Incremental Processing of Principle B : Mismatches Between Neural Models and Humans (2022)

Abstract: Despite neural language models qualitatively capturing many human linguistic behaviors, recent work has demonstrated that they underestimate the true processing costs of ungrammatical structures. We extend these more fine-grained comparisons between humans and models by investigating the interaction between Principle B and coreference processing. While humans use Principle B to block certain structural positions from affecting their incremental processing, we find that GPT-based language models are influenced by ungrammatical positions. We conclude by relating the mismatch between neural models and humans to properties of training data and suggest that certain aspects of human processing behavior do not directly follow from linguistic data.

## introduction neural models trained on text data alone have been shown to qualitatively capture aspects of a large variety of human linguistic behaviors (e.g., @xcite @xcite @xcite . investigations have evaluated a range of levels of linguistic knowledge, including: i) syntax @xcite @xcite , ii) semantics @xcite , and iii) discourse structure and pragmatics @xcite . recent work has placed increased attention on finer-grained comparisons between neural models and humans (e.g., @xcite @xcite . the growing consensus is that neural models underestimate the processing costs seen with humans, while nonetheless capturing the broad patterns (see @xcite . the present study adds to this literature by comparing the incremental processing of coreference in humans and neural models. while coreference, more generally, is modulated by discourse, pragmatics, and information structure (e.g., @xcite @xcite @xcite , there are sentential restrictions on coreference that have immediate effects on human incremental processing (e.g., @xcite @xcite . this study finds that, contrary to humans, autoregressive neural models do not similarly restrict their behavior in coreference processing. in particular, the present study investigated the interaction between the binding principles, articulated in @xcite , and incremental processing. binding principles account for the constrained distribution of pronouns (and anaphora) and their possible linguistic antecedents: ## background in human coreference processing, a major question is whether antecedent retrieval, triggered by the presence of a pronoun, is restricted first by agreement features (e.g., gender, number), returning possibly ungrammatical antecedents, or by structural constraints, like principle b, which serve as an initial filter. as an illustration consider the following set of stimuli discussed in @xcite : (4) a. john thought that bill liked him. b. john thought that mary liked him. c. jane thought that bill liked him. d. jane thought that mary liked him. if principle b immediately restricts the set of possible antecedents of him, then we would expect the reading times at him to be the same for (4-a) and (4-b), as in both cases the structurally licit antecedent agrees in gender. if instead structurally ungrammatical antecedents can influence the immediate processing of him, then we would expect that (4-a)-(4-c) would pattern together, to the exclusion of (4-d), where no antecedent is given in the linguistic context. put another way, whether the structurally ungrammatical antecedent influences reading times at him is indicative of the status of principle b in human linguistic processing. the bulk of work investigating these, and similar constructions, has found that structural constraints like principle b do immediately influence human incremental processing (e.g., @xcite @xcite @xcite . that is, finding that (4-a) and (4-b) pattern together and (4-c) and (4-d) pattern together. 1 within work in natural language processing, existing models have been claimed to capture aspects of principle a (e.g., @xcite . principle c has received less attention, though see @xcite which found that lstm language models failed to obey principle c. coreference, more broadly, has also been explored, with results suggesting that models encode features of coreference resolution (e.g., @xcite and the interaction of implicit causality and pronouns (verb biases that influence preferred antecedents for pronouns; @xcite @xcite . the present study straightforwardly extends existing studies of neural models to principle b. while we cannot assess whether neural models truly "interpret" the pronoun as coreferring with certain antecedents (and thus fully verify whether they have learned principle b, or even principle a), we can compare the difference in model behavior conditioned on minimally contrastive stimuli. in fact, human online sentence comprehension stud-ies are similarly limited. since we cannot directly measure the content retrieved in reading a pronoun, online reading times are taken as a proxy for the consideration of certain antecedents. ## neural models and measures we analyzed four autoregressive models with gptlike architectures: gpt-2 xl (1.5b parameters; @xcite , gpt-neo (2.7b parameters; @xcite , gpt-j (6b parameters; wang and komatsuzaki, 2021), and gpt-3 (175b parameters; @xcite . gpt-2 xl, gpt-neo, and gpt-j were accessed via huggingface @xcite , and gpt-3 by using openai's api. 2 in evaluating model performance, we used surprisal @xcite : surprisal has a linear relationship with human reading times @xcite . we follow a growing body of work in utilizing this relationship to compare the behavior of neural models and humans (e.g., @xcite . 3 to aid the interpretation of the results, we calculated by-item gender mismatch effects (gmmes). gmmes are used in human experiments to index the increased cost in processing incurred when encountering a pronoun (or a postcedent, in the case of cataphoric pronoun processing) that was not expected (e.g., @xcite @xcite . thus, gmmes are a means of measuring human predictions by providing evidence for mismatches between expectations and reality. we calculated two classes of gmmes for neural models targeting gender prediction for, i) "vanilla" pronouns, and ii) subjects after reading cataphoric pronouns. for predictions about upcoming pronouns, consider: to calculate the gmme for (5), we took the difference between the surprisal for him in (5-a) and the surprisal for him in (5-b). more generally, we calculated a gmme by taking the difference in the surprisal of the target (either a pronoun or the subject noun) between minimal pairs. a positive gmme would suggest that the model was more surprised when the embedded subject mismatched in gender with the pronoun; in other words, the gender of the embedded subject influenced the surprisal of the pronoun. in this case, comparing the gmme for him and his is informative about the status of principle b in neural models. humans have been shown to exhibit no gmme dependent on the embedded subject with him, because principle b blocks co-indexation between these positions. for his, however, co-indexation is possible, and a gmme is obtained (see @xcite ). 4 for predictions about upcoming antecedents after cataphoric pronouns, consider: for (6) we calculated a gmme by taking the difference in surprisal of keisha in (6-b) and the surprisal of fred in (6-a). a positive gmme would indicate that the neural model was more surprised when the subject mismatched with the gender of the cataphoric subject pronoun. foot_5 ## principle b and pronouns recall, humans restrict their incremental processing of coreference to just those antecedents which are grammatically licensed (e.g., @xcite . that is, in sentences like fred thought amy hated him, him cannot be co-indexed with the structural position that amy occupies, and thus, the gender of amy does not hinder the processing of him. in this section, we evaluated the ability of gpt-like autoregressive neural models to replicate this qualitative effect across four experimental conditions. ## principle b and cataphora the above section explored the role principle b plays in pronoun prediction for gpt-like neural models, finding a qualitative mismatch between the incremental processing of neural models and humans. recent work in psycholinguistics has also demonstrated that principle b can restrict the prediction of subjects following cataphoric object pronouns @xcite . ( 9) a. while baking him some cookies, nicholas chatted with mark. b. while an employee baked him some cookies, nicholas happily chatted with mark. in ( 9 ), him is a cataphoric pronoun -the noun phrase it corefers with comes later in the sentence. while him can be co-indexed with nicholas in (9-b) (meaning nicholas had some cookies baked for him), him cannot be co-indexed with nicholas in (9-a). foot_11 principle b excludes this latter coindexation. 12 @xcite found that humans exhibited a gmme at the subject (e.g., nicholas) only in cases where co-indexation between the catphoric him and the subject was possible (e.g., (9-b)). as with "vanilla" pronouns, it seems, then, that principle b immediately restricts the human parser, such that grammatically unavailable structural positions are ignored. in the following section, we evaluated whether neural models patterned like humans in this respect. that is, whether models exhibited a gmme only in cases where principle b did not block coindexation. first, we also verified that the neural models could use cataphoric pronouns to restrict the prediction of subjects more generally. ## general discussion this study investigated whether autoregressive neural models displayed similar incremental coreference processing to humans. specifically, we examined the interaction between principle b and coreference processing with two broad case studies: i) "vanilla" pronouns (where the antecedent precedes the pronoun), and ii) cataphoric pronouns (where the pronoun precedes its coreferring noun phrase). for the first case study, we found that the pronoun predictions of all models were influenced by structural positions deemed ungrammatical by principle b, inconsistent with the incremental processing behavior of humans. for the second case study, we found that two of the four models (gpt-j and gpt-3), displayed human-like processing behavior in predicting subjects after cataphoric object pronouns (e.g., him), specifically with principle b blocking the influence of the pronoun on the prediction of the later subject. three questions remain concerning the behavior of neural models: 1) how closely do models predict the observed processing cost in human studies, 2) why do gpt-j and gpt-3, and not the other models, pattern like humans in cataphoric processing, and 3) why do models consider ungrammatical antecedents in their incremental processing of pronouns.