Common Sense Bias in Semantic Role Labeling (2021)

Abstract: Large-scale language models such as ELMo and BERT have pushed the horizon of what is possible in semantic role labeling (SRL), solving the out-of-vocabulary problem and enabling end-to-end systems, but they have also introduced significant biases. We evaluate three SRL parsers on very simple transitive sentences with verbs usually associated with animate subjects and objects, such as, “Mary babysat Tom”: a state-of-the-art parser based on BERT, an older parser based on GloVe, and an even older parser from before the days of word embeddings. When arguments are word forms predominantly used as person names, aligning with common sense expectations of animacy, the BERT-based parser is unsurprisingly superior; yet, with abstract or random nouns, the opposite picture emerges. We refer to this as “common sense bias” and present a challenge dataset for evaluating the extent to which parsers are sensitive to such a bias. Our code and challenge dataset are available here: github.com/coastalcph/comte

## introduction semantic role labeling (srl) refers to a shallow semantic dependency parsing that returns predicate-argument structures for input sentences; see figure 1 . modern-day srl systems, like most other nlp technologies, rely heavily on largescale language models. such language models are extremely useful for generalizing to out-ofvocabulary items, making subtle syntactic distinctions, and for capturing a range of lexical ambiguities; but they also introduce notable biases. previous work has shown that srl systems exhibit demographic biases @xcite ; we focus on a form of belief bias @xcite , which we will refer to as common sense bias, reflecting how language models encode conventional associations, which in many ways are indistinguishable from common sense (trinh and le, 2019). while demographic biases can lead to discrimination against under-represented demographics, belief biases can lead to discrimination against rare events; or, more precisely, lead srl systems to err on sentences that express unlikely states of affairs. this is what belief biases refer to in cognitive science @xcite : human preferences for conclusions that align with values, beliefs, and prior knowledge. belief biases in models can, like demographic biases, exacerbate societal challenges, e.g., anomaly detection, and also correlate with demographics, since groups differ in how much they engage with counterfactual and fictitious contents. we compare the errors of a modern, competitive srl system @xcite , based on bert @xcite , and show how it, unlike earlier srl systems, suffers from common sense bias: when confronted with sentences that, when read literally, express unlikely states of affairs, it can ignore obvious cues and produce false predicateargument structures even for very simple sentences. the sentence in figure 1 , for example, can be understood as expressing that the abstract concept of memory babysat the abstract concept of reasoning. the literal reading represents an unlikely state names countries abstract random-nouns random-strings figure 2 : examples of transitive sentences with person names, country names, abstract nouns, (randomly chosen) plural common nouns, or random strings as arguments. person names, and to some degree country names (which are often personified @xcite ), align with expectations of animacy. ## coarse-grained error analysis in our error analysis, we focus on simple threeword sentences that consist of a noun, a transitive verb, and a noun. the transitive verbs are handpicked to exhibit strong preferences for animate subjects and objects, low ambiguity, and predominantly realize their agents (a 0 ) as subjects, and their second argument (a 1 ) as objects. the error analysis consists of comparing performance across different types of subjects and objects and comprises examples such as those in figure 2 . the arguments exhibit various degrees of animacy associations, aligning more or less with common sense expectations. we obtain the names from the names library, foot_0 the country names from worldmap, foot_1 the abstract nouns from yourdictionary, foot_2 and common nouns from the princeton wordnet. 4 we assume a correct semantic parse associates subject with a 0 and object with a 1 (of the predicate introduced by the verb). this is obviously not true for all verbs @xcite . in ## fine-grained error analysis multitude of errors our first observation is that across all verb lemmata, the parser in @xcite produces many different output trees, depending on the argument word forms. for some lemmata, the error distribution is near-uniform across 15-20 outputs. it is well-established in srl that infrequent contexts lead to low confidence @xcite , explaining why common sense bias leads to a multitude of errors. ## comte: a test of common sense bias our challenge dataset foot_8 comte consists of 1,000 simple, three-word sentences with the same gold analysis: the second word is the predicate, the first word its a 0 , the last word its a 1 . the predicates are sampled at random from a list of six carefully selected verbs (see §3) that select for animate subjects and objects and consistently prefer these to be a 0 and a 1 . as before, we combine the verbs with names, countries, abstract nouns, plural common nouns, and random strings. the sentences were simply the first 1,000 sentences that we sampled this way, with 200 sentences in each category (names, countries, etc.) -and which satisfied a simple criterion: neither shi and lin ( 2019 ) nor @xcite would get it right. comte, in other words, consists of 1,000 trivial sentences that two competitive srl parsers failed to parse correctly. what can comte be used for? obviously, it can not be used to fine-tune parsers on, for example. it would take only a few examples to learn what is going on in the data, and training would likely lead to over-fitting. comte can also not be used to derive parsing performance figures that tell us much about the performance of parsers in the wild. the 1,000 sentences should, in our view, be thought of as a single probe into the degree to which a parser is sensitive to common sense bias. a parser should rarely err on the examples in the challenge dataset: they are all trivially simple, and while some argument words can be ambiguous, the verbs so strongly select for simple a 0 va 1 frames that parsers should unambiguously prefer this reading. if they don't, this is a sign they struggle with simple transitive sentences, like @xcite , or that they are prone to common sense bias, like @xcite . in order to quantify the degree to which the effect can be attributed to common sense bias, performance with names can be used as a baseline: if performance is much better for names than for some of the other categories, like with @xcite , this is an indicator of common sense bias.