I work in a number of different research directions: Affective Computing, Machine Comprehension, Commonsense Knowledge in NLP, Multimodal NLP, Natural Language Understanding, Legal NLP, NLP for Social Good, Deep Reinforcement Learning, Computational Morality, and Financial Text Understanding. Please check out the relevant papers on these topics.

Currently, these are the main areas, I am currently focussing on:


The publication list below may not be updated and may not be exhaustive, please refer to Google Scholar page for the latest and exhausitive list of publications.

Publications

Patents



Some of the datasets are linked below. For code, model implementations and datasets described in other papers please refer to the following GitHub Page: Exploration Lab

MCScript

Description: MCScript is a new dataset for the task of machine comprehension focussing on commonsense knowledge. Questions were collected based on script scenarios, rather than individual texts, which resulted in question–answer pairs that explicitly involve commonsense knowledge. It comprises 13,939 questions on 2,119 narrative texts and covers 110 different everyday scenarios. Each text is annotated with one of 110 scenarios. Questions are typed with a crowdsourced annotation, indicating whether they can be answered from the text or if commonsense knowledge is needed for finding an answer.
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Paper: MCScript: A Novel Dataset for Assessing Machine Comprehension Using Script Knowledge (Link)

Modeling Semantic Expectations

Description: This resource contains the DR predictions (by humans) on the InScript corpus. These were collected using Amazon Mechanical Turk. For details please refer to the paper mentioned below.
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Paper: Modeling Semantic Expectations: Using Script Knowledge for Referent Prediction (Link)

InScript (Narrative Texts annotated with Script Information)

Description: The InScript corpus contains a total of 1000 narrative texts crowdsourced via Amazon Mechanical Turk. The texts cover 10 different scenarios describing everyday situations like taking a bath, baking a cake etc. It is annotated with script information in the form of scenario-specific events and participants labels. The texts are also annotated with coreference chains linking different mentions of the same entity within the document.
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Paper: InScript: Narrative texts annotated with script information (Link)

Script Data

Description: The provided includes both development (dev) data as well as test data. Each of these two directories contains different script scenarios located in respective sub-directories, e.g. the script scenario for preparing coffee can be found in the sub-directory test/coffee. Please consult the readme (link below) for further information.
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Readme
Paper: Inducing Neural Models of Script Knowledge (Link)