Papers by Adyasha Maharana
Multimodal Intent Discovery from Livestream Videos (2022.findings-naacl)
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Adyasha Maharana, Quan Tran, Franck Dernoncourt, Seunghyun Yoon, Trung Bui, Walter Chang, Mohit Bansal
| Challenge: | Existing models for instructional video understanding struggle to understand abstract intents . identifying procedural intent within instructional videos is a challenging task . |
| Approach: | They propose to extract instructional intent from software instructional livestreams by using a multimodal cascaded cross-attention model that integrates weaker and noisier video signals with more discriminative text signals. |
| Outcome: | The proposed model improves on baseline models and compares it to existing models. |
GraDA: Graph Generative Data Augmentation for Commonsense Reasoning (2022.coling-1)
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| Challenge: | Recent advances in commonsense reasoning have been fueled by the availability of large-scale human annotated datasets. |
| Approach: | They propose a graph-generative data augmentation framework to synthesize factual data samples from knowledge graphs for commonsense reasoning. |
| Outcome: | The proposed framework improves SocialIQA, CODAH, HellaSwag and CommonsenseQA . it also performs well for generative tasks like ProtoQA proving its robustness to adversaries . |
On Curriculum Learning for Commonsense Reasoning (2022.naacl-main)
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| Challenge: | Recent research suggests that data order can have a significant impact on the performance of finetuned models for natural language understanding. |
| Approach: | They use paced curriculum learning to rank data and sample training mini-batches with increasing levels of difficulty during finetuning. |
| Outcome: | The proposed model improves performance for socialIQA, CosmosQA, CODAH, HellaSwag, WinoGrande in both tuning settings. |
Integrating Visuospatial, Linguistic, and Commonsense Structure into Story Visualization (2021.emnlp-main)
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| Challenge: | Existing work on text-to-image synthesis does not explore the use of linguistic structure of the input text. |
| Approach: | They propose to use constituency parse trees to encode structured input and a Transformer-based recurrent architecture to augment commonsense information to generate visual story. |
| Outcome: | The proposed model improves visual quality and consistency of images from a target domain without fine-tuning. |
Evaluating Very Long-Term Conversational Memory of LLM Agents (2024.acl-long)
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| Challenge: | Existing studies on long-term open-domain dialogues focus on evaluating responses within contexts spanning no more than five chat sessions. |
| Approach: | They propose a machine-human pipeline to generate very long-term dialogues by leveraging LLMs and retrieval augmented generation techniques. |
| Outcome: | The proposed pipeline generates very long-term dialogues using LLMs and RAGs . the generated conversations are verified and edited by human annotators for long-range consistency and grounding to the event graphs. |
Analysis of Tree-Structured Architectures for Code Generation (2021.findings-acl)
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| Challenge: | Code generation is the task of generating code snippets from input user specifications written in natural language (NL). |
| Approach: | They evaluate the significance of input parse trees for code generation by using constituency-based parsers as input and an abstract syntax tree as the target. |
| Outcome: | The proposed models on a Python-based code generation dataset and a semantic parsing dataset show that constituency trees encoded using a structure-aware model improve performance. |
Improving Generation and Evaluation of Visual Stories via Semantic Consistency (2021.naacl-main)
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| Challenge: | Story visualization is an underexplored task that requires a generative model to generate images . prior work has focused on image generation but there is room for improvement . |
| Approach: | They propose to add a dual learning framework to reinforce semantic alignment between story and generated images and a copy-transform mechanism to model sequentially-consistent story visualization. |
| Outcome: | The proposed models outperform text-to-image synthesis models on the story visualization task . the proposed models also improve visual quality, coherence and relevance . |
Debiasing Multimodal Models via Causal Information Minimization (2023.findings-emnlp)
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| Challenge: | Existing methods for debiasing multimodal models use approximate heuristics to represent the biases, such as shallow features from early stages of training or unimodal features for multimodal tasks like VQA, which may not be accurate. |
| Approach: | They propose a method that leverages causally-motivated information minimization to learn the confounder representations of a causal graph for multimodal data. |
| Outcome: | The proposed method improves out-of-distribution performance on multiple multimodal datasets without sacrificing in-distance performance. |
Adversarial Augmentation Policy Search for Domain and Cross-Lingual Generalization in Reading Comprehension (2020.findings-emnlp)
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| Challenge: | Reading comprehension models often overfit to nuances of training datasets and fail at adversarial evaluation. |
| Approach: | They propose a method that introduces multiple points of confusion within the context and shows dependence on insertion location of the distractor. |
| Outcome: | The proposed methods improve robustness against adversarial evaluation but weak generalization to the source domain and new domains and languages. |