The Possible, the Plausible, and the Desirable: Event-Based Modality Detection for Language Processing (2021)

Abstract: Modality is the linguistic ability to describe vents with added information such as how desirable, plausible, or feasible they are. Modality is important for many NLP downstream tasks such as the detection of hedging, uncertainty, speculation, and more. Previous studies that address modality detection in NLP often restrict modal expressions to a closed syntactic class, and the modal sense labels are vastly different across different studies, lacking an accepted standard. Furthermore, these senses are often analyzed independently of the events that they modify. This work builds on the theoretical foundations of the Georgetown Gradable Modal Expressions (GME) work by Rubinstein et al. (2013) to propose an event-based modality detection task where modal expressions can be words of any syntactic class and sense labels are drawn from a comprehensive taxonomy which harmonizes the modal concepts contributed by the different studies. We present experiments on the GME corpus aiming to detect and classify fine-grained modal concepts and associate them with their modified events. We show that detecting and classifying modal expressions is not only feasible, it also improves the detection of modal events in their own right.

## introduction modality refers to the linguistic ability to describe alternative ways the world could be. 1 modal expressions aim to identify wishes, rules, beliefs, or norms in texts @xcite , which is a crucial part of natural language understanding (nlu) @xcite . concretely, events in natural language are often reported in a manner that emphasizes non-actual perspectives on them, rather than their actual propositional content. consider examples (1a)-(1b): these sentences cannot be verified or falsified simply by examining whether p actually came or will come to pass, and in fact, such verification is not the goal of this way of reporting. rather, speakers describe such events in order to indicate plans (2a), desires (2b), norms (2c), or the assessed plausibility (2d) of the associated propositional content p. investigating how to classify these perspectives on events has been the focus of extensive research on modality in theoretical linguistics @xcite @xcite . in terms of nlp technology, modal concepts as expressed in (2) are relevant to many downstream tasks, such as the automatic detection of hedging and speculation @xcite , uncertainty @xcite @xcite , opinion @xcite @xcite , and factuality @xcite . although these tasks rely on modality features, so far there is no accepted standard for modal concepts and labels, which aligns with the semantic space of modal senses that linguists identify. consequently, modality features are either treated idiosyncratically or are absent from semantic frameworks @xcite . in support of such downstream tasks, a different type of nlp investigations targets modality annotation and detection in its own right @xcite ; @xcite ; @xcite ; @xcite ; @xcite ; @xcite ; @xcite ; @xcite ; @xcite , and others). however, each of these studies creates its own scheme, and none of these schemes has been picked up as an accepted standard by the community. moreover, different endeavors suffer from one (or more) of the following types of deficiencies with respect to their expressivity and coverage. first, many studies limit the modal triggers, i.e., the expressions that trigger the modal meaning, to a closed class of auxiliary verbs (e.g., can, might, should, must in english @xcite @xcite ). however, as acknowledged by linguists @xcite and nlp researchers @xcite @xcite , words of any part-of-speech (pos) can trigger modality. consider, for instance, the following triggers: we should remain calm (aux); we have a plan to reduce the costs (noun); our agency prefers this equipment (verb); marx is probably patriotic (adv); devaluation has been necessary (adj). second, the modal senses, i.e., the labels that indicate the modal perspectives, differ from one study to another, with no accepted standard. some studies focus only on a particular sense, such as epistemic modality @xcite . others use labels that mix modal senses with orthogonal notions (e.g., force, distinguishing permission from requirement as in @xcite ), thereby making their deployment into existing annotations and tasks less transparent. in general, there is no single annotation standard that covers the full spectrum of modal senses attested in the data and confirmed by the latest linguistic theories, as portrayed by @xcite . finally, modality detection in nlp has often been cast as a word-sense disambiguation (wsd) task @xcite or as a sentence-classification task @xcite . both perspectives are insufficient for any practical use. the latter is too coarse-grained, as a sentence may contain multiple events, each of which potentially carries a different modal sense. the former is uninformative, because the modal trigger is not explicitly associated with the event being modified. @xcite take a step in the right direction, offering to annotate modal sense constructions. the current work proposes to address all of the aforementioned deficiencies as follows. we define a prediction task that we term event-based modality detection, where, given a sentence as input, we aim to return all of its modal triggers, their associated modal senses, and, for each trigger, the respective event being modified. crucially, the modal triggers can be from any syntactic class. the modal senses are drawn from a single taxonomy that we motivate based on linguistic research and which harmonizes the different modal concepts contributed in previous studies ( §3). finally, we propose to view modal triggers as semantic modifiers of eventive heads in event-based (a.k.a., neo-davidsonian; @xcite ) semantics. this is motivated by practical concerns -when extracting events from texts to benefit downstream tasks, one would want easy access to the features that indicate the perspective on each event, above and beyond its participants. the accompanying annotation standard we assume for the task is based on the georgetown gradable modal expressions (gme) framework @xcite , with two simplifications that are designed to allow for more consistent annotations and increased ease-of-use by non-experts. first, we change the modal sense labels to be intuitive and self-explanatory. second, instead of the event span (a.k.a., prejacent) in the gme, we mark the head of the event being modified. to assess the feasibility of the proposed task, we use the gme corpus @xcite to train and test the automatic detection of modal triggers, their senses, and associated events. our experiments show that while identifying a closed set of auxiliary verbs as modal triggers is straightforward, expanding the set of triggers to any syntactic class indeed makes it a harder task. notwithstanding this difficulty, we show that a model based on large pre-trained contextualized embeddings @xcite obtains substantial improvements over our baseline on the full task. moreover, we show that detecting modalized events in fact improves with the availability of information about the modal triggers. all in all, we contribute a new task, a new standard and a set of strong baselines for the eventbased modality task we defined. ## linguistic background modal expressions allow language users to discuss alternative realities. for example, the sentence she can reach the ceiling is modal because it describes the event of her reaching the ceiling as feasible, but potentially non-actual. similarly, she hopefully will reach the ceiling is modal because it describes such an event as desirable, and likewise potentially non-actual. a sentence like she was reported to reach the ceiling describes the event of her reaching the ceiling as potentially actual, according to one's state of knowledge, yet implying that in reality it could have been otherwise. over the last 40 years linguists have achieved an increasingly refined understanding of how to classify modal senses. the most traditional and fundamental distinction is between epistemic modals and non-epistemic modals (also called root modals). epistemic modals have to do with knowledge and plausibility of the event actually happening. nonepistemic modals have to do with agent actions and motivations underlying the events. 2 epistemic modality is not a unified class. some modals express a perspective on the event that is based on knowledge, while others express a perspective related to the objective chance of the event happening (a.k.a., circumstantial modality in @xcite ). furthermore, linguists posit two types of non-epistemic modal senses: one which focuses on the objective abilities and dynamic unfolding of events @xcite , and another which focuses on subjective reasons to prioritise one event over another @xcite . within the latter subtype there are further subdivisions according to whether the event is prioritised in terms of norms (deontic), desires/preferences (bouletic), or goals/plans (teleological) @xcite @xcite . the traditional three-way classification of modal senses into deontic, epistemic, and dynamic, which has been used in previous nlp work (e.g., @xcite ; @xcite ), did not attend to these subdivisions, which are nonetheless expected to be important for reasoning and other tasks that require deep understanding. @xcite make finer-grained distinctions in the non-epistemic case, distinguishing between requirements, permissions, wants, and intentions, but not all of these in fact track distinct modal senses. for example, their "require" modality conflates both rule-based obligations and goal-oriented preferences. most importantly, the discussion of modality in nlp often resorts to linguistic regimes that are not understandable by non-linguists and non-expert practitioners, making the output of these systems essentially unusable for nlp engineers and designers of downstream tasks. this paper aims to bridge this gap, offering a single task and annotation standard that cover the rich space of concepts, while being intuitively understandable and easy-to-use. ## event-based modality detection: proposed task definition we propose an event-based modality detection task that rests upon three assumptions: (i) the set of possible modal triggers is open-ended, and may be of any pos tag, (ii) the associated modal senses are fine-grained and form an hierarchical taxonomy, and (iii) each trigger is associated with an event. consider, for instance, the following examples: in these examples, the words in bold indicate the modal expression, which we call a trigger. the co-indexed items in italics mark the head of the event for which the modal perspective is ascribed. in (3a), 'reported' triggers a modal perspective on the event of 'being (in custody)'. in (3b), 'believed' triggers a modal perspective on the 'making' event, and 'possible' indicates a modal perspective on the 'seeing (the satellite)' event. clearly, the modal perspectives on these events, i.e., the modal senses, are of different types. how should we label these fine-grained modal senses? a hierarchical taxonomy of modal senses having established that a given expression serves as a modal trigger, we are interested in classifying the particular sense, or perspective, that it assigns to the modal event. figure 1 presents the complete taxonomy that we propose for modal sense classification in nlp. it is based on the modal senses proposed and justified by @xcite , with a few simplifications that make it intuitive and easyto-use by nlp practitioners and non-linguists. 4 the highest level of the hierarchy tracks the distinction between events whose plausibility is being assessed, and events whose priority is stated. more specifically, plausibility has to do with events that are expected to happen or not happen, given a relevant set of assumptions which are made explicit. plausibility can be assessed based on our state of knowledge ("i heard i she got married i "), based on what is objectively probable due to facts about the world ("the ice cream will definitely i melt i in the sun"), or based on inherent (physical) abilities of an agent ("i can i easily swim i 10 km"). in contrast, the priority branch marks a perspective where events are prioritized, or considered "good" by the speaker (or more generally, by a relevant attitude holder) @xcite . events can be preferred because they are normatively obliged or commendable ("you should i n't drink and drive i ), because they realize a goal ("the plan i to reduce i costs in q2"), or because they are otherwise desirable, as a matter of personal taste or preference ("i will preferably i meet i them over lunch"). to make these notions accessible, we assign intuitive labels to these fine-grained concepts. on the plausibility side, we distinguish plausibility based on the state of knowledge (previously, epistemic), plausibility based on a state of the world (circumstantial), and plausibility based on the objective abilities of the agent (dynamic). on the priority side, we distinguish priorities based on rules and norms (deontic), priorities based on desires and wishes (bouletic), and priorities based on plans and goals (teleological). as illustrated in the proposed taxonomy unifies and harmonizes the different modal senses offered by previous studies. importantly, we enrich the epistemic-deonticdynamic classification used in previous nlp research @xcite with the finer-grained notions introduced by @xcite and refer to the various labels in work by @xcite ; @xcite ## experimental setup goal we set out to assess the feasibility of our proposed event-based modality task. concretely, we would like to gauge how well we can learn to detect and classify the different levels of modal senses afforded by our taxonomy ( §3) and to identify the events modified by the triggers. ## conclusion we propose an event-based modality detection task which is based on solid theoretical foundations yet is adapted to fit the needs of nlp practitioners. the task has three facets: modal triggers can be of any syntactic type, sense labels are drawn from a unified taxonomy we propose, and modal triggers are associated with their modified events. we propose this task and standard as a potential extension for standard semantic representations (amr, sdg, ucca, etc.) towards easy incorporation of modal events as features in downstream tasks.