Chitta Baral.

Also published as: Chitta Baral


2020

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Deeply Embedded Knowledge Representation & Reasoning For Natural Language Question Answering: A Practitioner’s Perspective
Arindam Mitra | Sanjay Narayana | Chitta Baral
Proceedings of the Fourth Workshop on Structured Prediction for NLP

Successful application of Knowledge Representation and Reasoning (KR) in Natural Language Understanding (NLU) is largely limited by the availability of a robust and general purpose natural language parser. Even though several projects have been launched in the pursuit of developing a universal meaning representation language, the existence of an accurate universal parser is far from reality. This has severely limited the application of knowledge representation and reasoning (KR) in the field of NLP and also prevented a proper evaluation of KR based NLU systems. Our goal is to build KR based systems for Natural Language Understanding without relying on a parser. Towards this we propose a method named Deeply Embedded Knowledge Representation & Reasoning (DeepEKR) where we replace the parser by a neural network, soften the symbolic representation so that a deterministic mapping exists between the parser neural network and the interpretable logical form, and finally replace the symbolic solver by an equivalent neural network, so the model can be trained end-to-end. We evaluate our method with respect to the task of Qualitative Word Problem Solving on the two available datasets (QuaRTz and QuaRel). Our system achieves same accuracy as that of the state-of-the-art accuracy on QuaRTz, outperforms the state-of-the-art on QuaRel and severely outperforms a traditional KR based system. The results show that the bias introduced by a KR solution does not prevent it from doing a better job at the end task. Moreover, our method is interpretable due to the bias introduced by the KR approach.

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Visuo-Linguistic Question Answering (VLQA) Challenge
Shailaja Keyur Sampat | Yezhou Yang | Chitta Baral
Findings of the Association for Computational Linguistics: EMNLP 2020

Understanding images and text together is an important aspect of cognition and building advanced Artificial Intelligence (AI) systems. As a community, we have achieved good benchmarks over language and vision domains separately, however joint reasoning is still a challenge for state-of-the-art computer vision and natural language processing (NLP) systems. We propose a novel task to derive joint inference about a given image-text modality and compile the Visuo-Linguistic Question Answering (VLQA) challenge corpus in a question answering setting. Each dataset item consists of an image and a reading passage, where questions are designed to combine both visual and textual information i.e., ignoring either modality would make the question unanswerable. We first explore the best existing vision-language architectures to solve VLQA subsets and show that they are unable to reason well. We then develop a modular method with slightly better baseline performance, but it is still far behind human performance. We believe that VLQA will be a good benchmark for reasoning over a visuo-linguistic context. The dataset, code and leaderboard is available at https://shailaja183.github.io/vlqa/.

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Self-Supervised Knowledge Triplet Learning for Zero-Shot Question Answering
Pratyay Banerjee | Chitta Baral
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

The aim of all Question Answering (QA) systems is to generalize to unseen questions. Current supervised methods are reliant on expensive data annotation. Moreover, such annotations can introduce unintended annotator bias, making systems focus more on the bias than the actual task. This work proposes Knowledge Triplet Learning (KTL), a self-supervised task over knowledge graphs. We propose heuristics to create synthetic graphs for commonsense and scientific knowledge. We propose using KTL to perform zero-shot question answering, and our experiments show considerable improvements over large pre-trained transformer language models.

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Video2Commonsense: Generating Commonsense Descriptions to Enrich Video Captioning
Zhiyuan Fang | Tejas Gokhale | Pratyay Banerjee | Chitta Baral | Yezhou Yang
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Captioning is a crucial and challenging task for video understanding. In videos that involve active agents such as humans, the agent’s actions can bring about myriad changes in the scene. Observable changes such as movements, manipulations, and transformations of the objects in the scene, are reflected in conventional video captioning. Unlike images, actions in videos are also inherently linked to social aspects such as intentions (why the action is taking place), effects (what changes due to the action), and attributes that describe the agent. Thus for video understanding, such as when captioning videos or when answering questions about videos, one must have an understanding of these commonsense aspects. We present the first work on generating commonsense captions directly from videos, to describe latent aspects such as intentions, effects, and attributes. We present a new dataset “Video-to-Commonsense (V2C)” that contains \sim9k videos of human agents performing various actions, annotated with 3 types of commonsense descriptions. Additionally we explore the use of open-ended video-based commonsense question answering (V2C-QA) as a way to enrich our captions. Both the generation task and the QA task can be used to enrich video captions.

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MUTANT: A Training Paradigm for Out-of-Distribution Generalization in Visual Question Answering
Tejas Gokhale | Pratyay Banerjee | Chitta Baral | Yezhou Yang
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

While progress has been made on the visual question answering leaderboards, models often utilize spurious correlations and priors in datasets under the i.i.d. setting. As such, evaluation on out-of-distribution (OOD) test samples has emerged as a proxy for generalization. In this paper, we present MUTANT, a training paradigm that exposes the model to perceptually similar, yet semantically distinct mutations of the input, to improve OOD generalization, such as the VQA-CP challenge. Under this paradigm, models utilize a consistency-constrained training objective to understand the effect of semantic changes in input (question-image pair) on the output (answer). Unlike existing methods on VQA-CP, MUTANT does not rely on the knowledge about the nature of train and test answer distributions. MUTANT establishes a new state-of-the-art accuracy on VQA-CP with a 10.57% improvement. Our work opens up avenues for the use of semantic input mutations for OOD generalization in question answering.

2019

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Combining Knowledge Hunting and Neural Language Models to Solve the Winograd Schema Challenge
Ashok Prakash | Arpit Sharma | Arindam Mitra | Chitta Baral
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Winograd Schema Challenge (WSC) is a pronoun resolution task which seems to require reasoning with commonsense knowledge. The needed knowledge is not present in the given text. Automatic extraction of the needed knowledge is a bottleneck in solving the challenge. The existing state-of-the-art approach uses the knowledge embedded in their pre-trained language model. However, the language models only embed part of the knowledge, the ones related to frequently co-existing concepts. This limits the performance of such models on the WSC problems. In this work, we build-up on the language model based methods and augment them with a commonsense knowledge hunting (using automatic extraction from text) module and an explicit reasoning module. Our end-to-end system built in such a manner improves on the accuracy of two of the available language model based approaches by 5.53% and 7.7% respectively. Overall our system achieves the state-of-the-art accuracy of 71.06% on the WSC dataset, an improvement of 7.36% over the previous best.

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Careful Selection of Knowledge to Solve Open Book Question Answering
Pratyay Banerjee | Kuntal Kumar Pal | Arindam Mitra | Chitta Baral
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Open book question answering is a type of natural language based QA (NLQA) where questions are expected to be answered with respect to a given set of open book facts, and common knowledge about a topic. Recently a challenge involving such QA, OpenBookQA, has been proposed. Unlike most other NLQA that focus on linguistic understanding, OpenBookQA requires deeper reasoning involving linguistic understanding as well as reasoning with common knowledge. In this paper we address QA with respect to the OpenBookQA dataset and combine state of the art language models with abductive information retrieval (IR), information gain based re-ranking, passage selection and weighted scoring to achieve 72.0% accuracy, an 11.6% improvement over the current state of the art.

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Identification of Adverse Drug Reaction Mentions in Tweets – SMM4H Shared Task 2019
Samarth Rawal | Siddharth Rawal | Saadat Anwar | Chitta Baral
Proceedings of the Fourth Social Media Mining for Health Applications (#SMM4H) Workshop & Shared Task

Analyzing social media posts can offer insights into a wide range of topics that are commonly discussed online, providing valuable information for studying various health-related phenomena reported online. The outcome of this work can offer insights into pharmacovigilance research to monitor the adverse effects of medications. This research specifically looks into mentions of adverse drug reactions (ADRs) in Twitter data through the Social Media Mining for Health Applications (SMM4H) Shared Task 2019. Adverse drug reactions are undesired harmful effects which can arise from medication or other methods of treatment. The goal of this research is to build accurate models using natural language processing techniques to detect reports of adverse drug reactions in Twitter data and extract these words or phrases.

2016

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Learning To Use Formulas To Solve Simple Arithmetic Problems
Arindam Mitra | Chitta Baral
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

2015

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Learning to Automatically Solve Logic Grid Puzzles
Arindam Mitra | Chitta Baral
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

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Identifying Various Kinds of Event Mentions in K-Parser Output
Arpit Sharma | Nguyen Vo | Somak Aditya | Chitta Baral
Proceedings of the The 3rd Workshop on EVENTS: Definition, Detection, Coreference, and Representation

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Recognizing Social Constructs from Textual Conversation
Somak Aditya | Chitta Baral | Nguyen Ha Vo | Joohyung Lee | Jieping Ye | Zaw Naung | Barry Lumpkin | Jenny Hastings | Richard Scherl | Dawn M. Sweet | Daniela Inclezan
Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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The NL2KR Platform for building Natural Language Translation Systems
Nguyen Vo | Arindam Mitra | Chitta Baral
Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

2011

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Using Inverse lambda and Generalization to Translate English to Formal Languages
Chitta Baral | Juraj Dzifcak | Marcos Alvarez Gonzalez | Jiayu Zhou
Proceedings of the Ninth International Conference on Computational Semantics (IWCS 2011)

2009

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Towards Effective Sentence Simplification for Automatic Processing of Biomedical Text
Siddhartha Jonnalagadda | Luis Tari | Jörg Hakenberg | Chitta Baral | Graciela Gonzalez
Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Companion Volume: Short Papers

2005

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IntEx: A Syntactic Role Driven Protein-Protein Interaction Extractor for Bio-Medical Text
Syed Toufeeq Ahmed | Deepthi Chidambaram | Hasan Davulcu | Chitta Baral
Proceedings of the ACL-ISMB Workshop on Linking Biological Literature, Ontologies and Databases: Mining Biological Semantics

2004

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Using answer set programming to answer complex queries
Chitta Baral | Michael Gelfond | Richard Scherl
Proceedings of the Workshop on Pragmatics of Question Answering at HLT-NAACL 2004