no code implementations • ECNLP (ACL) 2022 • Kalyani Roy, Vineeth Balapanuru, Tapas Nayak, Pawan Goyal
In this paper, we investigate the suitability of the generative approach for PQA.
no code implementations • PANDL (COLING) 2022 • Indrajit Bhattacharya, Subhasish Ghosh, Arpita Kundu, Pratik Saini, Tapas Nayak
Semi-structured metadata of a textbook — the table of contents and the index — provide rich cues for technical question generation.
no code implementations • COLING 2022 • Arpita Kundu, Subhasish Ghosh, Pratik Saini, Tapas Nayak, Indrajit Bhattacharya
Predicting difficulty of questions is crucial for technical interviews.
no code implementations • LREC 2022 • Ankan Mullick, Shubhraneel Pal, Tapas Nayak, Seung-Cheol Lee, Satadeep Bhattacharjee, Pawan Goyal
In the last few years, several attempts have been made on extracting information from material science research domain.
no code implementations • 18 Jan 2024 • Ankan Mullick, Akash Ghosh, G Sai Chaitanya, Samir Ghui, Tapas Nayak, Seung-Cheol Lee, Satadeep Bhattacharjee, Pawan Goyal
Material science literature is a rich source of factual information about various categories of entities (like materials and compositions) and various relations between these entities, such as conductivity, voltage, etc.
no code implementations • 6 Nov 2023 • Prabir Mallick, Tapas Nayak, Indrajit Bhattacharya
Pre-trained Generative models such as BART, T5, etc.
Extractive Question-Answering Long Form Question Answering +1
1 code implementation • 24 Oct 2023 • Chayan Sarkar, Avik Mitra, Pradip Pramanick, Tapas Nayak
At its core, our system employs an inventive neural network model designed to extract a series of tasks from complex task instructions expressed in natural language.
no code implementations • 1 Oct 2023 • Pratik Saini, Tapas Nayak, Indrajit Bhattacharya
Joint models, which capture interactions across triples, are the more recent development, and have been shown to outperform pipeline models for sentence-level extraction tasks.
1 code implementation • 6 Jun 2023 • Soumya Sharma, Tapas Nayak, Arusarka Bose, Ajay Kumar Meena, Koustuv Dasgupta, Niloy Ganguly, Pawan Goyal
Relation extraction models trained on a source domain cannot be applied on a different target domain due to the mismatch between relation sets.
no code implementations • 20 Feb 2023 • Pratik Saini, Samiran Pal, Tapas Nayak, Indrajit Bhattacharya
This approach leads to overall performance improvement in these models within the realistic experimental setting.
Ranked #11 on Relation Extraction on NYT
no code implementations • 15 Aug 2022 • Kalyani Roy, Tapas Nayak, Pawan Goyal
Attribute values of the products are an essential component in any e-commerce platform.
1 code implementation • 12 Apr 2022 • Tapas Nayak, Soumya Sharma, Yash Butala, Koustuv Dasgupta, Pawan Goyal, Niloy Ganguly
Causality represents the foremost relation between events in financial documents such as financial news articles, financial reports.
1 code implementation • EMNLP 2021 • Rajdeep Mukherjee, Tapas Nayak, Yash Butala, Sourangshu Bhattacharya, Pawan Goyal
Aspect Sentiment Triplet Extraction (ASTE) deals with extracting opinion triplets, consisting of an opinion target or aspect, its associated sentiment, and the corresponding opinion term/span explaining the rationale behind the sentiment.
1 code implementation • RANLP 2021 • Tapas Nayak, Navonil Majumder, Soujanya Poria
Distantly supervised models are very popular for relation extraction since we can obtain a large amount of training data using the distant supervision method without human annotation.
1 code implementation • RANLP 2021 • Tapas Nayak, Hwee Tou Ng
Distantly supervised datasets for relation extraction mostly focus on sentence-level extraction, and they cover very few relations.
no code implementations • 18 Aug 2021 • Ankan Mullick, Animesh Bera, Tapas Nayak
In this work, we present a Web-based annotation tool `Relation Triplets Extractor' \footnote{https://abera87. github. io/annotate/} (RTE) for annotating relation triplets from the text.
1 code implementation • 13 Aug 2021 • Samson Yu Bai Jian, Tapas Nayak, Navonil Majumder, Soujanya Poria
We first focus on sentiments expressed in a sentence, then identify the target aspect and opinion terms for that sentiment.
Ranked #6 on Aspect Sentiment Triplet Extraction on ASTE-Data-V2
Aspect Sentiment Triplet Extraction reinforcement-learning +2
1 code implementation • 5 Apr 2021 • Tapas Nayak
Relation extraction from text is an important task for automatic knowledge base population.
no code implementations • 31 Mar 2021 • Tapas Nayak, Navonil Majumder, Pawan Goyal, Soujanya Poria
Recently, with the advances made in continuous representation of words (word embeddings) and deep neural architectures, many research works are published in the area of relation extraction and it is very difficult to keep track of so many papers.
1 code implementation • CONLL 2019 • Tapas Nayak, Hwee Tou Ng
Relation extraction is the task of determining the relation between two entities in a sentence.
1 code implementation • 22 Nov 2019 • Tapas Nayak, Hwee Tou Ng
A relation tuple consists of two entities and the relation between them, and often such tuples are found in unstructured text.
Ranked #1 on Relation Extraction on NYT24
no code implementations • COLING 2016 • Santanu Pal, Sudip Kumar Naskar, Marcos Zampieri, Tapas Nayak, Josef van Genabith
We present a free web-based CAT tool called CATaLog Online which provides a novel and user-friendly online CAT environment for post-editors/translators.
no code implementations • LREC 2016 • Santanu Pal, Marcos Zampieri, Sudip Kumar Naskar, Tapas Nayak, Mihaela Vela, Josef van Genabith
The tool features a number of editing and log functions similar to the desktop version of CATaLog enhanced with several new features that we describe in detail in this paper.