Do pretrained transformers infer telicity like humans? (2021)

Abstract: Pretrained transformer-based language models achieve state-of-the-art performance in many NLP tasks, but it is an open question whether the knowledge acquired by the models during pretraining resembles the linguistic knowledge of humans. We present both humans and pretrained transformers with descriptions of events, and measure their preference for telic interpretations (the event has a natural endpoint) or atelic interpretations (the event does not have a natural endpoint). To measure these preferences and determine what factors influence them, we design an English test and a novel-word test that include a variety of linguistic cues (noun phrase quantity, resultative structure, contextual information, temporal units) that bias toward certain interpretations. We find that humans’ choice of telicity interpretation is reliably influenced by theoretically-motivated cues, transformer models (BERT and RoBERTa) are influenced by some (though not all) of the cues, and transformer models often rely more heavily on temporal units than humans do.

## introduction large pretrained-language models (elmo: @xcite @xcite keep achieving new states of the art in a variety of nlp tasks, leading to a growing interest in exploring what has been acquired by the pretraining objectives. many recent works utilize probes: shallow, usually supervised classifiers that try to determine which linguistic phenomena are predictable from the pretrained representations. the linguistic properties studied include syntactic relationships (hewitt and manning, 2019), morphological information @xcite , and semantic knowledge and entailment @xcite , and the representations studied include sentence embeddings @xcite @xcite and token/word-level embeddings @xcite . recent work has also explored unsupervised analysis of attention heads instead of supervised training @xcite @xcite . another approach to understanding pretrained language models is to test their behavior on psycholinguistic tasks. stimuli in psycholinguistic tasks are typically designed to reveal linguistic bias in human behaviors (e.g., grammatical judgments, reading speeds, neural responses). evaluating pretrained language models on such tasks can thus provide insights on the linguistic biases acquired by the models. the linguistic properties studied include subject-verb agreement @xcite , filler-gap depedencies @xcite , garden-path effects @xcite , other types of syntactic awareness @xcite , and variations of grammatical judgment based on availability of contexts @xcite . most works focus on syntactic knowledge (though see @xcite 's work on semantic and pragmatic aspect). our work contributes to this line of research. while most previous studies probe syntactic properties, we investigate a semantic property: telicity (whether an event has reached an end point or not). telicity encoding is covert in english and needs to be inferred from information such as inherent telicity of the main verb, the argument structure, the quantity of noun phrases, and other constituents @xcite . for instance, the description "john read the book" might allow both telic (he finished reading the book) or atelic (he did not finish reading the book) interpretations, while the description "john reached the park" has only a telic interpretation. there is a rich literature on what linguistic information biases listeners toward which types of interpretations. thus, our study aims first to test, given a description of an event, which linguistic structures influence interpretation preference, both for humans and pretrained language models. we test telicity preference using an adverbial phrase task frequently used in semantics that is also well-aligned with the masking task used during model pretraining. this allows us to probe telicity preference without fine-tuning on telicity and is directly comparable to the task we assign to human subjects. it also avoids tokenization issues since the prepositions are single word-pieces. we also extend these english sentence tests to novelword tests where we replace content words such as verbs and nouns with made-up words guaranteed to be in neither the vocabulary of native english speakers nor the vocabulary of the pretrained models. novel-word tests have deep roots in psycholinguistic studies and are often used to evaluate linguistic generalizability @xcite . in our experiments, they allow us to test to what extent linguistic features such as plurality still affect the telicity preferences in novel contexts. our findings suggest that humans attend to the cues predicted by linguistic literature in both real and novel conditions, and that pretrained transformers have acquired some but not all of these cues: ## background on telicity telicity is an important semantic property that indicates whether an event has an inherent endpoint (telos) or boundary. for instance, the description 'john solved the problem' typically elicits a telic interpretation where the event reaches the endpoint when the problem gets resolved. in contrast, the description 'john gazed at the sunset' typically elicits an atelic interpretation where there is no clear boundary when the event is completed. the property also plays an important role in temporal inference. for example, when a punctual temporal adverbial phrase modifies an atelic event (e.g., 'john ran at 8 am'), the timestamp is inferred as the inception time of the event, but when it modifies a telic event (e.g., 'john arrived at 8 am'), the timestamp is inferred as the endpoint of the event. english encodes telicity with both overt grammatical aspect (e.g., progressives or perfectives) and covert situational (lexical) aspect. we focus on the situational aspect of telicity because it is considered the fundamental aspectual class @xcite and its coding is non-transparent, i.e., it must be constructed by the human or model. ## method we adapted the time adverbial test @xcite @xcite into forced-choice fill-in-the-blank tests. for human subjects, we ask participants to select "in" or "for" to fill in the blank of a given sentence, e.g.: john loved mary 2 years. a. in b. for for pretrained transformers, we used maskedlanguage modeling. specifically, we replace the preposition with the masking token, e.g., john loved mary [mask] 2 years and then compare the probability at the [mask] token of predicting 'for' vs. predicting 'in', and select the preposition with the higher probability. ## discussion we tested how telicity interpretation preferences among human subjects and pre-trained transformers are influenced by a variety of linguistic information-verb type, noun phrase quantity, resultative structure, and contextual informationunder the assumption that the preposition choice ('in'/'for') in time adverbial phrases reflects their intepretation preference (telic or atelic) for a given description of an event. survey results can be found in https://github.com/ yiyunzhao/telicity-probing.git we tested on both natural english and novelword sets. we found that humans reliably used all theoretically-motivated cues with both natural english and novel predicates, passing our strict decision measure in all cases except resultatives with novel predicates (and there they at least passed our soft ranking measure). we found that transformers followed humans in being highly sensitive to verbs with a strong telicity preference, and to number and non-quantized object noun phrases. however, for most other theoretically motivated cues, transformer sensitivity was weaker, and they passed only our soft ranking measure, not our strict decision measure, with resultatives being especially challenging. we also found that both human and transformers' preferences are affected by temporal units, with humans relying on this cue less than other cues but transformers relying on it heavily. while time adverbial tests are easy to pose to both humans and pre-trained transformers, these tests have some limitations. though choice of preposition hints at the participant's interpretation, we do not see the participant's exact mental picture. for example, we found unexpectedly variable responses to some well cited stimuli, e.g., 52.54% of participants preferred 'for' in 'john died in/for 10 minutes', a standard example where 'in' is expected. for human participants, it would be useful to ask those who preferred 'john died for 10 minutes' to describe the specific scene in their mind: perhaps they conceived of a specific scenario where john died but was resuscitated. for transformers, eliciting such descriptions is an open area for future research. because the adverbial test aligns so well with the task on which the transformer models were pretrained, one might worry that it overestimates the understanding of the transformer models as they could perhaps solve the fill-in-the-blank problem without really understanding telicity. this is why we individually tested several different types of telicity cues, and indeed found that transformers failed to use many of these cues as well as humans. still, future work could explore temporal inference and counting tests for additional insights. for example, one could ask "how long does it take john to finish the book" in the sentence "john read a book in an hour" (≤1 hour) vs. "john read a book for an hour" (≥1 hour). extracting such temporal inferences from pre-trained models is an interesting direction for future research.