Augmenting Abstract Meaning Representation for Human-Robot Dialogue

Abstract

We detail refinements made to Abstract Meaning Representation (AMR) that make the representation more suitable for supporting a situated dialogue system, where a human remotely controls a robot for purposes of search and rescue and reconnaissance. We propose 36 augmented AMRs that capture speech acts, tense and aspect, and spatial information. This linguistic information is vital for representing important distinctions, for example whether the robot has moved, is moving, or will move. We evaluate two existing AMR parsers for their performance on dialogue data. We also outline a model for graph-to-graph conversion, in which output from AMR parsers is converted into our refined AMRs. The design scheme presented here, though task-specific, is extendable for broad coverage of speech acts using AMR in future task-independent work.

A Two-Level Interpretation of Modality in Human-Robot Dialogue

“…Furthermore, a robot needs to understand specific instructions such as how far to go and when, evaluate whether or not these instructions are feasible, and communicate and discuss the status of a given task in relation to a larger goal. To this end, we incorporate the speech act inventory of Bonial et al (2020) and Dial-AMR, a collection of 1122 utterances from the SCOUT corpus annotated with speech acts tailored to the robot in the search and navigation domain. 3 In delineating and defining their speech acts, the authors focus on the effects of an utterance relating to belief and obligation within human-robot dialogue (Traum, 1999;Poesio and Traum, 1998).…”

“…In addition to modal expressions, we annotate negation and quantification for the purpose of detecting scope relations and meaning in dialogue more broadly in future work. Our approach acknowledges both the semantic richness of how modals are assigned interpretations in context (Rubinstein et al, 2013), as well as the situational grounding of the role an expression is playing in the task-oriented dialogue (Sarathy et al, 2019;Bonial et al, 2020). For this reason, we have developed a two-level annotation scheme that separates out the basic modal value of an expression from its eventual interpretation within a context.…”

“…This information allows us to evaluate the ease with which future work integrating modality can be conducted (and whether or not is is a worthwhile endeavor to begin with). It also builds on the work of Bonial et al (2020), whose scheme disfavors multiple possible interpretations that may nevertheless add important information to the dialogue.…”

“…We hypothesize that certain readings and scope preferences for modal operators are more salient in human-robot dialogue because of the unique makeup of the common ground (Poesio, 1993). We provide a mapping from formal semantic theories of modality related to participant beliefs and updates of the common ground (Portner, 2009), to a practical model of speech acts that translates into robot action for search and navigation task-oriented dialogue and an automated NLU and NLG system (Bonial et al, 2020). This mapping is formalized in an annotation scheme in which the use of modal expressions is mapped to their effect in dialogue, providing a model for the robot to learn the meaning of modal expressions (Chai et al, 2018).…”

XL-AMR: Enabling Cross-Lingual AMR Parsing with Transfer Learning Techniques

“…AMR unifies, in a single structure, a rich set of information coming from different tasks, such as Named Entity Recognition (NER), Semantic Role Labeling (SRL), Word Sense Disambiguation (WSD) and coreference resolution. Such representations are actively integrated in several Natural Language Processing (NLP) applications, inter alia, information extraction (Rao et al, 2017), text summarization (Hardy and Vlachos, 2018;Liao et al, 2018), paraphrase detection (Issa et al, 2018), spoken language understanding (Damonte et al, 2019), machine translation (Song et al, 2019b) and human-robot interaction (Bonial et al, 2020). It is therefore desirable to extend AMR semantic representations across languages along the lines of cross-lingual representations for grammatical annotation (de Marneffe et al, 2014), concepts (Conia and Navigli, 2020) and semantic roles (Akbik et al, 2015;Di Fabio et al, 2019).…”