., a dog, appears in a context surrounded by other dogs, the word dog will not provide enough information about the target referent, and speakers will avoid it, choosing a more specific expression like dalmatian, in order to help listeners identify the target @xcite . speaker production choices are also constrained by considerations about utterance cost, often measured in terms of utterance length. speakers are hypothesized to choose referring expressions to maximize a utility function, trading off the maximization of the contextual informativeness with the minimization of the production costs. the rsa tradition, given this major focus on context-dependent word informativeness, does not discuss properties of lexical systems as a whole. cross-linguistic studies on lexical systems have highlighted that, even though different languages partition their semantic space in different ways, this variation is constrained. the structure of lexical systems is believed to derive from the competing communicative principles of informativeness and simplicity, and languages optimize this trade-offsimilar in nature to the one discussed for language use-in different ways @xcite @xcite . in this tradition, rooted in rate-distortion theory, the informativeness of a word is inversely related to the reconstruction error caused in a listener when a speaker uses it to describe a referent. in this sense, word informativeness is noncontextual: the informativeness of a word w for a target object t relates to the word's semantics and the referent's properties, and is not conditional on the local context in which t appears. this feature is in common with the word informativeness measure we adopt in this study. of note, @xcite showed theoretical connections between the objective proposed in the rsa framework and ratedistortion theory, suggesting that similar pressures guide the evolution of lexical systems and their pragmatic use (on a similar topic, see also @xcite . in this work, we propose a new measure of word informativeness that allows us to study speakers' adaptation to context in language use as well as the structure of lexical systems as a whole, bridging a gap between approaches focusing on contextual informativeness and approaches focusing on lexical informativeness -see section 3.2. ## i and language use as discussed in section 2, models of language use predict that, in harder contexts, speakers will utter longer and more specific referring expressions to achieve successful communication. in our analysis of language use, we aim at replicating these findings with our word informativeness measure and @xcite 's data. easier contexts are those where targets and distractors share fewer properties @xcite or, in the case of color chips, target and distractor chips are further away in the color space @xcite . our expectation for i w is that in harder contexts higher i w will be needed to reach successful communication -figure 1 , bottom. recall that we are especially interested in exploring what happens to the same target in different contexts. thus, we subset the data to keep chips that appear at least twice in the dataset (see appendix b for models fitted on all the data). this leaves us with 5491 data points across 2524 target chips, for english, and 60 data points across 29 target chips, for chinese. ## i and language systems we have seen that words with different informativeness values are used by speakers in differently hard contexts. what are the consequences of this on the structure of the lexical system? we argue that, to communicate successfully across differently hard interactions, we need a lexical system where multiple entries providing different amounts of information map to the same referent, allowing for a dynamic adaptation in lexical choice. we first formalize this idea in a simulation, and then run an empirical test to confirm it. simulation a lexical system like the one just described is not the most compact option: it lists 7 words for 6 referents, while, for instance, keeping only the general names would result in 3 words, and keeping only the specific names would result in 4 words. however, as we will show, this kind of system is more efficient: given that referents appear in context, the system can maintain high accuracy in communication, allowing a listener to identify the referent, while minimizing the overall information provided by speakers with their utterances. how is this achieved? imagine that each referent can appear in a visual context with another referent, with uniform probabilities (e.g., {referent 1-referent 2}, {referent 1-referent 3}, and so on). assume furthermore that a listener's accuracy in guessing the target is at chance (50%) if the name uttered to describe the target applies to the distractor as well. then, a speaker-listener pair could achieve very high accuracy by leveraging the system structure -as we have shown in section 4uttering the general name (low informativeness) when the two referents do not share it (easy context), and the specific name (high informativeness) when they do (i.e. in a harder context where the objects share more properties). we run a simulation with this setup, using the color data of @xcite . in particular, we use the target chips that were annotated with at least two different names, and the i w values of their corresponding names; and we generate from these data all the possible target-distractor pairs. results are reported in overall, thus, a system with a soft mapping between referents and names is an optimal solution, maximizing communicative accuracy with lower overall i l . ## discussion in this work, we have studied why a lexical system where multiple names map to the same referent is a good solution for human communication. we have done so by proposing a measure of word informativeness grounded in a visual feature space and based on word denotations. previous studies on the optimality of lexical systems often consider the number of lexical entries in a system as a measure of system complexity @xcite , with smaller lexicons preferable over large ones due to cognitive constraints. these approaches would fail at capturing how an increased lexicon size can become advantageous when we factor in communication in context, allowing for the minimization of the overall amount of information transmitted in language use. our study, drawing a connection between lan-guage use in context and the consequent structure of an efficient lexical system, bridges this gap. connecting properties of language production in context with properties of the lexicon is much in the spirit of previous work connecting language production and properties of grammars regarding language universals @xcite . future work should explore parallelisms between the lexicon and the grammar in this respect. this is also intimately connected to diachronic dynamics, and the causes and consequences of semantic change. adopting an evolutionary perspective, @xcite study how a human-like semantics can emerge from contextually-rich, pragmatic interactions; and @xcite compare lexicons emerging in artificial agents that have access to context information to those of context-agnostic ones. future work should delve deeper into how word informativeness and reference to objects in context interact to produce a given lexicon. a related question is how system learnability affects communication accuracy and shapes language evolution (on the topic see, for instance, @xcite @xcite . our work also resonates with previous research on why the lexicon presents pervasive ambiguity, and specifically the fact that most words have multiple meanings @xcite @xcite . as mentioned in the introduction, lexical ambiguity corresponds to one-tomany relationships between words and meanings, while we have focused on many-to-one relationships between words and referents. we believe that both phenomena are different consequences of efficiency constraints acting on the lexicon. our findings complement previous research @xcite by showing that, in general, many-tomany mappings between words and meanings can be characterized as efficient solutions. an advantage of our specific approach lies in the simplicity and the flexibility of our informativeness measure, that could be adapted to other kinds of distributed representations, allowing us to study different phenomena besides referential language in language and vision. for instance, when the subject of a sentence is unexpected for a listener, a more informative word like president instead of man may be preferred @xcite . our measure (which could be derived from language models considering the distances between contextualized embeddings of the same word) could allow for joint analyses of discourse and vision data under the same light, taking a step towards a unified view of human referential acts (see, for instance, franzon and zanini, 2023, for the analysis of a related phenomenon in morphology). that being said, our measure only accounts for the semantic meaning of words, suffering from a limitation: in every interaction, pragmatics enriches word meanings and modulates the information provided by words in context, or their cost for speakers. a denotational measure of word informativeness cannot capture the full range of phenomena characterizing language production in context. moreover, our formulation of the measure cannot accurately describe words denoted by non-convex regions in meaning spaces. we leave it to future work to define alternative measures that take into account more complex shapes (see figure 4 ), while still being general enough. finally, in our analyses we have made the simplified assumption that speakers want to provide the right amount of information, avoiding overinformative utterances. there is literature showing that this is not always the case @xcite , and that the production of redundant and over-informative referring expressions can fall in the set of behaviors that maximize efficient communication @xcite . moreover, considerations beyond informativeness affect speakers' naming choices, e.g. speakers could choose professor instead of woman to highlight aspects of the referent contingently relevant to them @xcite , even without any explicit pressure for discrimination. a more comprehensive analysis of language use should account for these factors as well. ## conclusion objects have many names. in this work, we have analyzed human color naming data exploring why some degree of soft mapping is a feature of an optimal lexical system, bridging the gap between analyses of lexical systems and language use. we conclude that systems where multiple words conveying different amounts of information can be used to describe the same referent are optimal, in that they maximize communicative accuracy while minimizing the amount of information conveyed.