Implementing ASLN et V1.0: Progress and Plans (2021)

Abstract: We report on the development of ASLNet, a wordnet for American Sign Language (ASL). ASLNet V1.0 is currently under construction by mapping easy-to-translate ASL lexical nouns to Princeton WordNet synsets. We describe our data model and mapping approach, which can be extended to any sign language. Analysis of the 390 synsets processed to date indicates the success of our procedure yet also highlights the need to supplement our mapping with the “merge” method. We outline our plans for upcoming work to remedy this, which include use of ASL free-association data.

## background and motivation first proposed in 2019 by @xcite , aslnet is an effort to extend the wordnet model pioneered by the princeton wordnet @xcite to the visual-kinesthetic realm of sign languages. this endeavor is in part inspired by the creation of wordnets in dozens of other spoken languages, including those outside the indo-european language family @xcite , as well as images via imagenet @xcite . it is only natural to develop wordnets for sign languages like american sign language (asl) as they are unique languages in their own right. there are many benefits to creating a wordnet representation of the asl lexicon. the semantic relations encoded by a wordnet enable semanticallydriven language acquisition @xcite , resulting in a powerful first-language (l1) and second-language (l2) pedagogical resource that will also contribute to asl linguistics. furthermore, with the princeton wordnet (pwn) serving as a hub linking multiple wordnets, connecting an asl wordnet to pwn will bridge asl to other languages (and imagenet images), allowing for novel linguistic investigations. lastly, with wordnets being invaluable to natural language processing (nlp), specifically word sense disambiguation @xcite , an asl wordnet will support burgeoning asl machine translation efforts @xcite . the original aslnet proposal @xcite examined theoretical questions and strategies for extending the wordnet model to a sign language. the findings were synthesized into a proposed roadmap for creating aslnet via a hybrid "map" and "merge" approach. aslnet development would start with mapping straightforward asl lexical nouns to pwn synsets, followed by creating aslnet synsets with aslnet-specific relations to be merged with pwn where appropriate. in this paper, we report on the latest progress on implementing aslnet v1.0 according to the prescription set forth by @xcite . we describe the mapping procedure and evaluate its effectiveness. we also discuss how our work to date is informing the direction of subsequent aslnet development. ## aslnet v1.0 overview as recommended by @xcite , our objective for aslnet v1.0 is to map asl signs to their corresponding pwn synsets. for simplicity, we consider only lexical nouns, which often refer to concrete entities and hence are easier to represent; nouns also tend to map better crosslingually than verbs. furthermore, most of the words in a lexicon (e.g., dictionaries, wordnets, etc.) are generally nouns, resulting in more data to work with. therefore, aslnet v1.0 is a table of noun pwn synsets and their mapped asl sign(s). all semantic structure is directly derived from pwn, considerably simplifying the development work, albeit at the cost of (temporarily) ignoring aspects of asl not present in english and therefore not encoded by pwn, such as classifier constructions, which lack a clear parallel in the english language. while the "map" technique is not new to the wordnet community, this work presents a novel challenge in that, by working with a sign language, we are required to employ video exemplars. this stems from the lack of a conventional system for transcribing signs; there is no standardized writing system or even an international phonetic alphabet (ipa) for sign languages. consequently, the signing community has no consensus on how to distinguish phonologically similar signs from one another, which complicates the isolation of particular signed forms for encoding in a sign language wordnet. by leaning on existing pwn synsets and structure during this initial stage of development, we therefore have more bandwidth for implementing an experimental model for organizing the sign data. difficulties with encoding signs are significant contributing factors to the resource-scarce nature of sign languages like asl. they complicate the logistical challenges of gathering and processing video exemplars of signs, especially in the absence of practical computer vision, motion capture, and sign language machine translation technologies. consequently, sign language lexical databases and corpora tend to be comparatively smaller than those of languages accompanied by robust orthographies. accordingly, the challenges faced by the aslnet team are not so different from those of teams working with other under-resourced languages. in fact, many of the techniques utilized in the development of aslnet v1.0, such as the initial focus on mapping lexical nouns, are similar to those employed by the african wordnet project (awn) in creating wordnets for five resource-scarce african languages @xcite . ## sign-synset mapping to link the "form" and "meaning" levels of our data model, we developed a procedure to map signs to synsets with the objective of creating highdensity synset clusters in aslnet v1.0. while we are working with asl signs, our procedure may be extended to any sign language with available lexical databases and corresponding wordnets. ## mapping progress the lexicographers are currently performing preliminary mapping work to evaluate the strategy described in sections 2 and 3, paving the way for large-scale development. to date, we have processed 390 synsets (including both "tentative" and "final" states) in 14 randomly-selected clusters with scores in vicinity of the average from the cluster screening process ( a total of 271 signs foot_4 have been mapped to synsets. on average, each cluster contains ∼184 synsets with ∼50% of its synsets processed (e.g., reviewed by the lexicographers) and ∼13% mapped to at least one sign. these statistics, along with these reported in the remainder of this paper, consider all processed synsets (i.e., both the "tentative" and "final" states) unless otherwise noted. as indicated by of the synsets with mapped signs, 13 had 1 sign, 18 had 2 signs, and 62 had 3+ signs. the apparent propensity of these synsets to have a large number of mapped signs is likely due to the fact we ## conclusion overall, progress is being made with developing aslnet v1.0, with a focus on mapping easy-totranslate lexical nouns. our tripartite data model, cluster screening technique, synset mapper tool, and mapping protocol all have enabled successful linking of asl signs to pwn synsets, and in fact can be easily extended to other sign languages. in particular, these tools so far have been helpful in solving the unique challenges of building a sign language wordnet, overcoming the fact that there is no conventional notation system for identifying and disambiguating signs. however, preliminary work has highlighted the need for the "merge" tech-nique to incorporate aspects of asl overlooked by our current mapping efforts such as asl-only synsets. moving forward, the "map" technique used so far will be supplemented by "merge" development work that include the utilization of asl free-association data.