Abstract Meaning Representation (AMR) is a general-purpose meaning representation that has become popular for its clear structure, ease of annotation and available corpora, and overall expressiveness. While AMR was designed to represent sentence meaning in English text, recent research has explored its adaptation to broader domains, including documents, dialogues, spatial information, cross-lingual tasks, and gesture. In this paper, we present an annotated corpus of multimodal (speech and gesture) AMR in a task-based setting. Our corpus is multilayered, containing temporal alignments to both the speech signal and to descriptions of gesture morphology. We also capture coreference relationships across modalities, enabling fine-grained analysis of how the semantics of gesture and natural language interact. We discuss challenges that arise when identifying cross-modal coreference and anaphora, as well as in creating and evaluating multimodal corpora in general. Although we find AMR’s abstraction away from surface form (in both language and gesture) occasionally too coarse-grained to capture certain cross-modal interactions, we believe its flexibility allows for future work to fill in these gaps. Our corpus and annotation guidelines are available at https://github.com/klai12/encoding-gesture-multimodal-dialogue.

Actions are critical for interpreting dialogue: they provide context for demonstratives and definite descriptions in discourse, and they continually update the common ground. This paper describes how Abstract Meaning Representation (AMR) can be used to annotate actions in multimodal human-human and human-object interactions. We conduct initial annotations of shared task and first-person point-of-view videos. We show that AMRs can be interpreted by a proxy language, such as VoxML, as executable annotation structures in order to recreate and simulate a series of annotated events.

In this task, we identify a challenge that is reflective of linguistic and cognitive competencies that humans have when speaking and reasoning. Particularly, given the intuition that textual and visual information mutually inform each other for semantic reasoning, we formulate a Competence-based Question Answering challenge, designed to involve rich semantic annotation and aligned text-video objects. The task is to answer questions from a collection of cooking recipes and videos, where each question belongs to a “question family” reflecting a specific reasoning competence. The data and task result is publicly available.

This paper presents Gesture AMR, an extension to Abstract Meaning Representation (AMR), that captures the meaning of gesture. In developing Gesture AMR, we consider how gesture form and meaning relate; how gesture packages meaning both independently and in interaction with speech; and how the meaning of gesture is temporally and contextually determined. Our case study for developing Gesture AMR is a focused human-human shared task to build block structures. We develop an initial taxonomy of gesture act relations that adheres to AMR’s existing focus on predicate-argument structure while integrating meaningful elements unique to gesture. Pilot annotation shows Gesture AMR to be more challenging than standard AMR, and illustrates the need for more work on representation of dialogue and multimodal meaning. We discuss challenges of adapting an existing meaning representation to non-speech-based modalities and outline several avenues for expanding Gesture AMR.