L i M i T : The Literal Motion in Text Dataset (2020)

Abstract: Motion recognition is one of the basic cognitive capabilities of many life forms, yet identifying motion of physical entities in natural language have not been explored extensively and empirically. We present the Literal-Motion-in-Text (LiMiT) dataset, a large human-annotated collection of English text sentences describing physical occurrence of motion, with annotated physical entities in motion. We describe the annotation process for the dataset, analyze its scale and diversity, and report results of several baseline models. We also present future research directions and applications of the LiMiT dataset and share it publicly as a new resource for the research community.

## introduction natural language understanding (nlu) tasks involving semantic and pragmatic levels in natural language processing (nlp) are challenging mainly due to the pervasive ambiguity of language and the subtly different perceptions humans have of the meaning of word, phrase, and sentence @xcite . the premise in the natural semantic metalanguage (nsm) @xcite approach to semantics is that any complex meaning can be decomposed without circularity and without residue into a combination of discrete other meanings called semantic primes. unlike other semantic approaches such as semantic role labeling (srl) @xcite and abstract meaning representation (amr) @xcite , where semantic features can have peculiarities of a particular language @xcite , the irreducible semantic core considered by nsm and composed by the semantic primes 1 , composes a universal 1 https://intranet.secure.griffith.edu.au/ schools-departments/natural-semantic-metalanguage/ what-is-nsm/semantic-primes mini-language, i.e., the primes are universal to many, if not all, languages. the hypothesis is that meaning could be reconstructed into basic elements or semantic primes, one of which is move representing the meaning of motion. at a cognitive level, almost all life forms beyond plants and simple organisms recognize motion. in natural language, motion can describe different movement types (e.g., rotational, transactional, internal), it can relate to changes in a concept or abstract thing when it is figurative motion (e.g., "her voice twisted from incredulity to astonishment"), or it can describe the movement of a physical object when it is literal motion (e.g., "the player twisted his leg before kicking the ball"), which makes motion analysis and detection challenging. because motion is a linguistic primitive @xcite ) that allows us to express complicated events more concisely, it has been considered extensively in theoretical linguistic analysis. many linguists have agreed that motion is a semantic fundamental @xcite , thus identifying motion in text and its features could improve nlu tasks. because of the nature of motion and its importance in natural language, we selected it as the first semantic prime to explore. however, and to the best of our knowledge, there is no publicly available dataset for motion detection in natural language that enables researchers to empirically investigate and understand the implications of motion in natural language. this paper describes the limit dataset, a large and annotated dataset of literal and no literal motion sentences in english. we share the dataset publicly and show results on classification models built to detect physical motion in text and models to identify the physical entities in motion. we describe applications of the limit dataset, and delineate future research directions using the dataset. ## background this section describes related work of motion in natural language, including motion elements or features, the move semantic prime, and previous attempts to create a motion corpus. ## annotation methodology in this section we describe the methodology used for the crowdsourcing annotation of physical entities in motion. we describe the annotation job that allows workers to understand and participate in the annotation tasks i.e., identifying whether sentences describe motion of physical entities and annotating entities in motion in sentences with physical occurrence of motion. we studied different annotation task designs to collect judgments from crowdsourced workers. the analysis of different annotation task designs allowed us to select the design that provided the best quality of annotation data and achieve a good agreement among annotation workers. ## the literal motion in text dataset in this section, we present the literal-motion-in-text (limit) foot_2 dataset and its characteristics. for sentences labeled as motion, i.e., describing the movement of a physical entity, ## motion in text baseline models for the train and test sets we used the data splits shown in ## conclusions and future research we presented the literal-motion-in-text (limit) dataset and described the crowdsourcing methodology employed to collect and consolidate annotations of literal motion sentences that describe physical entities's motion. we also presented preliminary results of two classifiers: one for the task of identifying literal motion sentences, and another for the task of identifying physical entities in motion in sentences. we highlighted potential applications and future research directions for using the limit dataset.