Uncovering Constraint-Based Behavior in Neural Models via Targeted Fine-Tuning (2021)

Abstract: A growing body of literature has focused on detailing the linguistic knowledge embedded in large, pretrained language models. Existing work has shown that non-linguistic biases in models can drive model behavior away from linguistic generalizations. We hypothesized that competing linguistic processes within a language, rather than just non-linguistic model biases, could obscure underlying linguistic knowledge. We tested this claim by exploring a single phenomenon in four languages: English, Chinese, Spanish, and Italian. While human behavior has been found to be similar across languages, we find cross-linguistic variation in model behavior. We show that competing processes in a language act as constraints on model behavior and demonstrate that targeted fine-tuning can re-weight the learned constraints, uncovering otherwise dormant linguistic knowledge in models. Our results suggest that models need to learn both the linguistic constraints in a language and their relative ranking, with mismatches in either producing non-human-like behavior.

## introduction ever larger pretrained language models continue to demonstrate success on a variety of nlp benchmarks (e.g., @xcite . one common approach for understanding why these models are successful is centered on inferring what linguistic knowledge such models acquire (e.g., @xcite @xcite . linguistic knowledge alone, of course, does not fully account for model behavior; non-linguistic heuristics have also been shown to drive model behavior (e.g., sentence length; see @xcite . nevertheless, when looking across a variety of experimental methods, models appear to acquire some grammatical knowledge (see @xcite . however, investigations of linguistic knowledge in language models are limited by the overwhelming prominence of work solely on english (though see @xcite @xcite . prior work has shown nonlinguistic biases of neural language models mimic english-like linguistic structure, limiting the generalizability of claims founded on english data (e.g., @xcite . in the present study, we show via cross-linguistic comparison, that knowledge of competing linguistic constraints can obscure underlying linguistic knowledge. our investigation is centered on a single discourse phenomena, implicit causality (ic) verbs, in four languages: english, chinese, spanish, and italian. when an ic verb occurs in a sentence, interpretations of pronouns are affected: in (1), both lavender and kate agree in gender with she, so both are possible antecedents. however, english speakers overwhelmingly interpret she as referring to lavender in (1-a) and kate in (1-b). verbs that have a subject preference (e.g., frightened) are called subject-biased ic verbs, and verbs with an object preference (e.g., admired) are called object-biased ic verbs. ic has been a rich source of psycholinguistic investigation (e.g., @xcite @xcite . current accounts of ic ground the phenomenon within the linguistic signal without the need for additional pragmatic inferences by comprehenders (e.g., @xcite . recent investigations of ic in neural language models confirms that the ic bias of english is learnable, at least to some degree, from text data alone @xcite . the ability of models trained on other languages to acquire an ic bias, however, has not been explored. within the psycholinguistic literature, ic has been shown to be remarkably consistent crosslinguistically (see @xcite . that is, ic verbs have been attested in a variety of languages. given the crosslinguistic consistency of ic, then, models trained on other languages should also demonstrate an ic bias. however, using two popular model types, bert based @xcite and roberta based @xcite ), 1 we find that models only acquired a human-like ic bias in english and chinese but not in spanish and italian. we relate this to a crucial difference in the presence of a competing linguistic constraint affecting pronouns in the target languages. namely, spanish and italian have a well studied process called pro drop, which allows for subjects to be 'empty' @xcite ). an english equivalent would be "(she) likes bert" where she can be elided. while ic verbs increase the probability of a pronoun that refers to a particular antecedent, pro drop disprefers any overt pronoun in subject position (i.e. the target location in our study). that is, both processes are in direct competition in our experiments. as a result, spanish and italian models are susceptible to overgeneralizing any learned pro-drop knowledge, favoring no pronouns rather than ic-conditioned pronoun generation. to exhibit an ic bias, models of spanish and italian have two tasks: learn the relevant constraints (i.e. ic and pro drop) and the relative ranking of these constraints. we find that the models learn both constraints, but, critically, instantiate the wrong ranking, favoring pro drop to an ic bias. using fine-tuning to demote pro drop, we are able to uncover otherwise dormant ic knowledge in spanish and italian. thus, the apparent failure of the spanish and italian models to pattern like english and chinese is not evidence on its own of a model's inability to acquire the requisite linguistic 1 these model types were chosen for ease of access to existing models. pretrained, large auto-regressive models are largely restricted to english, and prior work suggests that lstms are limited in their ability to acquire an ic bias in english @xcite . ## related work this work is intimately related to the growing body of literature investigating linguistic knowledge in large, pretrained models. largely, this literature articulates model knowledge via isolated linguistic phenomena, such as subject-verb agreement (e.g., @xcite , negative polarity items (e.g., @xcite , and discourse and pragmatic structure (including implicit causality; e.g., @xcite @xcite . our study differs, largely, in framing model linguistic knowledge as sets of competing constraints, which privileges the interaction between linguistic phenomena. prior work has noted competing generalizations influencing model behavior via the distinction of non-linguistic vs. linguistic biases (e.g., @xcite @xcite . the findings in @xcite , that linguistic knowledge is represented within a model much earlier than attestation in model behavior, bears resemblance to our claims. we find that linguistic knowledge can, in fact, lie dormant due to other linguistic processes in a language, not just due to non-linguistic preferences. our findings suggest that some linguistic knowledge may never surface in model behavior, though further work is needed on this point. in the construction of our experiments, we were inspired by synthetic language studies which probe the underlying linguistic capabilities of language models (e.g., @xcite . we made use of synthetically modified language data that accentuated, or weakened, evidence for certain linguistic processes. the goal of such modification in our work is quite similar both to work which attempts to remove targeted linguistic knowledge in model representations (e.g., @xcite and to work which model lang tokens bert en 3.3b roberta en 30b chinese bert zh 5.4b chinese roberta zh 5.4b beto es 3b ruperta es 3b italian bert it 2b umberto it 0.6b gilberto it 11b in the present study, rather than identifying isolated linguistic knowledge or using priming to study relations between underlying linguistic representations, we ask how linguistic representations interact to drive model behavior. ## models prior work on ic in neural language models has been restricted to autoregressive models for ease of comparison to human results (e.g., @xcite . in the present study, we focused on two popular non-autoregressive language model variants, bert @xcite and roberta @xcite . we used existing models available via huggingface @xcite . multilingual models have been claimed to perform worse on targeted linguistics tasks than monolingual models (e.g., @xcite . we confirmed this claim by evaluating mbert which exhibited no ic bias in any language. 2 thus, we focus in the rest of this paper on monolingual models (summarized in ## experimental stimuli and measures our list of target verbs was derived from existing psycholinguistic studies of ic verbs. 4 for english, we used the ic verbs from @xcite . each verb in the human experiment was coded for ic bias based on continuations of sentence fragments (e.g., kate accused bill because ...). for spanish, we used the ic verbs from @xcite , which followed a similar paradigm as @xcite for english. participants were given sentence fragments and asked to complete the sentence and circle their intended referent. the study reported the percent of subject continuations for 100 verbs, from which we used the 61 verbs which had a significant ic bias (i.e. excluding verbs with no significant subject or object bias). for italian, we used the 40 ic verbs reported in @xcite . human participants were given ambiguous completed sentences with no overt pronoun like "john feared michael because of the kind of person (he) is" and were asked to judge who the null pronoun referred to, with the average number of responses that gave the subject as the antecedent reported. 5 for chinese, we used 59 ic verbs reported in @xcite , which determined average subject bias per verb in a similar way as @xcite (i.e. judgments of antecedent preferences given ambiguous sentences, this time with overt pronouns). 6 we generated stimuli using 14 pairs of stereotypical male and female nouns (e.g., man vs. woman, husband vs. wife) in each language, rather than rely on proper names as was done in the human experiments. the models we investigated are bidirectional, so we used a neutral right context, was there, for english and spanish, where human ex4 all stimuli, as well as code for reproducing the results of the paper are available at https://github.com/ forrestdavis/implicitcausality . for each language investigated, the stimuli were evaluated for grammaticality by native speakers with academic training in linguistics. as exemplified in (1), repeated below, ic verb bias modulates the preference for pronouns. an object-biased ic verb (e.g., admired) should increase the likelihood of pronouns that refer to the object, and a subject-biased ic verb (e.g., frightened) should increase the likelihood of reference to the subject. given that all the investigated stimuli were disambiguated by gender, we categorized our results by the antecedent of the pronoun and the ic verb bias. we first turn to english and chinese, which showed an ic bias in line with existing work on ic bias in autoregressive english models (e.g., @xcite . we then detail the results for spanish and italian, where only very limited, if any, ic bias was observed. ## pro drop and implicit causality: competing constraints we were left with an apparent mismatch between models of english and chinese and models of spanish and italian. in the former, an ic verb bias modulated pronoun preferences. in the latter, the same ic verb bias was comparably absent. recall that, for humans, the psycholinguistic literature suggests that ic bias is, in fact, quite consistent across languages (see @xcite . we found a possible reason for why the two sets of models behave so differently by carefully considering the languages under investigation. languages can be thought of as systems of competing linguistic constraints (e.g., optimality theory; @xcite . spanish and italian exhibit pro drop and typical grammatical sentences often lack overt pronouns in subject position, opting instead to rely on rich agreement systems to disambiguate the intended subject at the verb @xcite . this constraint competes with ic, which favors pronouns that refer to either the subject or the object. chinese also allows for empty arguments (both subjects and objects), typically called discourse pro-drop @xcite . 11 as the name suggests, however, this process is more discourse constrained than the process in spanish and italian. for example, in chinese, the empty subject can only refer to the subject of the preceding sentence (see @xcite . as a means of comparison, in surveying three universal dependencies datasets, foot_6 8% of nsubj (or nsubj:pass) relations were pronouns for chinese, while only 2% and 3% were pronouns in spanish and italian respectively. english lies on the opposite end of the continuum, requiring overt pronouns in the absence of other nominals (cf. he likes nlp and *likes nlp). therefore, it's possible that the presence of competing constraints in spanish and italian obscured the underlying ic knowledge: one constraint preferring pronouns which referred to the subject or object and the other constraint penalizing overt pronouns in subject positions (i.e. the target position masked in our experiments). in the following sections, we removed or otherwise demoted the dominance of each model's pro-drop constraint for spanish and italian, and introduced or promoted a pro-drop like constraint in english and chinese. we found that the degree of ic bias in model behavior could be controlled by the presence, or absence, of a competing pro-drop constraint. ## discussion the present study investigated the ability of roberta and bert models to demonstrate knowledge of implicit causality across four languages (recall the contrast between lavender frightened kate and lavender admired kate in (1)). contrary to humans, who show consistent subject and objectbiased ic verb preferences across languages (see @xcite , bert and roberta models of spanish and italian failed to demonstrate the full ic bias found in english and chinese bert and roberta models (with our english results supporting prior work on ic bias in neural models and extending it to non-autoregressive models; @xcite . following standard behavioral probing (e.g., @xcite , this mismatch could be interpreted as evidence of differences in linguistic knowledge across languages. that is, model behavior in spanish and italian was inconsistent with predictions from the psycholinguistic ic literature, suggesting that these models lack knowledge of implicit causality. however, we found that to be an incorrect inference; the models did have underlying knowledge of ic. other linguistic processes influence pronouns in spanish and italian, and we showed that competition between multiple distinct constraints affects model behavior. one constraint (pro drop) decreases the probability of overt pronouns in subject position, while the other (ic) increases the probability of pronouns that refer to particular antecedents (subject-biased verbs like frightened favoring subjects and object-biased verbs like admired favoring objects). models of spanish and italian, then, must learn not only these two con-straints, but also their ranking (i.e. should the model generate a pronoun as ic dictates, or generate no pronoun in line with pro drop). by fine-tuning the models on data contrary to pro drop (i.e. with overt pronouns in subject position), we uncovered otherwise hidden ic knowledge. moreover, we found that fine-tuning a pro-drop constraint into english and chinese greatly diminished ic's influence on model behavior (with as little as 0.0004% of a models original training data). taken together, we conclude that there are two ways of understanding mismatches between model linguistic behavior and human linguistic behavior. either a model fails to learn the necessary linguistic constraint, or it succeeds in learning the constraint but fails to learn the correct interaction with other constraints. existing literature points to a number of reasons a model may be unable to learn a linguistic representation, including the inability to learn mappings between form and meaning and the lack of embodiment (e.g., @xcite . we suggest that researchers should re-conceptualize linguistic inference on the part of neural models as inference of constraints and constraint ranking in order to better understand model behavior. we believe such framing will open additional connections with linguistic theory and psycholinguistics. minimally, we believe targeted fine-tuning for constraint re-ranking may provide a general method both to understand what linguistic knowledge these models possess and to aid in making their linguistic behavior more human-like. ## conclusion and future work the present study provided evidence that model behavior can be meaningfully described, and understood, with reference to competing constraints. we believe that this is a potentially fruitful way of reasoning about model linguistic knowledge. possible future directions include pairing our behavioral analyses with representational probing in order to more explicitly link model representations and model behavior (e.g., @xcite or exploring constraint competition in different models, like gpt-2 which has received considerable attention for its apparent linguistic behavior (e.g., @xcite and its ability to predict neural responses (e.g., @xcite . siva reddy, dima taji, nizar habash, herman leung, marie-catherine de marneffe, manuela sanguinetti, maria simi, hiroshi kanayama, valeria de paiva, kira droganova, héctor martínez alonso, c ¸agrı c ¸öltekin, umut sulubacak, hans uszkoreit, vivien macketanz, aljoscha burchardt, kim harris, katrin marheinecke, georg rehm, tolga kayadelen, mohammed attia, ali elkahky, zhuoran yu, emily pitler, saran lertpradit, michael mandl, jesse kirchner, hector fernandez alcalde, jana strnadová, esha banerjee, ruli manurung, antonio stella, atsuko shimada, sookyoung kwak, gustavo mendonc ¸a, tatiana lando, rattima nitisaroj, and josie li. 2017. conll 2017 shared task: multilingual parsing from raw text to universal dependencies. in proceedings of the conll 2017 shared task: multilingual parsing from raw text to universal dependencies, pages 1-19. association for computational linguistics.