Pavan Kapanipathi


2022

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Zero-shot Entity Linking with Less Data
G P Shrivatsa Bhargav | Dinesh Khandelwal | Saswati Dana | Dinesh Garg | Pavan Kapanipathi | Salim Roukos | Alexander Gray | L Venkata Subramaniam
Findings of the Association for Computational Linguistics: NAACL 2022

Entity Linking (EL) maps an entity mention in a natural language sentence to an entity in a knowledge base (KB). The Zero-shot Entity Linking (ZEL) extends the scope of EL to unseen entities at the test time without requiring new labeled data. BLINK (BERT-based) is one of the SOTA models for ZEL. Interestingly, we discovered that BLINK exhibits diminishing returns, i.e., it reaches 98% of its performance with just 1% of the training data and the remaining 99% of the data yields only a marginal increase of 2% in the performance. While this extra 2% gain makes a huge difference for downstream tasks, training BLINK on large amounts of data is very resource-intensive and impractical. In this paper, we propose a neuro-symbolic, multi-task learning approach to bridge this gap. Our approach boosts the BLINK’s performance with much less data by exploiting an auxiliary information about entity types. Specifically, we train our model on two tasks simultaneously - entity linking (primary task) and hierarchical entity type prediction (auxiliary task). The auxiliary task exploits the hierarchical structure of entity types. Our approach achieves superior performance on ZEL task with significantly less training data. On four different benchmark datasets, we show that our approach achieves significantly higher performance than SOTA models when they are trained with just 0.01%, 0.1%, or 1% of the original training data. Our code is available at https://github.com/IBM/NeSLET.

2021

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Leveraging Abstract Meaning Representation for Knowledge Base Question Answering
Pavan Kapanipathi | Ibrahim Abdelaziz | Srinivas Ravishankar | Salim Roukos | Alexander Gray | Ramón Fernandez Astudillo | Maria Chang | Cristina Cornelio | Saswati Dana | Achille Fokoue | Dinesh Garg | Alfio Gliozzo | Sairam Gurajada | Hima Karanam | Naweed Khan | Dinesh Khandelwal | Young-Suk Lee | Yunyao Li | Francois Luus | Ndivhuwo Makondo | Nandana Mihindukulasooriya | Tahira Naseem | Sumit Neelam | Lucian Popa | Revanth Gangi Reddy | Ryan Riegel | Gaetano Rossiello | Udit Sharma | G P Shrivatsa Bhargav | Mo Yu
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

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A Semantics-aware Transformer Model of Relation Linking for Knowledge Base Question Answering
Tahira Naseem | Srinivas Ravishankar | Nandana Mihindukulasooriya | Ibrahim Abdelaziz | Young-Suk Lee | Pavan Kapanipathi | Salim Roukos | Alfio Gliozzo | Alexander Gray
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)

Relation linking is a crucial component of Knowledge Base Question Answering systems. Existing systems use a wide variety of heuristics, or ensembles of multiple systems, heavily relying on the surface question text. However, the explicit semantic parse of the question is a rich source of relation information that is not taken advantage of. We propose a simple transformer-based neural model for relation linking that leverages the AMR semantic parse of a sentence. Our system significantly outperforms the state-of-the-art on 4 popular benchmark datasets. These are based on either DBpedia or Wikidata, demonstrating that our approach is effective across KGs.

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Efficient Text-based Reinforcement Learning by Jointly Leveraging State and Commonsense Graph Representations
Keerthiram Murugesan | Mattia Atzeni | Pavan Kapanipathi | Kartik Talamadupula | Mrinmaya Sachan | Murray Campbell
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)

Text-based games (TBGs) have emerged as useful benchmarks for evaluating progress at the intersection of grounded language understanding and reinforcement learning (RL). Recent work has proposed the use of external knowledge to improve the efficiency of RL agents for TBGs. In this paper, we posit that to act efficiently in TBGs, an agent must be able to track the state of the game while retrieving and using relevant commonsense knowledge. Thus, we propose an agent for TBGs that induces a graph representation of the game state and jointly grounds it with a graph of commonsense knowledge from ConceptNet. This combination is achieved through bidirectional knowledge graph attention between the two symbolic representations. We show that agents that incorporate commonsense into the game state graph outperform baseline agents.

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Looking Beyond Sentence-Level Natural Language Inference for Question Answering and Text Summarization
Anshuman Mishra | Dhruvesh Patel | Aparna Vijayakumar | Xiang Lorraine Li | Pavan Kapanipathi | Kartik Talamadupula
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Natural Language Inference (NLI) has garnered significant attention in recent years; however, the promise of applying NLI breakthroughs to other downstream NLP tasks has remained unfulfilled. In this work, we use the multiple-choice reading comprehension (MCRC) and checking factual correctness of textual summarization (CFCS) tasks to investigate potential reasons for this. Our findings show that: (1) the relatively shorter length of premises in traditional NLI datasets is the primary challenge prohibiting usage in downstream applications (which do better with longer contexts); (2) this challenge can be addressed by automatically converting resource-rich reading comprehension datasets into longer-premise NLI datasets; and (3) models trained on the converted, longer-premise datasets outperform those trained using short-premise traditional NLI datasets on downstream tasks primarily due to the difference in premise lengths.

2020

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Reading Comprehension as Natural Language Inference:A Semantic Analysis
Anshuman Mishra | Dhruvesh Patel | Aparna Vijayakumar | Xiang Li | Pavan Kapanipathi | Kartik Talamadupula
Proceedings of the Ninth Joint Conference on Lexical and Computational Semantics

In the recent past, Natural language Inference (NLI) has gained significant attention, particularly given its promise for downstream NLP tasks. However, its true impact is limited and has not been well studied. Therefore, in this paper, we explore the utility of NLI for one of the most prominent downstream tasks, viz. Question Answering (QA). We transform one of the largest available MRC dataset (RACE) to an NLI form, and compare the performances of a state-of-the-art model (RoBERTa) on both these forms. We propose new characterizations of questions, and evaluate the performance of QA and NLI models on these categories. We highlight clear categories for which the model is able to perform better when the data is presented in a coherent entailment form, and a structured question-answer concatenation form, respectively.

2018

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A Systematic Classification of Knowledge, Reasoning, and Context within the ARC Dataset
Michael Boratko | Harshit Padigela | Divyendra Mikkilineni | Pritish Yuvraj | Rajarshi Das | Andrew McCallum | Maria Chang | Achille Fokoue-Nkoutche | Pavan Kapanipathi | Nicholas Mattei | Ryan Musa | Kartik Talamadupula | Michael Witbrock
Proceedings of the Workshop on Machine Reading for Question Answering

The recent work of Clark et al. (2018) introduces the AI2 Reasoning Challenge (ARC) and the associated ARC dataset that partitions open domain, complex science questions into easy and challenge sets. That paper includes an analysis of 100 questions with respect to the types of knowledge and reasoning required to answer them; however, it does not include clear definitions of these types, nor does it offer information about the quality of the labels. We propose a comprehensive set of definitions of knowledge and reasoning types necessary for answering the questions in the ARC dataset. Using ten annotators and a sophisticated annotation interface, we analyze the distribution of labels across the challenge set and statistics related to them. Additionally, we demonstrate that although naive information retrieval methods return sentences that are irrelevant to answering the query, sufficient supporting text is often present in the (ARC) corpus. Evaluating with human-selected relevant sentences improves the performance of a neural machine comprehension model by 42 points.

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An Interface for Annotating Science Questions
Michael Boratko | Harshit Padigela | Divyendra Mikkilineni | Pritish Yuvraj | Rajarshi Das | Andrew McCallum | Maria Chang | Achille Fokoue | Pavan Kapanipathi | Nicholas Mattei | Ryan Musa | Kartik Talamadupula | Michael Witbrock
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing: System Demonstrations

Recent work introduces the AI2 Reasoning Challenge (ARC) and the associated ARC dataset that partitions open domain, complex science questions into an Easy Set and a Challenge Set. That work includes an analysis of 100 questions with respect to the types of knowledge and reasoning required to answer them. However, it does not include clear definitions of these types, nor does it offer information about the quality of the labels or the annotation process used. In this paper, we introduce a novel interface for human annotation of science question-answer pairs with their respective knowledge and reasoning types, in order that the classification of new questions may be improved. We build on the classification schema proposed by prior work on the ARC dataset, and evaluate the effectiveness of our interface with a preliminary study involving 10 participants.