1 code implementation • EMNLP 2021 • Adyasha Maharana, Mohit Bansal
Such information is even more important for story visualization since its inputs have an explicit narrative structure that needs to be translated into an image sequence (or visual story).
1 code implementation • NAACL 2022 • Adyasha Maharana, Mohit Bansal
Hence, we examine the effect of a human-like easy-to-difficult curriculum during finetuning of language models for commonsense reasoning tasks.
1 code implementation • COLING 2022 • Adyasha Maharana, Mohit Bansal
Recent advances in commonsense reasoning have been fueled by the availability of large-scale human annotated datasets.
no code implementations • Findings (NAACL) 2022 • Adyasha Maharana, Quan Tran, Franck Dernoncourt, Seunghyun Yoon, Trung Bui, Walter Chang, Mohit Bansal
We construct and present a new multimodal dataset consisting of software instructional livestreams and containing manual annotations for both detailed and abstract procedural intent that enable training and evaluation of joint video and text understanding models.
no code implementations • 27 Feb 2024 • Adyasha Maharana, Dong-Ho Lee, Sergey Tulyakov, Mohit Bansal, Francesco Barbieri, Yuwei Fang
Using this pipeline, we collect LoCoMo, a dataset of very long-term conversations, each encompassing 300 turns and 9K tokens on avg., over up to 35 sessions.
1 code implementation • 28 Nov 2023 • Vaidehi Patil, Adyasha Maharana, Mohit Bansal
In this paper, we study bias arising from confounders in a causal graph for multimodal data and examine a novel approach that leverages causally-motivated information minimization to learn the confounder representations.
1 code implementation • 11 Oct 2023 • Adyasha Maharana, Prateek Yadav, Mohit Bansal
There are two dominant approaches: (1) geometry-based data selection for maximizing data diversity in the coreset, and (2) functions that assign difficulty scores to samples based on training dynamics.
1 code implementation • 28 Mar 2023 • Adyasha Maharana, Amita Kamath, Christopher Clark, Mohit Bansal, Aniruddha Kembhavi
As general purpose vision models get increasingly effective at a wide set of tasks, it is imperative that they be consistent across the tasks they support.
1 code implementation • 13 Sep 2022 • Adyasha Maharana, Darryl Hannan, Mohit Bansal
Hence, we first propose the task of story continuation, where the generated visual story is conditioned on a source image, allowing for better generalization to narratives with new characters.
Ranked #2 on Story Continuation on FlintstonesSV
1 code implementation • 21 Oct 2021 • Adyasha Maharana, Mohit Bansal
Prior work in this domain has shown that there is ample room for improvement in the generated image sequence in terms of visual quality, consistency and relevance.
1 code implementation • NAACL 2021 • Adyasha Maharana, Darryl Hannan, Mohit Bansal
Therefore, we also provide an exploration of evaluation metrics for the model, focused on aspects of the generated frames such as the presence/quality of generated characters, the relevance to captions, and the diversity of the generated images.
1 code implementation • Findings of the Association for Computational Linguistics 2020 • Adyasha Maharana, Mohit Bansal
In this work, we present several effective adversaries and automated data augmentation policy search methods with the goal of making reading comprehension models more robust to adversarial evaluation, but also improving generalization to the source domain as well as new domains and languages.
no code implementations • WS 2017 • Adyasha Maharana, Meliha Yetisgen
Event detection from clinical notes has been traditionally solved with rule based and statistical natural language processing (NLP) approaches that require extensive domain knowledge and feature engineering.