Papers by Harshit Kumar
A Practical Dialogue-Act-Driven Conversation Model for Multi-Turn Response Selection (D19-1)
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| Challenge: | Dialogue acts are important in conversation modeling, but they are rarely available for new conversations. |
| Approach: | They propose an end-to-end multi-task model that integrates dialogue acts with context and response in a crossway fashion. |
| Outcome: | The proposed model improves the accuracy of the dialogue act prediction task and the MRR for the response selection task. |
Naamapadam: A Large-Scale Named Entity Annotated Data for Indic Languages (2023.acl-long)
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Arnav Mhaske, Harshit Kedia, Sumanth Doddapaneni, Mitesh M. Khapra, Pratyush Kumar, Rudra Murthy, Anoop Kunchukuttan
| Challenge: | Named Entity Recognition (NER) is a fundamental task in natural language processing (NLP). |
| Approach: | They present the largest publicly available Named Entity Recognition dataset for the 11 major Indian languages from two language families. |
| Outcome: | The proposed dataset is the largest publicly available Named Entity Recognition (NER) dataset for the 11 major Indian languages from two language families. |
Neural Conversational QA: Learning to Reason vs Exploiting Patterns (2020.emnlp-main)
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| Challenge: | Neural Conversational QA tasks such as ShARC require systems to answer questions based on the contents of a given passage. |
| Approach: | They propose to modify a data-set with fewer spurious patterns to exploit them . they also propose to build a heuristic-based program to exploit spurious clues . |
| Outcome: | The proposed program exploits spurious patterns in the ShARC dataset, compared to neural models. |
VeeAlign: Multifaceted Context Representation Using Dual Attention for Ontology Alignment (2021.emnlp-main)
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| Challenge: | State-of-the-art Ontology Alignment systems are based on domain-dependent approaches with handcrafted rules or domain-specific architectures, making them unscalable and inefficient. |
| Approach: | They propose a Deep Learning based model that exploits syntactic and semantic information encoded in ontologies by using a dual-attention mechanism. |
| Outcome: | The proposed model exploits syntactic and semantic information encoded in ontologies and is flexible and scalable to different domains with minimal effort. |
Dialogue-act-driven Conversation Model : An Experimental Study (C18-1)
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| Challenge: | In the last decade, natural language processing and machine learning have come a long way towards building an automated dialogue system. |
| Approach: | They propose a way to encode dialogue act information and use it to build a model that can use it in a natural way. |
| Outcome: | The proposed model outperforms baseline models on a new daily dialogue dataset and achieves an MRR of about 84.8%. |