Proceedings of the 2014 Workshop on the Use of Computational Methods in the Study of Endangered Languages, pages 6–14,Baltimore, Maryland, USA, 26 June 2014. c©2014 Association for Computational Linguistics

Documenting Endangered Languages with the WordsEye Linguistics ToolMorgan Ulinski∗

mulinski@cs.columbia.eduAnusha Balakrishnan∗ab3596@columbia.edu

Daniel Bauer∗bauer@cs.columbia.edu

Bob Coyne∗coyne@cs.columbia.edu

Julia Hirschberg∗julia@cs.columbia.edu

Owen Rambow†rambow@ccls.columbia.edu

∗Department of Computer Science †CCLSColumbia UniversityNew York, NY, USA

Abstract

In this paper, we describe how field lin-guists can use the WordsEye LinguisticsTool (WELT) to study endangered lan-guages. WELT is a tool under devel-opment for eliciting endangered languagedata and formally documenting a lan-guage, based on WordsEye (Coyne andSproat, 2001), a text-to-scene generationtool that produces 3D scenes from text in-put. First, a linguist uses WELT to createelicitation materials and collect languagedata. Next, he or she uses WELT to for-mally document the language. Finally, theformal models are used to create a text-to-scene system that takes input in the en-dangered language and generates a picturerepresenting its meaning.

1 Introduction

Although languages have appeared and disap-peared throughout history, today languages arefacing extinction at an unprecedented pace. Over40% of the estimated 7,000 languages in the worldare at risk of disappearing. When languages die,we lose access to an invaluable resource for study-ing the culture, history, and experience of peoplewho spoke them (Alliance for Linguistic Diversity,2013). Efforts to document languages and developtools to support these efforts become even moreimportant with the increasing rate of extinction.Bird (2009) emphasizes a particular need to makeuse of computational linguistics during fieldwork.

To address this issue, we are developing theWordsEye Linguistics Tool, WELT. In one modeof operation, we provide field linguists with toolsfor building elicitation sessions based on custom3D scenes. In another, we provide a way to for-mally document the endangered language. For-mal hypotheses can be verified using a text-to-scene system that takes input in the endangered

language, analyzes it based on the formal model,and generates a picture representing the meaning.

WELT provides important advantages to fieldlinguists for elicitation over the current practice ofusing a set of pre-fabricated static pictures. UsingWELT the linguist can create and modify scenesin real time, based on informants’ responses, cre-ating follow-up questions and scenes to supportthem. Since the pictures WELT supports are 3Dscenes, the viewpoint can easily be changed, al-lowing exploration of linguistic descriptions basedon different frames of reference, as for elicitationsof spatial descriptions. Finally, since scenes andobjects can easily be added in the field, the lin-guist can customize the images used for elicitationto be maximally relevant to the current informants.

Creating a text-to-scene system for an endan-gered language with WELT also has advantages.First, WELT allows documentation of the seman-tics of a language in a formal way. Linguists cancustomize the focus of their studies to be as deepor shallow as they wish; however, we believe that amajor advantage of documenting a language withWELT is that it enables studies that are much moreprecise. The fact that a text-to-scene system is cre-ated from this documentation will allow linguiststo test the theories they develop with native speak-ers, making changes to grammars and semanticsin real time. The resulting text-to-scene systemcan also be an important tool for language preser-vation, spreading interest in the language amongyounger generations of the community and re-cruiting new speakers.

In this paper, we discuss the WELT toolkit andits intended use, with examples from Arrernte andNahuatl. In Section 2 we discuss prior work onfield linguistics computational tools. In Section 3we present an overview of the WELT system. Wedescribe using WELT for elicitation in Section 4and describe the tools for language documentationin Section 5. We conclude in Section 6.

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2 Related Work

Computational tools for field linguistics fall intotwo categories: tools for native speakers to usedirectly, without substantial linguist intervention,and tools for field linguists to use. Tools intendedfor native speakers include the PAWS starter kit(Black and Black, 2009), which uses the answersto a series of guided questions to produce a draftof a grammar. Similarly, Bird and Chiang (2012)describe a simplified workflow and supporting MTsoftware that lets native speakers produce useabledocumentation of their language on their own.

One of the most widely-used toolkits in the lat-ter category is SIL FieldWorks (SIL FieldWorks,2014), or specifically, FieldWorks Language Ex-plorer (FLEx). FLEx includes tools for elicit-ing and recording lexical information, dictionarydevelopment, interlinearization of texts, analysisof discourse features, and morphological analy-sis. An important part of FLEx is its “linguist-friendly” morphological parser (Black and Si-mons, 2006), which uses an underlying modelof morphology familiar to linguists, is fully in-tegrated into lexicon development and interlin-ear text analysis, and produces a human-readablegrammar sketch as well as a machine-interpretableparser. The morphological parser is constructed“stealthily” in the background, and can help a lin-guist by predicting glosses for interlinear texts.

Linguist’s Assistant (Beale, 2011) provides acorpus of semantic representations for linguists touse as a guide for elicitation. After eliciting thelanguage data, a linguist writes rules translatingthese semantic representations into surface forms.The result is a description of the language that canbe used to generate text from documents that havebeen converted into the semantic representation.Linguists are encouraged to collect their own elic-itations and naturally occurring texts and translatethem into the semantic representation.

The LinGO Grammar Matrix (Bender et al.,2002) facilitates formal modeling of syntax bygenerating basic HPSG “starter grammars” forlanguages from the answers to a typological ques-tionnaire. Extending a grammar beyond the proto-type, however, does require extensive knowledgeof HPSG, making this tool more feasibly used bygrammar engineers and computational linguists.For semantics, the most common resource for for-mal documentation across languages is FrameNet(Filmore et al., 2003); FrameNets have been de-

veloped for many languages, including Spanish,Japanese, and Portuguese. However, FrameNet isalso targeted toward computational linguists.

In general, we also lack tools for creating cus-tom elicitation materials. With WELT, we hope tofill some of the gaps in the range of available fieldlinguistics tools. WELT will enable the creation ofcustom elicitation material and facilitate the man-agement sessions with an informant. WELT willalso enable formal documentation of the semanticsof a language without knowledge of specific com-putational formalisms. This is similar to the wayFLEx allows linguists to create a formal model ofmorphology while also documenting the lexiconof a language and glossing interlinear texts.

3 Overview of WELT Workflow

In this section, we briefly describe the workflowfor using WELT; a visual representation is pro-vided in Figure 1. Since we are still in the earlystages of our project, this workflow has not beentested in practice. The tools for scene creation andelicitation are currently useable, although morefeatures will be added in the future. The tools formodeling and documentation are still in develop-ment; although some functionality has been imple-mented, we are still testing it with toy grammars.

First, WELT will be used to prepare a set of 3Dscenes to be used to elicit targeted descriptions ornarratives. An important part of this phase will bethe cultural adaptation of the graphical semanticsused in WordsEye, so that scenes will be relevantto the native speakers a linguist works with. Wewill discuss cultural adaptation in more detail inSection 4.1. Next, the linguist will work with aninformant to generate language data based on pre-pared 3D scenes. This can be a dynamic process;as new questions come up, a linguist can easilymodify existing scenes or create new ones. WELTalso automatically syncs recorded audio with openscenes and provides an interface for the linguist towrite notes, textual descriptions, and glosses. Wewill discuss creating scenes and eliciting data withWELT in Section 4.2. After the elicitation session,the linguist can use WELT to review the data col-lected, listen to the audio recorded for each scene,and revise notes and glosses. The linguist can thencreate additional scenes to elicit more data or be-gin the formal documentation of the language.

Creating a text-to-scene system with WELT re-quires formal models of the morphology, syntax,

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Define  Lexicon  

Cultural  Adapta4on  of  

VigNet  Create  Scenes   Collect  Data  

from  informant  Clean-­‐up  notes/

glosses  

Modify  &  add  vigneBes  

Define  syntax  to  seman4cs  rules  

Define  Morphology  

Define  Syntax  L2  

Lexicon  L2  Syntax-­‐

Seman4cs  rules  VigNet  Resources

Output  &  Collabora4on   Prepare  L2  scenes  Verify  with  

informant  

XLE   FieldWorks  Tools WELT  

Figure 1: WELT workflow

and semantics of a language. Since the focusof WELT is on semantics, the formalisms usedto model morphology and syntax may vary. Weare using FieldWorks to document Nahuatl mor-phology, XFST (Beesley and Karttunen, 2003) tomodel Arrernte morphology, and XLE (Crouch etal., 2011) to model syntax in the LFG formal-ism (Kaplan and Bresnan, 1982). We will providetools to export WELT descriptions and glossesinto FLEx format and to export the lexicon cre-ated during documentation into FLEx and XLE.WELT will provide user interfaces for modelingthe syntax-semantics interface, lexical semantics,and graphical semantics of a language. We willdiscuss these in more detail in Section 5.3.

Once models of morphology, syntax, and se-mantics are in place (note that these can be work-ing models, and need not be complete), WELTputs the components together into a text-to-scenesystem that takes input in the endangered languageand uses the formal models to generate pictures.This system can be used to verify theories with in-formants and revise grammars. As new questionsarise, WELT can also continue to be used to createelicitation materials and collect linguistic data.

Finally, we will create a website for WELT solinguists can share resources such as modified ver-sions of VigNet, 3D scenes, language data col-lected, and formal grammars. This will allowcomparison of analyses across languages, as wellas facilitate the documentation of other languagesthat are similar linguistically or spoken by cul-

turally similar communities. In addition, sharingthe resulting text-to-scene systems with a wideraudience can generate interest in endangered lan-guages and, if shared with endangered-language-speaking communities, encourage younger mem-bers of the community to use the language.

4 Elicitation with WELT

WELT organizes elicitation sessions around a setof 3D scenes, which are created by inputting En-glish text into WordsEye. Scenes can be importedand exported between sessions, so that usefulscenes can be reused and data compared. WELTalso provides tools for recording audio (which isautomatically synced with open scenes), textualdescriptions, glosses, and notes during a session.Screenshots are included in Figure 2.

4.1 Cultural Adaptation of VigNetTo interpret input text, WordsEye uses VigNet(Coyne et al., 2011), a lexical resource based onFrameNet (Baker et al., 1998). As in FrameNet,lexical items are grouped in frames according toshared semantic structure. A frame contains a setof frame elements (semantic roles). FrameNet de-fines the mapping between syntax and semanticsfor a lexical item with valence patterns that mapsyntactic functions to frame elements.

VigNet extends FrameNet in order to capture“graphical semantics”, a set of graphical con-straints representing the position, orientation, size,color, texture, and poses of objects in the scene,

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Figure 2: Screenshots of WELT elicitation interfaces

which is used to construct and render a 3Dscene. Graphical semantics are added to frames byadding primitive graphical (typically, spatial) rela-tions between frame element fillers. VigNet distin-guishes between meanings of words that are dis-tinguished graphically. For example, the specificobjects (e.g., implements) and spatial relations inthe graphical semantics for cook depend on theobject being cooked and on the culture in whichit is being cooked (cooking turkey in Baltimorevs. cooking an egg in Alice Springs), even thoughat an abstract level cook an egg in Alice Springsand cook a turkey in Baltimore are perfectly com-positional semantically. Frames augmented withgraphical semantics are called vignettes.

Vignette Tailoring: Without digressing into a dis-cussion on linguistic relativity, we assume thatlarge parts of VigNet are language- and culture-independent. The low-level graphical relationsused to express graphical semantics are based onphysics and human anatomy and do not depend onlanguage. However, the graphical semantics for avignette may be culture-specific, and some new vi-gnettes will need to be added for a culture. In theU.S., for example, the sentence The woman boiledthe water might invoke a scene with a pot of wa-ter on a stove in a kitchen. Among the Arrernte

people, it would instead invoke a woman sittingon the ground in front of a kettle on a campfire.Figure 3 shows an illustration from the Easternand Central Arrernte Picture Dictionary (Broad,2008) of the sentence Ipmenhe-ipmenhele kwatyeurinpe-ilemele iteme, “My grandmother is boilingthe water.” The lexical semantics for the Englishverb boil and the Arrente verb urinpe-ileme arethe same, the relation APPLY-HEAT.BOIL. How-ever, the vignettes map to different, culture-typicalgraphical semantics. The vignettes for our exam-ple are shown in Figure 4.

Figure 3: Illustration from Broad (2008).

To handle cultural differences like these, a lin-guist will use WELT to extend VigNet with new

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Figure 4: Vignettes for the woman boils the water.The high-level semantics of APPLY-HEAT.BOIL

are decomposed into sets of objects and primitivegraphical relations that depend on cultural context.

graphical semantics for existing vignettes thatneed to be modified, and new vignettes for scenar-ios not already covered. We will create interfacesso that VigNet can easily be adapted.Custom WordsEye Objects: Another way toadapt WordsEye to a culture or region is to add rel-evant 3D objects to the database. WordsEye alsosupports 2D-cutout images, which is an easy wayto add new material without 3D modeling. Wehave created a corpus of 2D and 3D models forWordsEye that are specifically relevant to aborig-inal speakers of Arrernte, including native Aus-tralian plants and animals and culturally relevantobjects and gestures. Many of the pictures we cre-ated are based on images from IAD Press, usedwith permission, which we enhanced and croppedin PhotoShop. Some scenes that use these imagesare included in Figure 5. Currently, each new ob-ject has to be manually incorporated into Words-Eye, but we will create tools to allow WELT usersto easily add pictures and objects.

New objects will also need to be incorporatedinto the semantic ontology. VigNet’s ontologyconsists of semantic concepts that are linked to-gether with ISA relations. The ontology supportsmultiple inheritance, allowing a given concept tobe a sub-type of more than one concept. For exam-ple, a PRINCESS.N is a subtype of both FEMALE.Nand ARISTOCRAT.N, and a BLACK-WIDOW.N is asubtype of SPIDER.N and POISONOUS-ENTITY.N.Concepts are often linked to corresponding lexi-cal items. If a lexical item has more than oneword sense, the different word senses would berepresented by different concepts. In addition, ev-ery graphical object in VigNet is represented by

a unique concept. For example, a particular 3Dmodel of a dog would be a linked to the generalDOG.N concept by the ISA relation. The semanticconcepts in VigNet include the graphical objectsavailable in WordsEye as well as concepts tied torelated lexical items. While WordsEye might onlyhave a handful of graphical objects for dogs, Vi-gNet will have concepts representing all commontypes of dogs, even if there is no graphical objectassociated with them. We will provide interfacesboth for adding new objects and for modifying thesemantic concepts in VigNet to reflect the differ-ing lexical semantics of a new language.

4.2 Preparing Scenes and Eliciting Data

The next step in the workflow is the preparationof scenes and elicitation of descriptions. To testcreating elicitation materials with WELT, we builta set of scenes based on the Max Planck topolog-ical relations picture series (Bowerman and Ped-erson, 1992). In creating these, we used a featureof WordsEye that allows highlighting specific ob-jects (or parts of objects) in a scene. We used thesescenes to elicit descriptions from a native Nahuatlspeaker; some examples are included in Figure 6.

(a) in tapametì tìatsakwa se kalithe fence/wall around the house

(b) in tsopelik katsekotok tìatsintìa in tìapetSthe candy sticking under the table

Figure 6: Nahuatl examples elicited with WELT

One topic we will explore with WELT is the re-lationship in Arrernte between case and semanticinterpretation of a sentence. It is possible to signif-icantly alter a sentence’s meaning by changing thecase on an argument. For example, the sentencesin (1) from Wilkins (1989) show that adding dative

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Figure 5: WordsEye scenes using custom 2D gum tree and dingo from our corpus

case to the direct object of the sentence changesthe meaning from shooting and hitting the kanga-roo to shooting at the kangaroo and not hitting it.Wilkins calls this the “dative of attempt.”

(1) a. re aherre tyerre-kehe kangaroo shot-pcHe shot the kangaroo.

b. re aherre-ke tyerre-kehe kangaroo-DAT shot-pcHe shot at the kangaroo (but missed).

In order to see how this example generalizes,we will create pairs of pictures, one in which theobject of the sentence is acted upon, and one inwhich the object fails to be acted upon. Figure 7shows a pair of scenes contrasting an Australianfootball player scoring a goal with a player aimingat the goal but missing the shot. Sentences (2) and(3) are two ways of saying “score a goal” in Ar-rernte; we want to see if a native Arrernte speakerwould use goal-ke in place of goal in this context.

(2) artwe le goal arrerne-meman ERG goal put-NPThe man kicks a goal.

(3) artwe le goal kick-eme-ile-keman ERG goal kick-VF-TV-PSTThe man kicked a goal.

5 Modeling a Language with WELT

WELT includes tools for documenting the seman-tics of the language. It also uses this documenta-tion to automatically generate a text-to-scene sys-tem for the language. Because WELT is centeredaround the idea of 3D scenes, the formal docu-mentation will tend to focus on the parts of the se-mantics that can be represented graphically. Notethat this can include figurative concepts as well,although the visual representation of these may beculture-specific. However, linguists do not need

to be limited by the graphical output; WELT canbe used to document other aspects of semantics aswell, but linguists will not be able to verify thesetheories using the text-to-scene system.

To explain the necessary documentation, webriefly describe the underlying architecture ofWordsEye, and how we are adapting it to sup-port text-to-scene systems for other languages.The WordsEye system parses each input sentenceinto a labeled syntactic dependency structure, thenconverts it into a lexical-semantic structure usinglexical valence patterns and other lexical and se-mantic information. The resulting set of seman-tic relations is converted to a “graphical seman-tics”, the knowledge needed to generate graphicalscenes from language.

To produce a text-to-scene system for a new lan-guage, WELT must replace the English linguisticprocessing modules with models for the new lan-guage. The WELT processing pipeline is illus-trated in Figure 8, with stages of the pipeline ontop and required resources below. In this section,we will discuss creating the lexicon, morphologi-cal and syntactic parsers, and syntax-to-semanticsrules. The vignettes and 3D objects will largelyhave been done during cultural adaptation of Vi-gNet; additional modifications needed to handlethe semantics can be defined using the same tools.

5.1 The Lexicon

The lexicon in WELT is a list of word formsmapped to semantic concepts. The process ofbuilding the lexicon begins during elicitation.WELT’s elicitation interface includes an option todisplay each object in the scene individually be-fore progressing to the full scene. When an objectis labeled and glossed in this way, the word andthe semantic concept represented by the 3D ob-ject are immediately added to the lexicon. Wordforms glossed in scene descriptions will also beadded to the lexicon, but will need to be mappedto semantic concepts later. WELT will provide

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Figure 7: WordsEye scenes to elicit the “dative of attempt.”

Morph  Lexical  

Seman1cs  Graphical  Seman1cs   Scene  Input  

Text  

Processing  Pipeline  

VigNet  

Vigne<es   2D/3D  objects  Lexicon  

Syntax  

Morphological  Analyzer  

Syntac1c  Parser  

Syntax-­‐Seman1cs  Rules  

Figure 8: WELT architecture

tools for completing the lexicon by modifyingthe automatically-added items, adding new lexicalitems, and mapping each lexical item to a seman-tic concept in VigNet. Figure 9(a) shows a partialmapping of the nouns in our Arrernte lexicon.

WELT includes a visual interface for search-ing VigNet’s ontology for semantic concepts andbrowsing through the hierarchy to select a partic-ular category. Figure 9(b) shows a portion of theontology that results from searching for cup. Here,we have decided to map panikane to CUP.N. Se-mantic categories are displayed one level at a time,so initially only the concepts directly above andbelow the search term are shown. From there, it issimple to click on relevant concepts and navigatethe graph to find an appropriate semantic category.To facilitate the modeling of morphology and syn-tax, WELT will also export the lexicon into for-mats compatible with FieldWorks and XLE, so thelist of word forms can be used as a starting point.

5.2 Morphology and Syntax

As mentioned earlier, the focus of our work onWELT is on modeling the interface between syn-tax, lexical semantics, and graphical semantics.Therefore, although WELT requires models ofmorphology and syntax to generate a text-to-scenesystem, we are relying on third-party tools to buildthose models. For morphology, a very good toolalready exists in FLEx, which allows the creation

Lexical VigNetItem Conceptartwe PERSON.N

panikane CUP.Nangepe CROW.N

akngwelye DOG.Ntipwele TABLE.N

(a) (b)

Figure 9: (a) Arrernte lexical items mapped to Vi-gNet concepts; (b) part of the VigNet ontology

of a morphological parser without knowledge ofany particular grammatical formalism. For syn-tax, we are using XLE for our own work whileresearching other options that would be more ac-cessible to non-computational linguists. It is im-portant to note, though, that the modeling done inWELT does not require a perfect syntactic parser.In fact, one can vastly over-generate syntax andstill accurately model semantics. Therefore, thesyntactic grammars provided as models do notneed to be complex. However, the question of syn-tax is still an open area of research in our project.

5.3 Semantics

To use the WordsEye architecture, the systemneeds to be able to map between the formal syntaxof the endangered language and a representation ofsemantics compatible with VigNet. To accomplish

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Figure 10: Creating syntax-semantics rules in WELT

this, WELT includes an interface for the linguist tospecify a set of rules that map from syntax to (lex-ical) semantics. Since we are modeling Arrerntesyntax with LFG, the rules currently take syntacticf-structures as input, but the system could easily bemodified to accommodate other formalisms. Theleft-hand side of a rule consists of a set of con-ditions on the f-structure elements and the right-hand side is the desired semantic structure. Rulesare specified by defining a tree structure for theleft-hand (syntax) side and a DAG for the right-hand (semantics) side.

As an example, we will construct a rule toprocess sentence (2) from Section 4.2, artwe legoal arrerneme. For this sentence, our Arrerntegrammar produces the f-structure in Figure 11.We create a rule that selects for predicate ar-rerne with object goal and any subject. Figure10 shows the construction of this rule in WELT.Note that var-1 on the left-hand side becomesVIGNET(var-1) on the right-hand side; this in-dicates that the lexical item found in the input ismapped into a semantic concept using the lexicon.

Figure 11: F-structure for sentence 2, Section 4.2.

The rule shown in Figure 10 is a very sim-ple example. Nodes on the left-hand side ofthe rule can also contain boolean logic, if wewanted to allow the subject to be [(artwe ‘man’ ORarhele ‘woman’) AND NOT ampe ‘child’]. Rulesneed not specify lexical items directly but mayrefer to more general semantic categories. Forexample, our rule could require a particular se-mantic category for VIGNET(var-1), such asANIMATE-BEING.N. These categories are chosenthrough the same ontology browser used to cre-ate the lexicon. Finally, to ensure that our sen-

tence can be converted into graphics, we needto make sure that a vignette definition exists forCAUSE MOTION.KICK so that the lexical seman-tics on the right-hand side of our rule can be aug-mented with graphical semantics; the vignette def-inition is given in Figure 12. The WordsEye sys-tem will use the graphical constraints in the vi-gnette to build a scene and render it in 3D.

Figure 12: Vignette definition forCAUSE MOTION.KICK

6 Summary

We have described a novel tool under develop-ment for linguists working with endangered lan-guages. It will provide a new way to elicit datafrom informants, an interface for formally docu-menting the lexical semantics of a language, andallow the creation of a text-to-scene system for anylanguage. In this paper, we have focused specifi-cally on the workflow that a linguist would fol-low while studying an endangered language withWELT. WELT will provide useful tools for fieldlinguistics and language documentation, from cre-ating elicitation materials, to eliciting data, to for-mally documenting a language. In addition, thetext-to-scene system that results from document-ing an endangered language with WELT will bevaluable for language preservation, generating in-terest in the wider world, as well as encouragingyounger members of endangered language com-munities to use the language.

Acknowledgments

This material is based upon work supported bythe National Science Foundation under Grant No.1160700.

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