Papers with Answering questions
Latent Compositional Representations Improve Systematic Generalization in Grounded Question Answering (2021.tacl-1)
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| Challenge: | Neural networks fail to generalize to out-of-distribution examples that contain new compositions. |
| Approach: | They propose a model that computes a representation and denotation for all question spans in a bottom-up, compositional manner using a CKY-style parser. |
| Outcome: | The proposed model achieves 96.1% accuracy on a challenging dataset compared to baseline models on . previous models failed to generalize to out-of-distribution examples . |
Knowledge Base Question Answering through Recursive Hypergraphs (2021.eacl-main)
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| Challenge: | Existing methods for Knowledge Base Question Answering (KBQA) do not explicitly incorporate the recursive relational group structure in the given knowledge base. |
| Approach: | They propose a method to model KBs through recursive hypergraphs using hypergraph data. |
| Outcome: | The proposed method is based on recursive hypergraphs and has been released on multiple benchmarks. |
One-shot Learning for Question-Answering in Gaokao History Challenge (C18-1)
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| Challenge: | Existing work on deep question-answering tasks from admission exams is challenging since it requires effective representation to capture complicated semantic relations between questions and answers. |
| Approach: | They propose a hybrid neural model for deep question-answering task from history examinations using a gated network and a machine labeler. |
| Outcome: | The proposed model obtains substantial performance gains over baseline models in terms of multiple evaluation metrics. |
Hybrid Graphs for Table-and-Text based Question Answering using LLMs (2025.naacl-long)
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| Challenge: | Current methods for QA rely on fine-tuning and high-quality data, which is difficult to obtain. |
| Approach: | They propose a Hybrid Graph-based approach for Table-Text QA that leverages Large Language Models without fine-tuning. |
| Outcome: | The proposed approach improves Exact Match scores by 10% on Hybrid-QA and 5.4% on OTT-QA. |
TellMeWhy: A Dataset for Answering Why-Questions in Narratives (2021.findings-acl)
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| Challenge: | Existing models do not have the ability to answer "why" questions that require commonsense knowledge external to the narrative. |
| Approach: | They propose a crowd-sourced dataset that asks why characters perform actions . they show that state-of-the-art models are far below human performance on answering such questions . |
| Outcome: | The proposed dataset shows that state-of-the-art models are far below human performance on answering such questions. |
Using Commonsense Knowledge to Answer Why-Questions (2022.emnlp-main)
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Yash Kumar Lal, Niket Tandon, Tanvi Aggarwal, Horace Liu, Nathanael Chambers, Raymond Mooney, Niranjan Balasubramanian
| Challenge: | Existing approaches to integrating commonsense knowledge into large language models are implicit and explicit. |
| Approach: | They analyze the effects of model size and methods of injecting knowledge into TellMeWhy datasets to determine what aspects of commonsense knowledge are available in large language models. |
| Outcome: | The largest models yield substantial improvements over base models, but the amount of improvement decreases with larger model size. |
Question Answering with Long Multiple-Span Answers (2020.findings-emnlp)
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| Challenge: | Existing QA systems for question answering are limited by the availability of annotated datasets. |
| Approach: | They propose a dataset for question-answering that extracts information from multiple parts of text . they propose QA-based multi-span neural architecture that captures relevance among multiple answer spans . |
| Outcome: | The proposed model outperforms state-of-the-art QA models in this multi-span QA setting. |
Question Answering in Climate Adaptation for Agriculture: Model Development and Evaluation with Expert Feedback (2025.findings-acl)
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| Challenge: | Existing domain-specific question answering systems have generative capabilities, but their ability to answer climate adaptation questions remains unclear. |
| Approach: | They propose an iterative framework that enables LLMs to dynamically aggregate information from heterogeneous sources, such as climate literature and structured tabular climate data from climate model projections and historical observations. |
| Outcome: | The proposed framework enables LLMs to dynamically aggregate information from heterogeneous sources, such as text from climate literature and structured tabular climate data from climate model projections and historical observations. |
Question Answering over Electronic Devices: A New Benchmark Dataset and a Multi-Task Learning based QA Framework (2021.findings-emnlp)
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| Challenge: | EMQAP is an automated question answering system for electronics devices . it uses a supervised multitask learning framework to identify the section in the E-manual where the answer can be found and the exact answer span within that section. |
| Approach: | They develop an algorithm to exploit data from E-manuals and pretrain RoBERTa on it. |
| Outcome: | The proposed algorithm improves ROUGE-L F1 scores over most competitive baseline. |
BizBench: A Quantitative Reasoning Benchmark for Business and Finance (2024.acl-long)
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| Challenge: | Answering questions within business and finance requires reasoning, precision, and a wide-breadth of technical knowledge. |
| Approach: | They propose a benchmark for evaluating models’ ability to reason about realistic financial problems by focusing on question-answering over financial data via program synthesis. |
| Outcome: | The proposed benchmark evaluates models' financial background knowledge, ability to parse financial documents, and capacity to solve complex problems with code. |
FinMath: Injecting a Tree-structured Solver for Question Answering over Financial Reports (2022.lrec-1)
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| Challenge: | Existing models for answering complex questions require multiple-step numerical reasoning. |
| Approach: | They propose a framework that injects a tree-structured neural model into a model to perform multi-step numerical reasoning. |
| Outcome: | The proposed framework improves the previous best model by 8.5% absolute for Exact Match (EM) score and 6.1% absolute for numeracy-focused F1 score. |