Reasoning Requirements for Indirect Speech Act Interpretation (2020)

Abstract: We perform a corpus analysis to develop a representation of the knowledge and reasoning used to interpret indirect speech acts. An indirect speech act (ISA) is an utterance whose intended meaning is different from its literal meaning. We focus on those speech acts in which slight changes in situational or contextual information can switch the dominant intended meaning of an utterance from direct to indirect or vice-versa. We computationalize how various contextual features can influence a speaker’s beliefs, and how these beliefs can influence the intended meaning and choice of the surface form of an utterance. We axiomatize the domain-general patterns of reasoning involved, and implement a proof-of-concept architecture using Answer Set Programming. Our model is presented as a contribution to cognitive science and psycholinguistics, so representational decisions are justified by existing theoretical work.

## introduction understanding natural language utterances inherently involves resolving ambiguity associated with the meaning of linguistic expressions. resolving linguistic ambiguity involves choosing one amongst many possible meanings, at the word level, at the sentence level, and at the discourse level. word and sentence embeddings are current topics of active research and focus on selecting suitable word or sentence representations that best capture meaning. current transformer architectures such as bert @xcite are used to pretrain language representations so word and sentence embeddings account for nearby linguistic context. these systems are based on two key insights that we explore in this paper. the first insight is that word meanings (in the case of bert) and span meanings (in the case of spanbert @xcite ) cannot be understood separate from their context, which in these cases is primarily linguistic context. using these contextualized language embeddings have led to state-of-the-art performance in several downstream tasks. however, there is still the open problem of how to incorporate extra-linguistic context. specifically, it is unclear how to categorize and use knowledge derived from common sense knowledge about how the world works as well as situational knowledge about aspects of the physical environment in a systematic way for nlu. the second insight is that the way context influences meaning can be entirely captured statistically from data. that is, a word encountered together with context in a particular way is likely to mean the same as the same word encountered in a similar linguistic context. the assumption is that systems with sufficient training data, or at least with powerful pretrained models are likely to interpret language correctly. however, @xcite and others have argued that pattern recognition by itself might be insufficient, and such an association might require logical reasoning beyond learning statistical similarity. recent work by @xcite explored both these insights and argued that for the task of coreference resolution (at least with respect to imperative discourse), to interpret a pronoun an nlu system must reason about actions and change, normative obligations and social aspects, and the intent of speakers. knowledge from situational context combined with background domain-general knowledge is needed to perform this reasoning. meaning is derived from choosing the interpretation that makes the "most sense" (not just in terms of statistical regularity, but in terms of coherence more broadly) given a variety of contextual factors. we are inspired by this key idea to explore how context and reasoning can assist not only at the word level (as @xcite , but at the sentence level. specifically, we address the problem of how a listener can infer the intended meaning of an utterance given contextual knowledge. sometimes, as in indirect speech acts (isas), the literal meaning of a sentence (derived from the surface form) can be different from its intended meaning @xcite @xcite . for example, the utterance "can you open the door?" has a literal meaning of an elicitation of information from the listener regarding the listener's ability to open the door -this is because the utterance has the surface form of a question. and in fact, the speaker may in some cases be interested in whether the hearer is capable of opening the door: if the hearer is physically disabled and the speaker is a caregiver, for example. however, very frequently, the intended meaning of the utterance is a request that the listener perform the action of opening the door, in which case the utterance is an isa. as this example suggests, the proper interpretation is guided by context. research has shown that isa use is common in human-human communication @xcite , as well as in human-robot task-based dialogues @xcite foot_0 . the problem, however, is that isas are notoriously difficult to computationalize when their interpretation is influenced by contextual factors @xcite . unlike past approaches to isa interpretation, we incorporate a broader notion of context and extended reasoning capabilities to reason about the speaker's intent and beliefs. our contributions include a computational formalization of (1) the preconditions, related to the speaker's intentions and beliefs, that need to be satisfied to determine if a surface form carries indirect meaning, (2) the reasoning needed to infer speaker beliefs based on domain general principles about the world (e.g., inertia), expectations of linguistic alignment, and mutual knowledge and (3) the representations needed to assimilate factual contextual evidence available to the listener into the reasoning process. to make this work more tractable, we limit our focus to speech acts that have the surface form of a question, the literal meaning of an ask for information, and the intended meaning of an ask for information (in which case the speech act is not an isa) or a request for action (in which case the speech act is an isa). in the tradition of past work in computationally-aided linguistic analysis, these formalizations are derived using examples from several task-oriented dialogue corpora. ## approach our approach is based on two traditions. first, the tradition from computational linguistics of building formal representations of linguistic phenomena from corpora, guided by linguistic and cognitive scientific theories. recent papers in this tradition were presented by @xcite and @xcite . we emphasize that the model that we build from this corpus analysis is a cognitive scientific model, which builds on theories from cognitive science and psycholinguistics as described in section 3. second, the tradition of building explicit knowledge representation formalisms along with reasoners capable of deriving meaning of natural language expressions from their logical forms. recent work in this tradition has been performed by @xcite , @xcite , and @xcite . combining these traditions, we perform a corpus analysis to extract archetypal utterances from a variety of corpora; this process includes a consensus annotation of those utterance's direct and indirect meanings, as well as of the context that influences that interpretation, as described in section 2.1. then we build a formal computational model of the representations and reasoning required to perform those interpretations, ensuring that the computational model performs a complete coverage of the archetypal utterances, as described in section 2.2. the resulting model is detailed in section 3. ## conclusion in this paper we present a novel cognitive scientific model of the reasoning involved in automatically interpreting indirect speech acts (isas), empirically derived from a corpus analysis. the approach is grounded in existing cognitive science and psycholinguistic theories and provides a domain-general formal axiomatization of the reasoning needed to uncover the intended meanings of isas. per the approach, the listener reasons about the speaker's intentions and beliefs, using contextual evidence that both the speaker and listener have access to. unlike previous approaches, we incorporate reasoning about a speaker's beliefs about the listeners capabilities, socio-normative standards, and speaker preferences. moreover, extending past approaches, we provide a way to incorporate various aspects of extra-linguistic context into this reasoning process. following traditions of building formal representations from corpora, we ensure that our representational choices completely cover utterances across several task-oriented dialogue corpora that feature isas. we focus on those utterances that have similar surface form, but different meanings due to contextual factors. in future work, we intend exploring how linguistic norms of directness, politeness and brevity, associated with isas @xcite ) can be incorporated into the reasoning process either within or as an extension to brown's model. we also plan to compare our approach to existing models in inferring isas. we encode the formalism in a logic-based language for knowledge representation and reasoning. we use a logical representation for several reasons. first, such a representation allows for explainable processing; specifically, a system would be able to justify the "thought-process" behind a given interpretation. such explainability allows the human user to detect mistakes in and debug the system, and helps build trust in the system @xcite . second, such a representation could be used to support online learning: if a system produced a given interpretation, and it later discovered that the interpretation was incorrect, it would have an explicit representation that it could use to reason about whether the error was in its contextual evidence, its domain-general rule application, or its preconditions. although it may be argued from a data-driven perspective that if enough of the right type of data were embedded in the right input vector then bert might be sufficient, the thrust of this paper is that statistical correlations do not capture the intricate reasoning that is needed to derive the meaning of isas. furthermore, it is currently unknown how to perform abductive logical reasoning in a neural net. while there have been recent attempts in producing neural architectures capable of simple one-hop logical deductions, the type of reasoning we have explicated here is more sophisticated involving non-monotonic inference, default reasoning with first-order terms; we chose asp because these features come out of the box. ultimately, we present this model as a standalone scientific contribution in its own right, building on the work of brown, clark, and the others cited in section 3; a contribution in the fields of cognitive science and psycholinguistics. future work will involve testing this approach on corpora of isa schemas @xcite , and considering additional types of contextual evidence and domain-general reasoning.