1 code implementation • 29 Oct 2023 • Ashutosh Dwivedi, Pradhyumna Lavania, Ashutosh Modi
Etiquettes are an essential ingredient of day-to-day interactions among people.
1 code implementation • 11 Jul 2023 • Abhinav Joshi, Akshat Sharma, Sai Kiran Tanikella, Ashutosh Modi
To further promote research in PCR, in this paper, we propose a new large benchmark (in English) for the PCR task: IL-PCR (Indian Legal Prior Case Retrieval) corpus.
1 code implementation • 11 Jul 2023 • Abhinav Joshi, Susmit Agrawal, Ashutosh Modi
To the best of our knowledge, it is the largest translation dataset for continuous Indian Sign Language.
1 code implementation • 8 Jul 2023 • Abhinav Joshi, Areeb Ahmad, Umang Pandey, Ashutosh Modi
Text-based games provide a framework for developing natural language understanding and commonsense knowledge about the world in reinforcement learning based agents.
no code implementations • 19 Apr 2023 • Ashutosh Modi, Prathamesh Kalamkar, Saurabh Karn, Aman Tiwari, Abhinav Joshi, Sai Kiran Tanikella, Shouvik Kumar Guha, Sachin Malhan, Vivek Raghavan
LegalEval task has three sub-tasks: Task-A (Rhetorical Roles Labeling) is about automatically structuring legal documents into semantically coherent units, Task-B (Legal Named Entity Recognition) deals with identifying relevant entities in a legal document and Task-C (Court Judgement Prediction with Explanation) explores the possibility of automatically predicting the outcome of a legal case along with providing an explanation for the prediction.
no code implementations • 7 Nov 2022 • Abhinav Joshi, Naman Gupta, Jinang Shah, Binod Bhattarai, Ashutosh Modi, Danail Stoyanov
In order to process the multimodal information automatically and use it for an end application, Multimodal Representation Learning (MRL) has emerged as an active area of research in recent times.
1 code implementation • 7 Nov 2022 • Ashwani Bhat, Ashutosh Modi
Recently there has been active research in extracting the cause of an emotion expressed in text.
Ranked #6 on Causal Emotion Entailment on RECCON
2 code implementations • NAACL 2022 • Abhinav Joshi, Ashwani Bhat, Ayush Jain, Atin Vikram Singh, Ashutosh Modi
Emotions are an inherent part of human interactions, and consequently, it is imperative to develop AI systems that understand and recognize human emotions.
Ranked #2 on Multimodal Emotion Recognition on IEMOCAP
1 code implementation • Findings (ACL) 2022 • Arnav Kapoor, Mudit Dhawan, Anmol Goel, T. H. Arjun, Akshala Bhatnagar, Vibhu Agrawal, Amul Agrawal, Arnab Bhattacharya, Ponnurangam Kumaraguru, Ashutosh Modi
Further, as a use-case for the corpus, we introduce the task of bail prediction.
no code implementations • LREC 2022 • Prathamesh Kalamkar, Aman Tiwari, Astha Agarwal, Saurabh Karn, Smita Gupta, Vivek Raghavan, Ashutosh Modi
In this paper, we introduce a new corpus for structuring legal documents.
1 code implementation • MMMPIE (COLING) 2022 • Harsh Agarwal, Keshav Bansal, Abhinav Joshi, Ashutosh Modi
The proposed emotion-shift component is modular and can be added to any existing multimodal ERC model (with a few modifications).
1 code implementation • 3 Dec 2021 • Vijit Malik, Rishabh Sanjay, Shouvik Kumar Guha, Angshuman Hazarika, Shubham Nigam, Arnab Bhattacharya, Ashutosh Modi
For automatically segmenting the legal documents, we experiment with the task of rhetorical role prediction: given a document, predict the text segments corresponding to various roles.
no code implementations • SEMEVAL 2021 • Shashank Shailabh, Sajal Chaurasia, Ashutosh Modi
Finding relevant research papers and their contribution to the domain is a challenging problem.
no code implementations • SEMEVAL 2021 • Harshit Kumar, Jinang Shah, Nidhi Hegde, Priyanshu Gupta, Vaibhav Jindal, Ashutosh Modi
To tackle this issue of availability of annotated data, a lot of research has been done on unsupervised domain adaptation that tries to generate systems for an unlabelled target domain data, given labeled source domain data.
no code implementations • SEMEVAL 2021 • Aditya Jindal, Ankur Gupta, Jaya Srivastava, Preeti Menghwani, Vijit Malik, Vishesh Kaushik, Ashutosh Modi
There are two subtasks, in which given a table and a statement/fact, the subtask A is to determine whether the statement is inferred from the tabular data and the subtask B is to determine which cells in the table provide evidence for the former subtask.
no code implementations • SEMEVAL 2021 • Neil Shirude, Sagnik Mukherjee, Tushar Shandhilya, Ananta Mukherjee, Ashutosh Modi
This paper describes our contribution to SemEval 2021 Task 1 (Shardlow et al., 2021): Lexical Complexity Prediction.
1 code implementation • 26 Jul 2021 • Gargi Singh, Dhanajit Brahma, Piyush Rai, Ashutosh Modi
In this paper, we propose a new framework for fine-grained emotion prediction in the text through emotion definition modeling.
1 code implementation • 18 Jul 2021 • Ishika Singh, Gargi Singh, Ashutosh Modi
Given the sample-inefficiency of RL approaches, it is inefficient to learn rich enough textual representations to be able to understand and reason using the textual observation in such a complicated game environment setting.
1 code implementation • ACL 2021 • Vijit Malik, Rishabh Sanjay, Shubham Kumar Nigam, Kripa Ghosh, Shouvik Kumar Guha, Arnab Bhattacharya, Ashutosh Modi
The task requires an automated system to predict an explainable outcome of a case.
no code implementations • 12 May 2021 • Pradip Swarnakar, Ashutosh Modi
We investigate the opinions (sentiments) of major actors' narratives towards climate policy in the second methodology.
1 code implementation • 7 Apr 2021 • Aditya Jindal, Ankur Gupta, Jaya Srivastava, Preeti Menghwani, Vijit Malik, Vishesh Kaushik, Ashutosh Modi
Given a table and a statement/fact, subtask A determines whether the statement is inferred from the tabular data, and subtask B determines which cells in the table provide evidence for the former subtask.
1 code implementation • SEMEVAL 2021 • Archit Bansal, Abhay Kaushik, Ashutosh Modi
In this work, we present our approach and findings for SemEval-2021 Task 5 - Toxic Spans Detection.
no code implementations • SEMEVAL 2021 • Rohan Gupta, Jay Mundra, Deepak Mahajan, Ashutosh Modi
The task is a sentence pair classification problem where the goal is to detect whether a given word common to both the sentences evokes the same meaning.
1 code implementation • SEMEVAL 2021 • Abhishek Mittal, Ashutosh Modi
We fine-tuned the pre-trained masked language models namely BERT and ALBERT and used an Ensemble of these as our submitted system on Subtask 1 (ReCAM-Imperceptibility) and Subtask 2 (ReCAM-Nonspecificity).
1 code implementation • 4 Apr 2021 • Shashank Shailabh, Sajal Chaurasia, Ashutosh Modi
Finding relevant research papers and their contribution to the domain is a challenging problem.
1 code implementation • SEMEVAL 2021 • Akash Gangwar, Sabhay Jain, Shubham Sourav, Ashutosh Modi
This paper presents the system for SemEval 2021 Task 8 (MeasEval).
1 code implementation • SEMEVAL 2021 • Aishwarya Gupta, Avik Pal, Bholeshwar Khurana, Lakshay Tyagi, Ashutosh Modi
Humor and Offense are highly subjective due to multiple word senses, cultural knowledge, and pragmatic competence.
1 code implementation • 2 Apr 2021 • Neil Rajiv Shirude, Sagnik Mukherjee, Tushar Shandhilya, Ananta Mukherjee, Ashutosh Modi
This paper describes our contribution to SemEval 2021 Task 1: Lexical Complexity Prediction.
1 code implementation • 2 Mar 2021 • Aaditya Singh, Shreeshail Hingane, Saim Wani, Ashutosh Modi
The task of Emotion-Cause Pair Extraction (ECPE) aims to extract all potential clause-pairs of emotions and their corresponding causes in a document.
1 code implementation • EACL 2021 • Vijit Malik, Ashwani Bhat, Ashutosh Modi
Analysis of these attacks on the state of the art transformers in NLP can help improve the robustness of these models against such adversarial inputs.
1 code implementation • COLING 2020 • Ishika Singh, Ahsan Barkati, Tushar Goswamy, Ashutosh Modi
The model gives a user the flexibility to control the category and intensity of emotion as well as the topic of the generated text.
no code implementations • 30 Jul 2020 • Tushar Goswamy, Naishadh Parmar, Ayush Gupta, Raunak Shah, Vatsalya Tandon, Varun Goyal, Sanyog Gupta, Karishma Laud, Shivam Gupta, Sudhanshu Mishra, Ashutosh Modi
This research paper proposes a COVID-19 monitoring and response system to identify the surge in the volume of patients at hospitals and shortage of critical equipment like ventilators in South-east Asian countries, to understand the burden on health facilities.
no code implementations • FinNLP (COLING) 2020 • Vishal Keswani, Sakshi Singh, Ashutosh Modi
We leverage both context-dependent and context-independent word embeddings in our analysis.
1 code implementation • SEMEVAL 2020 • Karishma Laud, Jagriti Singh, Randeep Kumar Sahu, Ashutosh Modi
We submitted two models for sub-task C (offense target identification), one using soft labels and the other using BERT based fine-tuned model.
1 code implementation • SEMEVAL 2020 • Vishal Keswani, Sakshi Singh, Suryansh Agarwal, Ashutosh Modi
In this paper, we present our approaches for the Memotion Analysis problem as posed in SemEval-2020 Task 8.
1 code implementation • SEMEVAL 2020 • Vipul Singhal, Sahil Dhull, Rishabh Agarwal, Ashutosh Modi
This paper describes the system proposed for addressing the research problem posed in Task 10 of SemEval-2020: Emphasis Selection For Written Text in Visual Media.
no code implementations • SEMEVAL 2020 • Anirudh Anil Ojha, Rohin Garg, Shashank Gupta, Ashutosh Modi
This paper describes our efforts in tackling Task 5 of SemEval-2020.
1 code implementation • SEMEVAL 2020 • Paramansh Singh, Siraj Sandhu, Subham Kumar, Ashutosh Modi
This paper describes our submissions to SemEval 2020 Task 11: Detection of Propaganda Techniques in News Articles for each of the two subtasks of Span Identification and Technique Classification.
1 code implementation • SEMEVAL 2020 • Soumya Ranjan Dash, Sandeep Routray, Prateek Varshney, Ashutosh Modi
Out of the three subtasks, this paper reports the system description of subtask A and subtask B.
1 code implementation • SEMEVAL 2020 • Ayush Kumar, Harsh Agarwal, Keshav Bansal, Ashutosh Modi
Sentiment Analysis of code-mixed text has diversified applications in opinion mining ranging from tagging user reviews to identifying social or political sentiments of a sub-population.
no code implementations • SEMEVAL 2019 • Yeyao Zhang, Eleftheria Tsipidi, Sasha Schriber, Mubbasir Kapadia, Markus Gross, Ashutosh Modi
However, translating natural language text into animation is a challenging task.
no code implementations • 5 Apr 2019 • Sepehr Janghorbani, Ashutosh Modi, Jakob Buhmann, Mubbasir Kapadia
The process of creating such characters often involves a team of creative authors who describe different aspects of the characters in natural language, and planning experts that translate this description into a planning domain.
no code implementations • NAACL 2019 • Pierre Colombo, Wojciech Witon, Ashutosh Modi, James Kennedy, Mubbasir Kapadia
The majority of current systems for end-to-end dialog generation focus on response quality without an explicit control over the affective content of the responses.
no code implementations • NAACL 2019 • Pooja Chitkara, Ashutosh Modi, Pravalika Avvaru, Sepehr Janghorbani, Mubbasir Kapadia
Additionally, in contrast to offline processing of dialog, we also analyze the performance of our model in a more realistic setting i. e. in an online setting where the topic is identified in real time as the dialog progresses.
no code implementations • WS 2018 • Wojciech Witon, Pierre Colombo, Ashutosh Modi, Mubbasir Kapadia
This paper describes our participating system in the WASSA 2018 shared task on emotion prediction.
no code implementations • SEMEVAL 2018 • Simon Ostermann, Michael Roth, Ashutosh Modi, Stefan Thater, Manfred Pinkal
This report summarizes the results of the SemEval 2018 task on machine comprehension using commonsense knowledge.
no code implementations • LREC 2018 • Simon Ostermann, Ashutosh Modi, Michael Roth, Stefan Thater, Manfred Pinkal
We introduce a large dataset of narrative texts and questions about these texts, intended to be used in a machine comprehension task that requires reasoning using commonsense knowledge.
no code implementations • SEMEVAL 2017 • Dai Quoc Nguyen, Dat Quoc Nguyen, Ashutosh Modi, Stefan Thater, Manfred Pinkal
Our model generalizes the previous works in that it allows to induce different weights of different senses of a word.
no code implementations • LREC 2016 • Ashutosh Modi, Tatjana Anikina, Simon Ostermann, Manfred Pinkal
This paper presents the InScript corpus (Narrative Texts Instantiating Script structure).
no code implementations • TACL 2017 • Ashutosh Modi, Ivan Titov, Vera Demberg, Asad Sayeed, Manfred Pinkal
Recent research in psycholinguistics has provided increasing evidence that humans predict upcoming content.
no code implementations • 18 Dec 2013 • Ashutosh Modi, Ivan Titov
Induction of common sense knowledge about prototypical sequences of events has recently received much attention.