Coreference information guides human expectations during natural reading (2020)

Abstract: Models of human sentence processing effort tend to focus on costs associated with retrieving structures and discourse referents from memory (memory-based) and/or on costs associated with anticipating upcoming words and structures based on contextual cues (expectation-based) (Levy,2008). Although evidence suggests that expectation and memory may play separable roles in language comprehension (Levy et al., 2013), theories of coreference processing have largely focused on memory: how comprehenders identify likely referents of linguistic expressions. In this study, we hypothesize that coreference tracking also informs human expectations about upcoming words, and we test this hypothesis by evaluating the degree to which incremental surprisal measures generated by a novel coreference-aware semantic parser explain human response times in a naturalistic self-paced reading experiment. Results indicate (1) that coreference information indeed guides human expectations and (2) that coreference effects on memory retrieval may exist independently of coreference effects on expectations. Together, these findings suggest that the language processing system exploits coreference information both to retrieve referents from memory and to anticipate upcoming material.

## introduction maintaining a model of discourse referents and their relations is a core function of human language understanding and depends on the ability to recognize when linguistic expressions corefer. identifying the referent of a linguistic expression (coreference resolution) is a well-established task in natural language processing, and a sizeable psycholinguistic literature has explored the computations that support coreference resolution in humans. theories of coreference processing generally agree on a critical role played by searching associative memory stores for plausible referents @xcite @xcite @xcite , based on findings that coreference is more easily established to more salient or activated referents, as evidenced by human reading latencies @xcite . nonetheless, some coreference-related phenomena are not obviously related to search. consider the following example: sally frightened her brother because sally/she/he... numerous studies indicate that he in the boldfaced slot is more difficult to process (i.e. read more slowly) than she despite the fact that he corefers unambiguously to her brother, the most recently activated discourse referent @xcite @xcite @xcite . this is due to the implicit causality of the verb frightened, which predisposes comprehenders to expect an explanation in terms of the agent of the frightening event, rather than the patient @xcite . in addition, the full name sally is more difficult to process than the pronoun she, despite the fact that both expressions unambiguously identify their referent. this is due to a phenomenon known as the repeated name penalty @xcite . all of these expressions unambiguously identify their target referents, and for some, the preferred referent is not necessarily the most recent. it is thus not straightforward to account for these effects solely on the basis of retrieval difficulty. various accounts of these and related coreference phenomena exist, such as the informational load hypothesis of @xcite , which posits a trade-off between the information content of referring expressions and their linguistic complexity. however, to our knowledge, little prior work has explored the role coreference might play in more general expectation-based theories of human sentence processing @xcite @xcite , according to which comprehension difficulty is driven by mechanisms that incrementally reallocate cognitive resources between competing interpretations of the unfolding sentence. expectation-based theories have been highly successful in providing broad-coverage explanations of sentence processing difficulty, but have so far been evaluated using local lexical (n-gram) or syntactic (parser-based) contexts @xcite @xcite . we show here that they can also be a useful framework for understanding coreference-related phenomena like those mentioned above, since knowledge of available discourse referents and patterns of referential form might guide comprehenders' expectations about upcoming words. in this study, we implement a novel coreference-aware generative incremental semantic parser and show that coreference awareness significantly improves fit of surprisal estimates from the parser to human reaction times in a self-paced reading experiment. we further show that a previously proposed estimator of coreference resolution difficulty explains additional variance over that explained by the parser, suggesting that the memory phenomenon is at least partially independent of expectation. together, these results indicate that comprehenders incrementally exploit coreference information both to identify referents of linguistic expressions (a memory effect) and to inform predictions about upcoming words (an expectation effect). our study thus joins previously reported evidence of the need for both memory-related and expectation-related mechanisms in accounting for the full range of human sentence processing phenomena @xcite . ## previous work coreference resolution has been extensively studied, both as a psycholinguistic phenomenon and as a natural language processing task. psycholinguistic investigations have largely focused on the memory retrieval mechanisms that allow comprehenders to identify the referents of linguistic expressions @xcite @xcite @xcite @xcite . some computational models have attempted to implement these theories in order to solve the coreference resolution task. for example, @xcite implement @xcite , @xcite is inspired by centering theory @xcite , and @xcite draw on the accessibility hierarchy @xcite for feature design. @xcite propose an incremental coreference resolution system inspired by centering and accessibility theories by maintaining a ranked stack of entities supposed to reflect the reader's mental state. @xcite offer a bayesian account of pronominal reference that highlights an asymmetry between pronoun production and interpretation. other computational models are less directly inspired by human cognition but achieve strong performance on the coreference resolution task. @xcite use lstms to track each entity cluster, where the hidden state is updated with each mention that is linked to the cluster, allowing for use of entity-level features. @xcite propose an end-to-end coreference resolution system without external resources using bidirectional lstms and an attention mechanism for head finding. however, none of the above systems integrate explicit coreference tracking into a full generative model of natural language. as a result, while such systems may be amenable to studying coreference retrieval processes in human comprehenders, they cannot be used to test the present hypothesis that coreference information guides comprehenders' overall expectations about upcoming words. also of relevance are recent high-performance large-scale language models like elmo @xcite , bert @xcite , and gpt-2 @xcite , which have been argued to learn coreference information @xcite . however, these models rely on powerful deep neural networks that are difficult to interpret, and it is generally not possible to manipulate their access to coreference information in order to evaluate the impact of this information on human expectations. @xcite propose an incremental generative model that explicitly tracks coreference but the joint objectives are not depedendent on each other, meaning that they do not model word expectation as dependent on the coreference decision. ## background the models used in this paper obtain surprisal predictors from an incremental processing model based on a left-corner parser, trained on corpora annotated with both syntactic and coreference information @xcite , which produces a simple incremental probabilistic account of sentence processing by making a single lexical attachment decision and a single grammatical attachment decision at each word. ## coreference model like the semantic parser described above, the coreference-aware parser foot_1 is trained with explicit supervision from annotated corpora to maximize the probability of all variable values. however, a number of changes and additions are necessary to add coreference information to the parser described above. the coreference-aware model marginalizes prefix probabilities off of entire sequences of durable preterminal signs p 1..t (for mathematical convenience, we henceforth split the lexical decision ℓ t above into a lexical decision ℓ t and a preterminal decision p t ) as well as the current store state q t : these sequence probabilities are again defined recursively using a transition model with a larger sequential context: since the size of this distribution increases exponentially with the length of the sentence, only hidden state sequences with optimal p 1..t-1 are maintained on a beam. this larger context is propagated to the lexical model: referential contexts for preterminals are then augmented with contexts of antecedents h p t-i ℓ t (highlighted), where this term is a zero vector in case there is no coreference: the grammatical decisions retain the original context but depend on the lexical decision. adding referential context h ℓ t conditioned on coreference index i ℓ t allows the probabability of lexical decision ℓ t to be influenced by the referential contexts of prior coreferent material. since grammatical decisions depend on the lexical decision in a given timestep, parsing and coreference are fully interactive. for example, at the word its in figure 2 , the model would consider coreference antecedent offsets including those corresponding to he (ten words back: i ℓ @xmath0: i ℓ @xmath1. with the correct antecedent, the referential context of its would inherit the cloud 1 context, making the following word rain more likely than if it had inherited the incorrect antecedent context he 1. coreference decisions are modeled as a conditional probability distribution of an offset index i ℓ t at time t, given the referential context sets of the base sign and of all previous preterminal signs: natural deduction rules for the coreference-aware parser are shown in figure 3 ; lexical and grammatical match rules follow the same structural outcomes but the presence of an additional preterminal list now allows for coreference information to be inherited as described in equation 13. ## experiment 1 to test the hypothesis that comprehenders use coreference information to guide linguistic expectations, the non-coreference parsing model described in section 3.3 and the joint parsing and coreference model described in section 4 are trained on version 5 of the coreference-annotated subset of the ontonotes corpus @xcite , totaling 12,256 sentences, reannotated into a generalized categorial grammar @xcite to provide valences and operators for referential contexts. in a baseline model, a variety of control predictors including the non-coreference-aware parser surprisal are then regressed to self-paced reading times from the natural stories @xcite corpus using the r lmer @xcite package. then, a full model augments the baseline model with a fixed effect for the coreference-aware surprisal estimate and a likelihood ratio test (lrt) is performed. by-subject random effects are included for all predictors in both models. ## conclusion in this study, we have argued that expectation-based theories of human sentence processing @xcite @xcite can be augmented to account for the influence of coreference information on human comprehenders' expectations about upcoming words. we proposed a novel coreference-aware generative incremental semantic parser and showed empirically that coreference awareness explains significant variance in human reading behavior. we nevertheless showed that a previously proposed effect of coreference information on memory retrieval survives in the presence of the coreference-aware predictability estimate, indicating that this effect cannot be explained away by expectation as currently implemented. 7 our results suggest that coreference information may be used by multiple subroutines in the language comprehension system, both to retrieve likely antecedents from memory and to inform expectations about upcoming words. this proposal is in line with prior arguments that both memory and expectation are necessary to account for the full range of human sentence processing phenomena.