Adverbs, Surprisingly (2023)

Abstract: This paper begins with the premise that adverbs are neglected in computational linguistics. This view derives from two analyses: a literature review and a novel adverb dataset to probe a state-of-the-art language model, thereby uncovering systematic gaps in accounts for adverb meaning. We suggest that using Frame Semantics for characterizing word meaning, as in FrameNet, provides a promising approach to adverb analysis, given its ability to describe ambiguity, semantic roles, and null instantiation.

## introduction adverbs are the part of speech (pos) that has seen the least attention in (computational) linguistics, likely due to its challenging nature @xcite . as @xcite state, "the adverb is a [. . . ] residual category @xcite to which words are assigned if they do not satisfy the more specific criteria for nouns, verbs, adjectives, prepositions, and conjunctions." syntactically, they modify many poss, except nouns (eat porridge quickly, hardly noticeable), or even complete clauses (probably, i'll come tomorrow). they are semantically varied @xcite , ranging from intensifiers/modifiers (absolutely, beautifully) to temporal and spatial specifications (yesterday, forward), to so-called speaker-oriented adverbs yielding inferences about speaker attitudes, beliefs, and evaluations. finally, adverbs can occupy different positions in sentences, creating complex issues of scoping and ambiguity @xcite . consider the following sentences: 1 (1) a. happily, they watched tv until dinner. b. they happily watched tv until dinner. c. they watched tv happily until dinner. d. they watched tv until dinner happily. in sum, adverbs provide crucial information not just about the where and how of events, but also about attitudes and evaluations. however, relatively little research on adverbs exists in computational linguistics, although lexical factors are generally recognized as central for many nlp tasks @xcite . lexical information is generally represented either in online dictionaries or by embeddings extracted from corpora @xcite @xcite . as a dictionary, wordnet @xcite ) lists adverbs but only provides a relatively impoverished account, while lexicons for sentiment analysis @xcite and hedging detection @xcite only consider specific subtypes of adverbs as to how they modulate the intensity of adjectives. on the distributional side, adverbs have been considered from a derviational perspective @xcite ; yet, they are rarely scrutinized in detail. among the standard benchmarks, only glue @xcite and blimp @xcite ) cover adverbs, and then only marginally. the same is true of approaches that combine dictionaries and embeddings @xcite . as a consequence, sota language models consistently struggle with adverb meaning, as section 2.2 will demonstrate empirically. this paper argues that frame semantics @xcite , as realized in framenet (fn) @xcite , provides an efficacious framework to articulate the relevant aspects of adverb meaning. specifically, as ex. 1 illustrates, lexical ambiguity is captured in terms of frame ambiguity. moreover, inferences about the arguments of adverbs, typically filled by the speaker and the lexical unit that the adverb modifies, can be cap-tured and characterized via the frame elements (i.e. semantic roles) of the frame. notably, framenet mechanisms will account for null-instantiated roles, allowing it to hint at unexpressed content in cases like example 2b (v. section 4.2 for details). in such cases specifically, framenet considerations of frame element realization help to explain the absence of the speaker semantic role in 2b. plan of the paper. section 2 defines the scope of this paper (speaker-oriented adverbs) and shows the lack of accounts for adverbs in nlp through a literature review. section 3 presents a probing dataset for speaker-oriented adverbs on the basis of which it demonstrates empirically that current large language models do not provide accounts for adverb meaning. section 4 provides general background information on framenet, gives details on the framework's approach to the description of adverb meaning, and suggests its use to improve nlp models. section 5 concludes the paper. ## case study: modeling adverb meaning as natural language inference one possibility, so far not mentioned, is that the knowledge inherent in large neural language models might provide a sufficient account of the meaning of (speaker-oriented) adverbs. in that case, at least from the nlp perspective, no (new) specific treatment would be required. however, this state of affairs is not the case, as we show below. ## describing adverbs in framenet this section will provide a brief background to framenet (section 4.1), show how framenet can be useful for the analysis of adverbs (section 4.2), survey the data on adverbs contained in the current version of the dataset (section 4.3), and propose concrete directions for next steps (section 4.4). ## conclusion conlon and evens (1992) stated that adverbs are under-researched in computational linguistics; this statement is still true. adverbs have received attention only in two applications: sentiment analysis and hedging detection. the large language models used here show systematic gaps in capturing adverb meaning. the problem is not solved. we propose that frame semantics, as embodied in framenet, along with improved techniques to mitigate the annotation effort to extend fn with new frames and annotations, can capture the meaning and implicatures of adverbs. considering frames as lexical constructions @xcite , this proposal fits well with recent work to combine language models and construction grammar @xcite . multiple ways exist for computational modeling to use such a resource, e.g., by extending the coverage of semantic role labellers to a larger range of adverbs, or by fine-tuning language models on large annotated datasets for which our probing dataset can serve as a blueprint.