Papers by Siddhant Garg
Knowledge Transfer from Answer Ranking to Answer Generation (2022.emnlp-main)
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| Challenge: | Recent studies show that Question Answering (QA) based on Answer Sentence Selection (AS2) can be improved by generating an improved answer from the top-k ranked answer sentences. |
| Approach: | They propose to train a GenQA model by transferring knowledge from a trained AS2 model . they use top ranked candidate as the generation target and next k top rated candidates as context . |
| Outcome: | The proposed model outperforms existing models on public and industrial datasets. |
Pre-training Transformer Models with Sentence-Level Objectives for Answer Sentence Selection (2022.emnlp-main)
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| Challenge: | Existing models for answer sentence selection (AS2) are not yet available for AS2 . |
| Approach: | They propose to incorporate paragraph-level semantics within and across documents to improve transformers for AS2 . they propose to use a dataset to predict whether two sentences are extracted from the same paragraph . |
| Outcome: | The proposed model outperforms baseline models on public and industrial datasets on three public and one industrial dataset. |
ProMISe: A Proactive Multi-turn Dialogue Dataset for Information-seeking Intent Resolution (2024.findings-eacl)
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| Challenge: | Work done during internship at Amazon Alexa AI. |
| Approach: | They propose to use iterative suggested question-answering conversation to improve the trade-off between satisfaction of the user’s intent and keeping the information exchange natural. |
| Outcome: | The proposed proposed question-answering conversation improves the satisfaction of the user’s intent while keeping the information exchange natural and cognitive load of the interaction minimal on the users. |
Measuring Retrieval Complexity in Question Answering Systems (2024.findings-acl)
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| Challenge: | a new metric, retrieval complexity (RC), measures the difficulty of answering questions. |
| Approach: | They propose a retrieval complexity metric conditioned on the completeness of retrieved documents . they propose an unsupervised pipeline to measure RC given an arbitrary retrieval system . |
| Outcome: | The proposed pipeline measures RC more accurately than alternative estimators on six challenging QA benchmarks. |
Towards Improved Multi-Source Attribution for Long-Form Answer Generation (2024.naacl-long)
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| Challenge: | Current LLMs struggle with attribution for long-form answers which require reasoning over multiple evidence sources. |
| Approach: | They propose to improve attribution capability of large language models for long-form answer generation to multiple sources with multiple citations per sentence. |
| Outcome: | The proposed model improves on a wide range of attribution benchmark datasets on PolitiICite, a multi-source attribution dataset based on PolitIcite articles . |
Surprisingly Easy Hard-Attention for Sequence to Sequence Learning (D18-1)
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| Challenge: | Existing attention mechanisms are hard and hard, but they are more accurate when trained. |
| Approach: | They propose to use a beam approximation of the joint distribution between attention and output to train sequence to sequence learning. |
| Outcome: | The proposed method is compared to existing attention mechanisms on five translation tasks and shows consistent gains on the same tasks. |
Context-Aware Transformer Pre-Training for Answer Sentence Selection (2023.acl-short)
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| Challenge: | Existing approaches to perform Answer Sentence Selection (AS2) using only the candidate sentence are sub-optimal. |
| Approach: | They propose to use pre-trained transformers to perform contextual AS2 fine-tuning . they propose to apply pre-training objectives to local contextual AS2. |
| Outcome: | The proposed methods improve baseline AS2 accuracy by up to 8% on some datasets. |
Learning Answer Generation using Supervision from Automatic Question Answering Evaluators (2023.acl-long)
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| Challenge: | Recent studies show sentence-level extractive QA is outperformed by Generation-based QA (GenQA) models. |
| Approach: | They propose a training paradigm for GenQA using automatic QA evaluation models . they augment training data with answers generated by the GenQA model and labelled by GAVA . |
| Outcome: | The proposed training paradigm improves answering accuracy over existing models. |
Will this Question be Answered? Question Filtering via Answer Model Distillation for Efficient Question Answering (2021.emnlp-main)
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| Challenge: | Existing methods to improve QA efficiency do not take specific answers into account. |
| Approach: | They propose a transformer-based approach to improve QA efficiency by filtering out questions that will not be answered by the system. |
| Outcome: | The proposed model can approximate the Precision/Recall curves of the target QA system. |
Memory-QA: Answering Recall Questions Based on Multimodal Memories (2025.emnlp-main)
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Hongda Jiang, Xinyuan Zhang, Siddhant Garg, Rishab Arora, Shiun-Zu Kuo, Jiayang Xu, Aaron Colak, Xin Luna Dong
| Challenge: | Memory-QA is a real-world task that involves answering recall questions about visual content from previously stored multimodal memories. |
| Approach: | They propose a memory-QA task that involves answering recall questions about visual content from previously stored multimodal memories. |
| Outcome: | The proposed solution improves memory recording, compression, storage, and search accuracy over state-of-the-art solutions. |
BAE: BERT-based Adversarial Examples for Text Classification (2020.emnlp-main)
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| Challenge: | Recent studies have exposed the vulnerability of text classification models to adversarial examples . perturbed versions of the original text are indiscernible by humans and misclassified by the model . |
| Approach: | They propose a black box attack for generating adversarial examples using contextual perturbations from a BERT-masked language model. |
| Outcome: | The proposed attack produces examples with improved grammaticality and semantic coherence compared to previous work. |
Beyond Fine-tuning: Few-Sample Sentence Embedding Transfer (2020.aacl-main)
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| Challenge: | Fine-tuning (FT) pre-trained sentence embedding models on small datasets has been shown to have limitations. |
| Approach: | They propose to combine embeddings from a pre-trained model with a simple sentence embeddable model. |
| Outcome: | The proposed approach outperforms FT on small datasets with negligible computational overhead. |
Paragraph-based Transformer Pre-training for Multi-Sentence Inference (2022.naacl-main)
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| Challenge: | Recent studies show that pre-trained transformers perform poorly for multi-candidate inference tasks. |
| Approach: | They propose a pre-training objective that models paragraph-level semantics across multiple input sentences. |
| Outcome: | The proposed model outperforms existing models on three AS2 and one fact verification datasets. |