Cezar Pendus


2022

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SYGMA: A System for Generalizable and Modular Question Answering Over Knowledge Bases
Sumit Neelam | Udit Sharma | Hima Karanam | Shajith Ikbal | Pavan Kapanipathi | Ibrahim Abdelaziz | Nandana Mihindukulasooriya | Young-Suk Lee | Santosh Srivastava | Cezar Pendus | Saswati Dana | Dinesh Garg | Achille Fokoue | G P Shrivatsa Bhargav | Dinesh Khandelwal | Srinivas Ravishankar | Sairam Gurajada | Maria Chang | Rosario Uceda-Sosa | Salim Roukos | Alexander Gray | Guilherme Lima | Ryan Riegel | Francois Luus | L V Subramaniam
Findings of the Association for Computational Linguistics: EMNLP 2022

Knowledge Base Question Answering (KBQA) involving complex reasoning is emerging as an important research direction. However, most KBQA systems struggle with generalizability, particularly on two dimensions: (a) across multiple knowledge bases, where existing KBQA approaches are typically tuned to a single knowledge base, and (b) across multiple reasoning types, where majority of datasets and systems have primarily focused on multi-hop reasoning. In this paper, we present SYGMA, a modular KBQA approach developed with goal of generalization across multiple knowledge bases and multiple reasoning types. To facilitate this, SYGMA is designed as two high level modules: 1) KB-agnostic question understanding module that remain common across KBs, and generates logic representation of the question with high level reasoning constructs that are extensible, and 2) KB-specific question mapping and answering module to address the KB-specific aspects of the answer extraction. We evaluated SYGMA on multiple datasets belonging to distinct knowledge bases (DBpedia and Wikidata) and distinct reasoning types (multi-hop and temporal). State-of-the-art or competitive performances achieved on those datasets demonstrate its generalization capability.

2020

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The TechQA Dataset
Vittorio Castelli | Rishav Chakravarti | Saswati Dana | Anthony Ferritto | Radu Florian | Martin Franz | Dinesh Garg | Dinesh Khandelwal | Scott McCarley | Michael McCawley | Mohamed Nasr | Lin Pan | Cezar Pendus | John Pitrelli | Saurabh Pujar | Salim Roukos | Andrzej Sakrajda | Avi Sil | Rosario Uceda-Sosa | Todd Ward | Rong Zhang
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

We introduce TECHQA, a domain-adaptation question answering dataset for the technical support domain. The TECHQA corpus highlights two real-world issues from the automated customer support domain. First, it contains actual questions posed by users on a technical forum, rather than questions generated specifically for a competition or a task. Second, it has a real-world size – 600 training, 310 dev, and 490 evaluation question/answer pairs – thus reflecting the cost of creating large labeled datasets with actual data. Hence, TECHQA is meant to stimulate research in domain adaptation rather than as a resource to build QA systems from scratch. TECHQA was obtained by crawling the IBMDeveloper and DeveloperWorks forums for questions with accepted answers provided in an IBM Technote—a technical document that addresses a specific technical issue. We also release a collection of the 801,998 Technotes available on the web as of April 4, 2019 as a companion resource that can be used to learn representations of the IT domain language.

2019

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CFO: A Framework for Building Production NLP Systems
Rishav Chakravarti | Cezar Pendus | Andrzej Sakrajda | Anthony Ferritto | Lin Pan | Michael Glass | Vittorio Castelli | J. William Murdock | Radu Florian | Salim Roukos | Avi Sil
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP): System Demonstrations

This paper introduces a novel orchestration framework, called CFO (Computation Flow Orchestrator), for building, experimenting with, and deploying interactive NLP (Natural Language Processing) and IR (Information Retrieval) systems to production environments. We then demonstrate a question answering system built using this framework which incorporates state-of-the-art BERT based MRC (Machine Reading Com- prehension) with IR components to enable end-to-end answer retrieval. Results from the demo system are shown to be high quality in both academic and industry domain specific settings. Finally, we discuss best practices when (pre-)training BERT based MRC models for production systems. Screencast links: - Short video (< 3 min): http: //ibm.biz/gaama_demo - Supplementary long video (< 13 min): http://ibm.biz/gaama_cfo_demo

2013

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Generalized Reordering Rules for Improved SMT
Fei Huang | Cezar Pendus
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)