Proceedings of NAACL HLT 2021: IndustryTrack Papers, pages 222–229June 6–11, 2021. ©2021 Association for Computational Linguistics

222

Development of an Enterprise-Grade Contract Understanding System

A. Agarwal2, L. Chiticariu3, P. Chozhiyath Raman3, M. Danilevsky1, D. Ghazi5∗, A. Gupta2,S. Guttula2, Y. Katsis1, R. Krishnamurthy3, Y. Li1, S. Mudgal3, V. Munigala2, N. Phan6*,

D. Sonawane3, S. Srinivasan3, S. Thitte3, M. Vasa3, R. Venkatachalam3, V. Yaski4*, H. Zhu1

1 IBM Research - Almaden 2 IBM Research - India 3 IBM Data and AI4 Amazon 5 Google LLC 6 Visa

{arvagarw, ankushgupta, shguttul, vmunig10}@in.ibm.com; {chiti, pchozhi, mdanile,

rajase, yunyaoli, srthitte, mitesh.vasa, venkatra, huaiyu}@us.ibm.com;

{yannis.katsis, shubham.mudgal, dhaval.sonawane1, sneha.srinivasan1130}@ibm.com;

dimanghazi@google.com; thefryingphan@gmail.com; vinitha.yaski@gmail.com

Abstract

Contracts are arguably the most important typeof business documents. Despite their sig-nificance in business, legal contract reviewlargely remains an arduous, expensive andmanual process. In this paper, we describethe Transparent and Expert Contract Under-standing System (TECUS): a commercial sys-tem designed and deployed for contract under-standing and used by a wide range of enter-prise users for the past few years. We reflecton the challenges and design decisions whenbuilding TECUS. We also summarize the datascience life cycle of TECUS and share lessonslearned.

1 Introduction

A contract is an agreement between businessesand/or individuals to create mutual obligations en-forceable by law (Cornell Law School). Writtencontracts are also used by companies to safeguardtheir resources and as such, legal advice is soughtprior to participating in a binding contract. Cur-rently, legal review remains an arduous and expen-sive process. For instance, a procurement contractrequires 5 hours of legal review on average, con-tributing to thousands of dollars in total cost (Cum-mins, 2017).

While contract reviewing is a well-establishedlegal process, building an enterprise-grade systemfor Contract Understanding (CU) to facilitate thisprocess poses three major challenges:

C1: Model CU as an NLP Problem. CU doesnot have a corresponding standard NLP definition.

∗ Work done while author was working at IBM.

Table 1: Example Contract Understanding Use-cases

Context Application

Quote to Cash Identify non-standard, risky termsAccounts Receivable Prevent leakage, improve cash-flowProcurement Analyze numerous contracts in ne-

gotiationGlobal Accounting Assist with numerous compliance

checklistsMergers & Acquisi-tions

Identify early termination notice pe-riod, penalty amount etc.

The underlying processes and the associatedrequirements for CU need to be well understood totranslate it to concrete NLP tasks.

C2: Lack of Representative Data. Contracts,while often proprietary, also vary significantlyacross domains and businesses. Thus, onecannot assume the presence of representativecontracts towards building models. Moreover,Subject Matter Experts (SMEs) qualified tolabel ground truth are expensive1. As such,NLP models may need to be developed withlimited non-representative labeled data but still beable to generalize well over previously unseen data.

C3: Need for Model Stability. CU models areintegrated into existing business processes to drivedecisions. As the models evolve over time (e.g.due to availability of new data, updates to existinglabeled data, etc.), users expect the models to be-have in a stable manner and produce no surprisingresults (Kearns and Ron, 1997).

1According to https://www.zippia.com/contract-attorney-jobs/salary/, the average annual salary for a contract attorneyis $86,000 ( $41.35/hour).

223

Nature & Party Classification

Obligation - Supplier Nature: Definition, Disclaimer, Exclusion, Obligation, Right, …Party: Buyer, End User, Supplier, …

CategoryClassification

This is WarrantiesCategory: Amendments, Asset Use, Assignments, Audits, Business Continuity, Communication, Confidentiality, Deliverables, …

Attribute Extraction

Attributes: Currency, DateTime, DefinedTerm, Duration, Location, Number, Organization, …

Location: New York

Metadata Extraction

Metadata: Contract Types, Effective Dates, Termination Dates, Contract Amounts, Contract Terms, …

Effective Date:1/1/2016

Multi-Label Classification

Element-level Extraction

Document-level Extraction

Multi-LabelClassification

Problem Example Task Concepts Sample Supported Question

“Where will disputes regarding this agreement be settled?”(Location)

“Is there an additional charge for services and utilities?”(Pricing & Taxes)

“What is the effective & termination date of this agreement?”(Effective Date, Termination Date)

“Is Tenant allowed to install additional fixtures?”(Right - Tenant)

Figure 1: TECUS’ Sub-Problems (see (IBM, b) for complete list of supported concepts)

To overcome the above challenges, we designedand developed the Transparent and Expert Con-tract Understanding System (TECUS), a com-mercial system that enables legal professionals toreview contracts with minimal effort.

TECUS first models CU as a series of text clas-sification and extraction tasks, defined collabora-tively with SMEs, to capture the information thatlegal experts seek when reviewing contracts.

Second, it leverages SystemT, a state-of-the-artdeclarative text understanding engine for the en-terprise (Chiticariu et al., 2010, 2018), towardsdeveloping transparent models on top of syntac-tic and semantic linguistic features, to mitigate apossible lack of representative labeled data and tosatisfy model stability requirements. This approachenables (1) the development of stable models thatexplicitly capture domain knowledge without re-quiring large amounts of labeled data or representa-tive samples; and (2) a data science workflow thatsupports systematic error analysis and incorpora-tion of user feedback towards continuous modelimprovement (Section 3).

TECUS is available as part of multiple commer-cial products including IBM Watson® Discovery(IBM, a) and IBM Watson® Compare and Com-ply 2. As part of these products, it has been in useby enterprise customers since 2017 to support a va-riety of contract understanding use-cases (Table 1).While several other commercial offerings, suchas Cognitiv+ (Cog), Kira (Kir), LawGeex (Law),LegalSifter (Leg), and Lexion (Lex) use NLP toanalyze contracts, their internals are not publiclydisclosed. Thus, TECUS is to the best of our knowl-

2IBM and IBM Watson are trademarks of InternationalBusiness Machines Corporation, registered in many jurisdic-tions worldwide.

edge the first commercial automated contract un-derstanding system ever presented to the scientificcommunity in such detail.

In addition to assisting in the understanding of asingle contract, as described in this paper, TECUSalso allows legal professionals to compare twocontracts, identifying similarities and differencesalong multiple dimensions; another critical task inthe contract reviewing process. TECUS modelsthis problem as a clause-level comparison problem,identifying (i) clauses that are identical betweentwo contracts, (ii) clauses that are on the same topicbut have changed, and (iii) clauses that appear inone contract but not in the other. The comparisoncomponent, similar to the contract understandingcomponent, leverages syntactic and semantic lin-guistic features provided by SystemT and an as-sociated data science workflow tuned towards asystematic and stable model development. How-ever, for space reasons, this work focuses on singlecontract analysis, enabled by TECUS’ ContractUnderstanding (CU) component.

2 Modeling CU as an NLP Problem

Working with legal experts, we first define theCU problem as a combination of Multi-classMulti-label3 Classification and Entity Extractiontasks, as depicted in Figure 1.

Clause Classification. A business contract con-sists of thousands of sentences, each defining oneor more clauses, such as Obligation, Exclusion, etc.At the core of the legal review process are identi-

3The classification problems correspond to multi-label clas-sification, as elements are often complex and cover multipleCategories/Natures/Parties.

224

LabelingModel

DevelopmentModel

Evaluation

User Feedback

Error Analysis Model Deployment

Error Analysis Tool

Contracts

SMEs Model Developers

End Users

Model Invocation

Document Visualizer

LLEs

Contract

Model Building

Runtime

Model

Feedback Incorporation

Feedback Incorporation

Ontology

Figure 2: TECUS’ Architecture

fying, classifying and reviewing individual clausesto spot potential risks. For example, the sentence

"Purchaser will purchase the Assets by a cashpayment of FOUR HUNDRED FIFTEEN THOU-SAND US DOLLARS."

is a clause related to Pricing & Taxes that describesan Obligation for the Purchaser. To help legalprofessionals focus on relevant parts of the contract,we classify contract sentences (henceforth knownas elements 4) according to three dimensions ofinterest to domain experts:

• Category: the topic associated with the ele-ment, such as Pricing & Taxes in our example.

• Nature: the action described by the element,such as Obligation in our example.

• Party: the individual or entity affected by theaction, such as Supplier in our example.

A consistent ontology (shown in Figure 1)was defined in the early stages of the project, incollaboration with SMEs via an iterative process,and reflecting the prevailing views of legal experts.However, to also accommodate users who adoptslight variations of the definitions (which wediscovered can be common due to the subtle natureof legal terms), users can also customize TECUSthrough user feedback, as described in Section 3.3.

Attribute and Metadata Extraction. In additionto classifying elements, legal teams are also inter-ested in extracting entities of particular importanceto corporate law. These fall into two categories:

• Attributes: general entities of interest, such asOrganizations and Persons involved in a contract,

4TECUS supports classification of elements beyond sen-tences, including bulleted list items and table content, whichoften appear in contracts.

Dates, Locations and Currencies. Attributes areextracted from individual elements.

• Contract Metadata: document-level legal en-tities of interest, such as the Effective Dates, Ter-mination Dates, and Contract Amounts. ContractMetadata are extracted from across a contract, andare thus applicable to the entire contract.

3 System Overview

As can be seen in Figure 2, TECUS consists ofthree main components, which we subsequentlydescribe in detail: Runtime, Model Building, andFeedback Incorporation.

3.1 RuntimeAs shown in Figure 3, users can analyze their con-tracts by interacting with TECUS’s Document Vi-sualizer, via the following two panes:

The Faceted Exploration Pane (#1) allowsusers to quickly acquire an overview of the con-tract’s contents and drill down into specific cate-gories/natures/parties of interest. In our example, auser interested in Pricing & Taxes, focuses on suchclauses by selecting the corresponding checkbox.

The Contract View Pane (#2) allows users to seethe selected elements within the contract (#3) andfor each of them inspect the Category/Nature/Partyand Attributes identified by CU (#4). It also in-cludes a Metadata View showing the metadataextracted from the contract, such as ContractAmounts, Effective Dates, Termination Dates, etc.(omitted in the interest of space).

At any point in time, users can provide feedbackon the CU results through the “Suggest changes“feature (#5). User feedback is then further analyzedand incorporated as described in Section 3.3.

225

Contract Understanding Results

User Feedback

Faceted Exploration Pane

4

5

1 Contract View Pane2

Selected Element3

Document Visualizer

Figure 3: Reviewing a Contract through TECUS’ Document Visualizer

Figure 4: Contract Understanding Components

3.2 Model Building

3.2.1 Model DevelopmentFigure 4 illustrates the sequence of model compo-nents used by TECUS to accomplish the tasks inFigure 1. TECUS uses declarative models towardsboth classification and extraction tasks 5:

5Here, we focus on classification due to its challengingaspects. Attribute and Metadata extraction are performedusing entity extraction, enabled by SystemT.

(1) Once text and document structures such aslists, sections, tables etc. are extracted from a con-tract PDF document 6, sentences/elements (for clas-sification) and tokens/phrases (for extraction) areidentified using syntactic analysis.

(2) Next, extended Semantic Role Labels (SRL)(Palmer et al., 2010), provided by SystemT (Chiti-cariu et al., 2010, 2018) 7are identified in elements.As shown in Figure 4, SRL captures who did whatto whom, when, where, and how from the exampleelement.

(3) A collection of logical formulae, called Lin-guistic Logical Expressions (LLEs), are constructedusing these SRLs to perform logical reasoning us-ing linguistic patterns in contracts, similar to rea-soning by legal experts. For instance, the twoLLEs 8 in Figure 4 identify the Category, Nature,and Party concerning the example element.

Each classification model in TECUS consists ofa collection of such LLEs. Such a model not onlyyields a transparent understanding of a contractalong the concepts outlined in Figure 1, it is alsouniquely positioned to handle the challengesoutlined in Section 1 for the following reasons:

Generalizability. LLEs are manually built bymodel developers on top of SRL9, to explicitly

6Here, we use PDF as the business document format forease of exposition. The presented techniques apply also toother document formats, such as Microsoft Word.

7SystemT also provides additional information, such astense and voice; please refer to (Zhu et al., 2019) for details.

8Simplified from the actual product LLEs for readability.9Potentially aided by machine learning (Sen et al., 2019)

226

Figure 5: Analyzing CU Errors through the ModelLens Error Analysis Tool

capture domain knowledge. Each LLE reflectspatterns from not only the documents used duringthe development process, but also unseen contractswhere similar semantic patterns appear. As aresult, the CU model generalizes much better toyet unseen contracts than state-of-the-art black-boxmodels (see Section 4 for more details).

Enabling systematic model improvement work-flow. Use of LLEs enable a fine-grained associationof CU model output with highly specific, lower-level constructs of the model. This transparencyallows a team of developers to make localized up-dates and develop models with stable and explain-able behavior, aided by a carefully created datascience workflow of model evaluation, error analy-sis and feedback incorporation, as described next.

3.2.2 Model EvaluationAs the CU model in TECUS evolves over time, itis regularly evaluated for: (1) quality, in terms ofprecision, recall and accuracy towards both Nature-Party and Category classification tasks, and (2) per-formance, in terms of throughput, memory con-sumption and behavior profile.

We measure model quality over in-domain andout-of-domain data split into the usual train (dev)and test (hold-out) subsets. Similarly, we profileruntime performance upon multiple in-domain andout-of-domain sets, allowing developers to preemp-tively rectify potentially problematic runtime be-haviors, prior to deployment.

Beyond the typical global measurements, thetransparent nature of the CU model permits evalu-

ation at finer granularity: across classes, per-classand per-LLE towards both quality and runtime per-formance. Such detailed model evaluation alongwith a systematic model improvement workflowtogether enable TECUS to provide reliable guaran-tees of consistency and robustness of its results.

3.2.3 Error AnalysisWhile evaluation provides an overview of modelperformance, model improvement requires delvingdeeper and analyzing individual errors to under-stand their root causes and inform further modeldevelopment efforts (Ribeiro et al., 2020).

TECUS supports root cause identification oferrors through the ModelLens error analysis toolshown in Figure 5 and the associated error analysisworkflow (Katsis and Wolf, 2019). Specifically,ModelLens allows model developers to performthe following error analysis tasks:(1) Acquire a high-level overview of the errorsthrough a confusion matrix that depicts the typesof misclassifications made by the model, to helpprioritize errors for further analysis.(2) Inspect erroneous instances in context. For achosen misclassification type, developers can drilldown and inspect all elements that exhibit it (shownon the right side of the screen). For each element,ModelLens also provides additional context, suchas the surrounding text, the SRL output, and theprovenance of the model output, to help modeldevelopers identify the error root cause.(3) Annotate errors with their root causes. Once adeveloper identifies the root cause of an error, theycan record it through the drop-down next to the

227

corresponding label.This error analysis process classifies errors based

on their root causes, separating true model errorsfrom other errors that have to be treated differently(e.g., labeling errors, errors from preceding mod-els, such as PDF conversion errors, and others).Additionally, ModelLens exploits the transparentnature of the model to allow developers to identifyspecific LLEs that need to be revised to addressmodel errors. Moreover, by providing contextualinformation for each error, it also assists in iden-tifying additional linguistic patterns that could betranslated into new LLEs.

3.3 Feedback Incorporation

User feedback is essential for TECUS: First, it en-ables model improvement, by communicating tothe development team cases not captured by thecurrent model. This is especially important giventhe lack of representative labeled data discussedin Section 1. Second, it allows the customiza-tion of models. Custom models allow TECUS toadapt to the needs of individual customers, whomay adopt slightly different definitions of the Cat-egory/Nature/Party classes from our SMEs, as de-scribed in Section 2. Feedback is enabled by thefollowing human-in-the-loop process:

(1) Users review model results and suggest theexclusion of incorrect labels or the inclusion ofmissing labels through the Document Visualizer.

(2) The system locates other elements that sharea similar linguistic pattern (i.e., LLE) with the oneson which feedback was provided, and asks the userwhether they would like to propagate the suggestedlabel updates to those. This capability is to reduceuser efforts in providing feedback.

(3) The system associates user feedback to thecorresponding LLEs, allowing model changes tobe localized to a small part of the model.

The localized nature of the changes enable themodel to remain stable over time; in contrast, blackbox models may regress in unexpected ways whenglobally retrained over time with additional groundtruth data. Results on model stability and the effec-tiveness of feedback incorporation are presented inthe next section.

4 Results & Discussion

We next present evaluation results, showing howTECUS addresses the challenges discussed above.

Figure 6: Model Stability with Increasing Complexity

Precision RecallJan 59.79 25.33…July 64.7 59.96Aug 71.53 67.41Sep 73.5 68.09

Jan … July Aug Sep

PrecisionRecall

Human Performance

Improvements observed BAby company X leveragingglobal model improvements

Improvements specific to company X based ontheir specific feedback

Figure 7: Effectiveness of Feedback Incorporation

Model generalizability. To verify our intuitionthat the transparent nature of the CU model helpsit generalize to unseen contracts, we compare itto state of the art black box ML models. In ourexperiments, when trained on procurement con-tracts (PCs) sourced from within IBM (in-domain)and tested on PCs sourced randomly from the web(out-of-domain), the CU model significantly out-performs the alternatives, with 1.3x, 2.09x, and2.58x higher micro-F1 score over a BidirectionalLong Short-Term Memory (BiLSTM), Convolu-tional Neural Network (CNN), and Logistic Re-gression (LR) model, respectively.

For reference, prior state-of-the-art results ofnature identification in contracts (Chalkidis et al.,2018) and financial legislation (Neill et al., 2017)were based on different BiLSTM-based architec-tures, which we have found in our experiments toperform well on contracts similar to the ones onwhich they were trained (e.g., contracts that followsimilar templates) but generalize poorly to otherunseen contracts.

Model stability. To verify the stability of modelperformance over time, we capture in Figure 6the CU model’s precision and recall on categoryclassification across six consecutive developmentsprints (each two weeks long). During this timeperiod, the development team added support foradditional categories, increasing the supportedcategories from 10 to 23. Despite a quick addition

228

of new categories (with 2.25 new categoriesadded per sprint on average), the model’s qualityremained stable across all categories, old andnew. This can be attributed (a) to the transparentnature of the model, which allows changes to belocalized and (b) to the data science process whichallows quick additions of new categories withoutcompromising on quality.

Effectiveness of feedback incorporation. Toverify the effectiveness of TECUS’s feedback incor-poration mechanism, we capture in Figure 7 the CUmodel’s precision and recall for category classifica-tion on data of interest for an individual customerX over 9 months. During this period the modeldevelopment team incorporated two types of feed-back: in the first few months (January-July), theyleveraged feedback solely from other customers,while in the last few months (July-Sep), they in-corporated focused feedback from customer X. Asshown in the chart, customer X benefited both fromthe feedback given by other customers, as well asits own feedback, which further improved quality.Moreover, the model quality increased consistentlytowards human performance (calculated internallybased on evaluation of inter-annotator agreementamong SMEs).

5 Conclusion

We have presented TECUS, a commercial systemthat effectively assists and supplements legal ex-perts in understanding and reviewing contracts.TECUS’ effectiveness is based on (a) the transpar-ent nature of the CU model, comprised of Linguis-tic Logical Expressions on top of SRL, which inturn enables (b) a systematic data science workflowtowards swift yet stable model development. Thisleads to models that can be developed with limited,non-representative labeled data and remain stableand predictable over time; traits that are essentialnot just in the contract understanding domain butthe wider legal domain as well. Finally, while thesystem was developed for the CU problem, we be-lieve that its design and associated insights couldinform efforts in other areas that pose similar re-quirements of generalizability and stability.

References

Cognitiv+ (Accessed: 2021-04-09). http://www.cognitivplus.com.

a. IBM Watson Discovery (Accessed: 2021-04-09). https://www.ibm.com/cloud/watson-discovery.

b. IBM Watson Discovery: Understanding ContractAnalysis (Accessed: 2021-04-09). https://cloud.ibm.com/docs/discovery-data?topic=discovery-data-contract_parsing.

Kira (Accessed: 2021-04-09). https://kirasystems.com.

LawGeex (Accessed: 2021-04-09). https://www.lawgeex.com.

LegalSifter (Accessed: 2021-04-09). https://www.legalsifter.com.

Lexion (Accessed: 2021-04-09). https://lexion.ai.

Ilias Chalkidis, Ion Androutsopoulos, and AchilleasMichos. 2018. Obligation and Prohibition Extrac-tion Using Hierarchical RNNs. In Proceedings ofthe 56th Annual Meeting of the Association for Com-putational Linguistics (Volume 2: Short Papers),pages 254–259, Melbourne, Australia. Associationfor Computational Linguistics.

Laura Chiticariu, Marina Danilevsky, Yunyao Li, Fred-erick Reiss, and Huaiyu Zhu. 2018. SystemT:Declarative Text Understanding for Enterprise. InProceedings of the 2018 Conference of the NorthAmerican Chapter of the Association for Computa-tional Linguistics: Human Language Technologies,Volume 3 (Industry Papers), pages 76–83. Associa-tion for Computational Linguistics.

Laura Chiticariu, Rajasekar Krishnamurthy, Yunyao Li,Sriram Raghavan, Frederick R. Reiss, and Shivaku-mar Vaithyanathan. 2010. SystemT: An AlgebraicApproach to Declarative Information Extraction. InProceedings of the 48th Annual Meeting of the As-sociation for Computational Linguistics. ACL ’10,pages 128–137. Association for Computational Lin-guistics.

Cornell Law School (Accessed: 2021-04-09). Con-tract. https://www.law.cornell.edu/wex/contract.

Tim Cummins. 2017. Cost of processing a ba-sic contract soars to $6900 (accessed: 2021-04-09). https://blog.lawgeex.com/contractcosts/.

Yannis Katsis and Christine T. Wolf. 2019. ModelLens:an interactive system to support the model improve-ment practices of data science teams. In ConferenceCompanion Publication of the 2019 on ComputerSupported Cooperative Work and Social Computing,CSCW ’19, page 9–13, New York, NY, USA. Asso-ciation for Computing Machinery.

229

Michael Kearns and Dana Ron. 1997. Algorithmicstability and sanity-check bounds for leave-one-outcross-validation. In Proceedings of the Tenth An-nual Conference on Computational Learning The-ory, COLT ’97, page 152–162, New York, NY, USA.Association for Computing Machinery.

James O’ Neill, Paul Buitelaar, Cecile Robin, andLeona O’ Brien. 2017. Classifying SententialModality in Legal Language: A Use Case in Finan-cial Regulations, Acts and Directives. In Proceed-ings of the 16th Edition of the International Confer-ence on Articial Intelligence and Law, ICAIL ’17,page 159–168, New York, NY, USA. Association forComputing Machinery.

Martha Palmer, Daniel Gildea, and Nianwen Xue. 2010.Semantic Role Labeling. Synthesis Lectures onHuman Language Technology Series. Morgan andClaypool.

Marco Tulio Ribeiro, Tongshuang Wu, Carlos Guestrin,and Sameer Singh. 2020. Beyond accuracy: Be-havioral testing of NLP models with CheckList. InProceedings of the 58th Annual Meeting of the Asso-ciation for Computational Linguistics, pages 4902–4912, Online. Association for Computational Lin-guistics.

Prithviraj Sen, Yunyao Li, Eser Kandogan, Yiwei Yang,and Walter Lasecki. 2019. HEIDL: Learning linguis-tic expressions with deep learning and human-in-the-loop. In Proceedings of the 57th Annual Meeting ofthe Association for Computational Linguistics: Sys-tem Demonstrations, pages 135–140, Florence, Italy.Association for Computational Linguistics.

Huaiyu Zhu, Yunyao Li, and Laura Chiticariu. 2019.Towards universal semantic representation. In Pro-ceedings of the First International Workshop on De-signing Meaning Representations, pages 177–181,Florence, Italy. Association for Computational Lin-guistics.