Papers with QA models
UKP-SQuARE v2: Explainability and Adversarial Attacks for Trustworthy QA (2022.aacl-demo)
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Rachneet Sachdeva, Haritz Puerto, Tim Baumgärtner, Sewin Tariverdian, Hao Zhang, Kexin Wang, Hossain Shaikh Saadi, Leonardo F. R. Ribeiro, Iryna Gurevych
| Challenge: | Question Answering (QA) systems rely on deep neural networks, which are difficult to interpret by humans. |
| Approach: | They propose an interpretable model that provides an explanation infrastructure for comparing models based on saliency maps and graph-based explanations. |
| Outcome: | The proposed methods can be used to compare models based on saliency maps and graph-based explanations. |
TimelineQA: A Benchmark for Question Answering over Timelines (2023.findings-acl)
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Wang-Chiew Tan, Jane Dwivedi-Yu, Yuliang Li, Lambert Mathias, Marzieh Saeidi, Jing Nathan Yan, Alon Halevy
| Challenge: | Existing question answering techniques for lifelogs do not provide accurate answers . augmented reality glasses have led to the creation of personal assistants . |
| Approach: | They propose to use a benchmark to query lifelogs to find out what happened in real life . they find that extractive QA systems out-perform retrieval-augmented QA techniques . |
| Outcome: | The proposed method outperforms state-of-the-art retrieval-augmented QA systems in atomic queries and multi-hop queries. |
A Pipeline for Generating, Annotating and Employing Synthetic Data for Real World Question Answering (2022.emnlp-demos)
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| Challenge: | Question Answering (QA) is a growing area of research . state-of-the-art QA models struggle on out-of domain documents without fine-tuning . |
| Approach: | They propose a pipeline for validating and training QA data and an interface for human annotation. |
| Outcome: | The proposed pipeline improves QA performance on domain-specific datasets while preserving the accuracy of the model. |
BiQuAD: Towards QA based on deeper text understanding (2021.starsem-1)
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| Challenge: | Recent question answering and machine reading benchmarks require systems to pinpoint the span of the answer to a given text. |
| Approach: | They propose a dataset that requires deeper comprehension to answer questions extractively and deductively. |
| Outcome: | The proposed dataset outperforms existing benchmarks on extractive and deductive questions. |
FlowDelta: Modeling Flow Information Gain in Reasoning for Conversational Machine Comprehension (D19-58)
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| Challenge: | Existing machine comprehension models focus on a single-turn setting and do not account for previous reasoning processes. |
| Approach: | They propose to explicitly model the information gain through the dialogue reasoning . they propose to apply the proposed mechanism to other machine comprehension models . |
| Outcome: | The proposed model achieves state-of-the-art performance in a conversational QA dataset QuAC and a sequential instruction understanding dataset SCONE. |
Do Multi-hop Readers Dream of Reasoning Chains? (D19-58)
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| Challenge: | Existing models for multihop reasoning are limited in their performance . multi-hop reasoning requires the ability to gather information from multiple passages . |
| Approach: | They propose a method that provides the full reasoning chain of multiple passages instead of just one final passage where the answer appears. |
| Outcome: | The proposed model improves on existing models by providing the full reasoning chain of multiple passages instead of just one final passage where the answer appears. |
Generative Data Augmentation using LLMs improves Distributional Robustness in Question Answering (2024.eacl-srw)
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| Challenge: | Existing domain adaptation methods do not account for unseen natural distribution shifts. |
| Approach: | They perform experiments on 4 different datasets under varying amounts of distribution shift . they analyze how "in-the-wild" generation can help achieve domain generalization . |
| Outcome: | The proposed approach augments reading comprehension datasets with generated data to improve robustness towards natural distribution shifts. |
AIT-QA: Question Answering Dataset over Complex Tables in the Airline Industry (2022.naacl-industry)
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Yannis Katsis, Saneem Chemmengath, Vishwajeet Kumar, Samarth Bharadwaj, Mustafa Canim, Michael Glass, Alfio Gliozzo, Feifei Pan, Jaydeep Sen, Karthik Sankaranarayanan, Soumen Chakrabarti
| Challenge: | Table Question Answering (Table QA) systems have been shown to be highly accurate when trained and tested on open-domain datasets built on top of Wikipedia tables. |
| Approach: | They propose a domain-specific Table QA test dataset to test Table Question Answering systems on open-domain datasets built on top of Wikipedia tables. |
| Outcome: | The proposed methods are highly accurate when tested on open-domain datasets built on top of Wikipedia tables. |
Fantastic Questions and Where to Find Them: FairytaleQA – An Authentic Dataset for Narrative Comprehension (2022.acl-long)
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Ying Xu, Dakuo Wang, Mo Yu, Daniel Ritchie, Bingsheng Yao, Tongshuang Wu, Zheng Zhang, Toby Li, Nora Bradford, Branda Sun, Tran Hoang, Yisi Sang, Yufang Hou, Xiaojuan Ma, Diyi Yang, Nanyun Peng, Zhou Yu, Mark Warschauer
| Challenge: | Existing QA datasets rarely distinguish fine-grained reading skills, such as the understanding of varying narrative elements. |
| Approach: | They propose to use FairytaleQA to generate 10,580 questions based on 278 children-friendly stories to assess model's fine-grained learning skills. |
| Outcome: | The proposed dataset consists of 10,580 questions derived from 278 children-friendly stories, covering seven types of narrative elements or relations. |
Reinforced Question Rewriting for Conversational Question Answering (2022.emnlp-industry)
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| Challenge: | Existing approaches to CQA involve training new models from scratch . existing approaches are expensive and often not feasible . |
| Approach: | They propose to use QA feedback to supervise the rewriting model with reinforcement learning. |
| Outcome: | The proposed model can improve QA performance over baselines for extractive and retrieval QA. |
What Does My QA Model Know? Devising Controlled Probes Using Expert Knowledge (2020.tacl-1)
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| Challenge: | Existing models are far from perfect when assessed at the level of clusters of semantically connected probes, such as all hypernym questions about a single concept. |
| Approach: | They propose a method for automatically building probe datasets from expert knowledge sources, allowing systematic control and a comprehensive evaluation. |
| Outcome: | The proposed model is predisposed to recognize certain types of structural linguistic knowledge, but performance degrades even with a slight increase in the number of “hops” in the underlying taxonomic hierarchy. |
Narrative Question Answering with Cutting-Edge Open-Domain QA Techniques: A Comprehensive Study (2021.tacl-1)
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| Challenge: | Recent advances in open-domain question answering (ODQA) have led to human-level performance on many datasets. |
| Approach: | They provide a comprehensive and quantitative analysis about the difficulty of book QA . they compare the results of their research with extensive ODQA experiments . |
| Outcome: | The proposed model outperforms existing models on event-oriented questions on the NarrativeQA dataset. |
Accurate Training of Web-based Question Answering Systems with Feedback from Ranked Users (2023.acl-industry)
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| Challenge: | Recent work shows that large-scale annotated datasets are essential for training state-of-the-art Question Answering (QA) models. |
| Approach: | They use large-scale annotated datasets to train question answering models . they use feedback data collected from deployed QA systems to provide cheaper supervision . |
| Outcome: | The proposed model improves on the large scale annotated datasets from QA systems . the proposed model can be easily supervised on large-scale unlabeled web data . |
Multi-Relational Question Answering from Narratives: Machine Reading and Reasoning in Simulated Worlds (P18-1)
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| Challenge: | Question Answering (QA) has primarily focused on knowledge bases or free text as a source of knowledge. |
| Approach: | They propose a task of multi-relational QA over personal narrative using text worlds . they generate and release a lightweight Python-based framework for easily generating additional worlds and narrative . |
| Outcome: | The proposed framework combines elements of structured QA over knowledge bases and unstructured QA . it generates and analyzes five diverse datasets with dynamic narrative . the framework is lightweight and easy to use . |
DREAM: Improving Situational QA by First Elaborating the Situation (2022.naacl-main)
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| Challenge: | Cognitive science has long promoted the formation of mental models as central to understanding and question-answering. |
| Approach: | They train a new model, DREAM, to answer questions that elaborate the scenes that situated questions are about and then provide those elaborations as additional context to a question-answering (QA) model. |
| Outcome: | The proposed model is able to create better scene elaborations than a representative state-of-the-art, zero-shot model. |
Improving compositional generalization for multi-step quantitative reasoning in question answering (2022.emnlp-main)
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| Challenge: | Quantitative reasoning is an important aspect of question answering when numeric and verbal cues interact to indicate sophisticated, multi-step programs. |
| Approach: | They propose a method that encourages QA models to adjust attention patterns and capture input/output alignments that are meaningful to the reasoning task. |
| Outcome: | The proposed approach improves program accuracy and renders models more robust against overfitting as the number of reasoning steps grows. |
Supervised and Unsupervised Transfer Learning for Question Answering (N18-1)
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| Challenge: | Several QA scenarios and datasets have been introduced over the past few years. |
| Approach: | They conduct extensive experiments to investigate the transferability of knowledge from a source QA dataset to a target dataset using two QA models. |
| Outcome: | The proposed model outperforms the previous best model on TOEFL listening comprehension test by 7% on target datasets. |
QA Domain Adaptation using Hidden Space Augmentation and Self-Supervised Contrastive Adaptation (2022.emnlp-main)
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| Challenge: | Question answering models often suffer from performance deterioration upon deployment . |
| Approach: | They propose a self-supervised framework called QADA for QA domain adaptation . they propose to augment training QA samples with hidden space augmentation . |
| Outcome: | The proposed framework improves on multiple target datasets over state-of-the-art methods. |
Unsupervised Question Answering via Answer Diversifying (2022.coling-1)
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| Challenge: | Existing extractive question answering methods use labeled data to train QA models. |
| Approach: | They propose an unsupervised method by diversifying answers by using data construction, data augmentation and denoising filter. |
| Outcome: | The proposed method outperforms previous models on five benchmark datasets . it shows strong performance in the few-shot learning setting . |
Multi-View Source Ablation for Faithful Summarization (2023.findings-eacl)
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| Challenge: | MuFaSSa is a metric for evaluating faithfulness of abstractive summaries . it uses different strategies to remove information from source document to form multiple ablated views . |
| Approach: | They propose a metric for evaluating faithfulness of abstractive summaries using multiple ablated views. |
| Outcome: | The proposed metric outperforms existing models on summarization tasks and human-annotated faithfulness labels. |
What if you said that differently?: How Explanation Formats Affect Human Feedback Efficacy and User Perception (2024.naacl-long)
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| Challenge: | Question answering models can often be black boxes, as their reasoning process is mostly opaque. |
| Approach: | They analyze the effect of rationales generated by QA models on user feedback and how well they enable users to understand and trust model answers. |
| Outcome: | The proposed model can be used to improve model responses by removing feedback from end users and enhancing model outputs by using natural language feedback. |
Context-Aware Answer Extraction in Question Answering (2020.emnlp-main)
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| Challenge: | Extractive QA models have shown promising performance in predicting the correct answer to a given question. |
| Approach: | They propose a BLANC-based context prediction task that learns the context prediction tasks. |
| Outcome: | The proposed model outperforms the state-of-the-art models on reading comprehension and hotpotQA. |
Weakly-Supervised Questions for Zero-Shot Relation Extraction (2023.eacl-main)
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| Challenge: | Zero-Shot Relation Extraction (ZRE) is a task where the training and test sets have no shared relation types. |
| Approach: | They propose to learn a model that can translate relation descriptions into relevant questions, which are then leveraged to generate the correct tail entity. |
| Outcome: | The proposed model outperforms the state-of-the-art on the fewrel and WikiZSL datasets by more than 16 F1 points without using gold question templates. |
Counterfactual Multihop QA: A Cause-Effect Approach for Reducing Disconnected Reasoning (2023.acl-long)
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| Challenge: | Existing QA models rely on shortcuts to provide the true answer, referred to as disconnected reasoning problem. |
| Approach: | They propose a causal-effect approach that exploits true multi-hop reasoning instead of shortcuts. |
| Outcome: | The proposed method achieves 5.8% higher points of its Supps score on hotpotQA through true multihop reasoning. |
Explanations for CommonsenseQA: New Dataset and Models (2021.acl-long)
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Shourya Aggarwal, Divyanshu Mandowara, Vishwajeet Agrawal, Dinesh Khandelwal, Parag Singla, Dinesh Garg
| Challenge: | a dataset called CommonsenseQA (CQA) was recently released to advance the research on common-sense question answering (QA) |
| Approach: | They propose to retrieve and generate explanations for a given question, correct answer choice, incorrect answer choices tuple from a dataset called CommonsenseQA. |
| Outcome: | The proposed model beats baseline model by 100% in F1 score and similarity score of 61.9 . |
Learning Invariant Representation Improves Robustness for MRC Models (2022.findings-emnlp)
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| Challenge: | Existing approaches to improve machine reading comprehension models are vulnerable and not robust to adversarial examples. |
| Approach: | They propose to construct positive example pairs which have same answer by augmentation and then introduce stability and contrastive loss to improve invariance of representation. |
| Outcome: | The proposed approach boosts the robustness of QA models across different tasks and attack sets significantly and consistently. |
Finding Generalizable Evidence by Learning to Convince Q&A Models (D19-1)
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| Challenge: | a system that finds the strongest supporting evidence for a given answer is proposed . a study using passage-based question-answering (QA) shows that agents select evidence that generalizes . |
| Approach: | They propose a system that finds the strongest supporting evidence for a given answer . they use passage-based question-answering (QA) as a testbed to train evidence agents . |
| Outcome: | The proposed system improves QA in a robust manner by using agent-selected evidence. |
ConditionalQA: A Complex Reading Comprehension Dataset with Conditional Answers (2022.acl-long)
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| Challenge: | Existing datasets for reading comprehension have deterministic answers, but questions in the real world do not always have definite answers. |
| Approach: | They propose a Question Answering (QA) dataset that contains complex questions with conditional answers. |
| Outcome: | The proposed dataset will motivate further research in answering complex questions over long documents. |
TAT-QA: A Question Answering Benchmark on a Hybrid of Tabular and Textual Content in Finance (2021.acl-long)
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Fengbin Zhu, Wenqiang Lei, Youcheng Huang, Chao Wang, Shuo Zhang, Jiancheng Lv, Fuli Feng, Tat-Seng Chua
| Challenge: | Existing QA systems focus on unstructured text, structured knowledge base, or semi-structured tables. |
| Approach: | They propose a large-scale question answering model based on financial reports . numerical reasoning is usually required to infer the answer . |
| Outcome: | The proposed model achieves 58.0% inF1, an 11.1% increase over the baseline model, but still lags behind the best human model. |
Mitigating Bias for Question Answering Models by Tracking Bias Influence (2024.naacl-long)
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Mingyu Ma, Jiun-Yu Kao, Arpit Gupta, Yu-Hsiang Lin, Wenbo Zhao, Tagyoung Chung, Wei Wang, Kai-Wei Chang, Nanyun Peng
| Challenge: | Existing literature observes bias in question answering (QA) models, but there is no method to mitigate it. |
| Approach: | They propose an approach to mitigate the bias of question answering models by observing the influence of a query instance on another instance. |
| Outcome: | The proposed method reduces bias level in all 9 bias categories while maintaining comparable QA accuracy. |
NoiseQA: Challenge Set Evaluation for User-Centric Question Answering (2021.eacl-main)
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| Challenge: | Question-Answering (QA) systems are deployed in the real world . a lack of research attention has been devoted to studying the issues that arise when people use QA systems. |
| Approach: | They show that component components that precede an answering engine can introduce varied and considerable sources of error. |
| Outcome: | The proposed evaluations highlight the need for QA evaluation to expand to consider real-world use. |
Cheater’s Bowl: Human vs. Computer Search Strategies for Open-Domain QA (2022.findings-emnlp)
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| Challenge: | Open-domain and multi-hop QA is an important problem for both humans and computers. |
| Approach: | They propose a gamified interface where a human answers complex questions with access to traditional and modern search tools. |
| Outcome: | The proposed interface compares human queries to state-of-the-art QA models . human queries can improve the accuracy of existing systems, the authors argue . |
Repurposing Entailment for Multi-Hop Question Answering Tasks (N19-1)
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| Challenge: | Existing approaches to use entailment models for question answering are limited . large scale datasets are typically framed at a sentence level, whereas question answering requires verifying whether multiple sentences, taken together as a premise, entitle a hypothesis. |
| Approach: | They propose a general architecture that can use entailment models for multi-hop QA tasks. |
| Outcome: | The proposed model outperforms QA models trained on target datasets and the OpenAI transformer models. |
Can NLI Models Verify QA Systems’ Predictions? (2021.findings-emnlp)
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| Challenge: | Recent question answering systems perform well on benchmark datasets, but are not always well-calibrated to spot spurious answers under distribution shifts. |
| Approach: | They propose to use natural language inference to verify whether answers are correct . they leverage large pre-trained models and recent prior datasets to construct powerful question conversion and decontextualization modules. |
| Outcome: | The proposed approach improves the confidence estimation of a QA model across different domains, evaluated in a selective QA setting. |
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. |
NOAHQA: Numerical Reasoning with Interpretable Graph Question Answering Dataset (2021.findings-emnlp)
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| Challenge: | Existing question answering datasets lack numerical reasoning and reasoning processes . current research on numerical reasoning focuses on simple calculations . |
| Approach: | They propose a conversational and bilingual question answering dataset with numerical reasoning with compound mathematical expressions. |
| Outcome: | The proposed model achieves 55.5 exact match scores while human performance is 89.7. |
Confidence-guided Refinement Reasoning for Zero-shot Question Answering (2025.emnlp-main)
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| Challenge: | Existing frameworks that generate single-step reasoning do not improve QA reasoning . |
| Approach: | They propose a framework that strategically constructs and refines sub-questions and their answers (sub-QAs) they argue that sub-QA does not always enhance QA reasoning . |
| Outcome: | The proposed framework can be integrated with existing QA models and benchmarks. |
Template-Based Question Generation from Retrieved Sentences for Improved Unsupervised Question Answering (2020.acl-main)
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| Challenge: | Question Answering (QA) is a field of increasing demand due to the availability of information online. |
| Approach: | They propose an unsupervised approach to training QA models with generated pseudo-training data by applying a simple template on a related sentence rather than the original context sentence. |
| Outcome: | The proposed approach improves the performance of a QA model on generated pseudo-training data. |
Learning to Perturb Word Embeddings for Out-of-distribution QA (2021.acl-long)
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| Challenge: | QA models that are pretraining with unlabeled data can overfit and may not generalize well to unseen data that falls outside the training distribution. |
| Approach: | They propose a method which perturbs word embedding without changing their semantics. |
| Outcome: | The proposed method outperforms baseline methods on five target domains on a single source dataset on five different target domain domains. |
A Nil-Aware Answer Extraction Framework for Question Answering (D18-1)
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| Challenge: | Recent research suggests that reading comprehension-based question answering systems assume that every question has a valid answer in the associated passage. |
| Approach: | They propose a novel nil-aware answer span extraction framework that can return Nil or a text span from the associated passage as an answer in a single step. |
| Outcome: | The proposed framework outperforms baseline approaches on a newsQA dataset. |
RoMQA: A Benchmark for Robust, Multi-evidence, Multi-answer Question Answering (2023.findings-emnlp)
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| Challenge: | Existing QA models are not robust to variations in question constraints, but can be made more robust by tuning on clusters of related questions. |
| Approach: | They introduce RoMQA, the first benchmark for robust, multi-evidence, multianswer question answering (QA) RoMQ contains clusters of related questions that are derived from the Wikidata knowledge graph . |
| Outcome: | The proposed model is the first benchmark for robust, multi-evidence, multianswer question answering (QA) compared to prior QA datasets, it has more human-written questions that require reasoning over more evidence text and have, on average, many more correct answers. |
Learn to Resolve Conversational Dependency: A Consistency Training Framework for Conversational Question Answering (2021.acl-long)
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| Challenge: | Existing approaches do not explicitly train QA models on how to resolve conversational dependency, and thus these models are limited in understanding human dialogues. |
| Approach: | They propose a framework that generates self-contained questions that can be understood without the conversation history and then trains a QA model with the pairs of original and self-constructed questions using a consistency-based regularizer. |
| Outcome: | The proposed framework improves the models’ performance by up to 1.2 F1 on QuAC, and 5.2 F1 for CANARD, while addressing the limitations of the existing approaches. |
Unsupervised Question Answering by Cloze Translation (P19-1)
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| Challenge: | Existing QA datasets only available for limited domains and languages. |
| Approach: | They propose to generate context, question and answer triples in an unsupervised manner and synthesize extractive QA training data automatically. |
| Outcome: | The proposed approach outperforms existing QA models on a common EQA benchmark dataset. |
Interpretable Question Answering on Knowledge Bases and Text (P19-1)
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| Challenge: | Existing evaluation paradigms for ML based question answering models are lacking . a lack of explanation methods has been proposed for QA models . |
| Approach: | They propose an automatic evaluation paradigm for explanation methods in ML based question answering models . they adapt post hoc explanation methods such as LIME and input perturbation to the model . |
| Outcome: | The proposed evaluation paradigm compares explanation methods with human annotations. |
A Lightweight Method to Generate Unanswerable Questions in English (2023.findings-emnlp)
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| Challenge: | Existing approaches to build robust question answering models are too complex . antonym and entity swaps on answerable questions are used to build models . |
| Approach: | They propose a method for performing antonym and entity swaps on unanswerable questions. |
| Outcome: | The proposed method outperforms the previous state-of-the-art and has higher human-judged relatedness and readability. |
Preserving Commonsense Knowledge from Pre-trained Language Models via Causal Inference (2023.acl-long)
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Junhao Zheng, Qianli Ma, Shengjie Qiu, Yue Wu, Peitian Ma, Junlong Liu, Huawen Feng, Xichen Shang, Haibin Chen
| Challenge: | Existing studies attribute catastrophic forgetting to fine-tuning, and they retain pre-trained knowledge indiscriminately without identifying what knowledge is transferable. |
| Approach: | They propose a unified objective for fine-tuning to retrieve the causality back from pre-trained data and use it to mitigate negative transfer while preserving knowledge. |
| Outcome: | The proposed method outperforms state-of-the-art fine-tuning methods on commonsense QA datasets and can be implemented as a plug-in module to inflate the performance of existing QA models. |
Reason first, then respond: Modular Generation for Knowledge-infused Dialogue (2022.findings-emnlp)
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| Challenge: | Large language models can produce fluent dialogue but often hallucinate factual inaccuracies. |
| Approach: | They propose a modular model for incorporating knowledge into conversational agents that generates a knowledge sequence and then attends to its own generated knowledge sequence. |
| Outcome: | The proposed model hallucinates less in knowledge-grounded dialogue tasks and has advantages in terms of interpretability and modularity. |
Structured List-Grounded Question Answering (2025.coling-main)
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| Challenge: | Document-grounded dialogue systems aim to answer user queries by leveraging external information. |
| Approach: | They propose a dataset to evaluate QA systems' ability to interpret and use structured lists . they use language models and model-based filtering processes to enhance data quality . |
| Outcome: | The proposed model outperforms baselines on the LIST2QA dataset . it shows that the proposed model is more accurate and complete than baselines . |
DisentQA: Disentangling Parametric and Contextual Knowledge with Counterfactual Question Answering (2023.acl-long)
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| Challenge: | Question answering models have access to two sources of knowledge during inference time: parametric knowledge and contextual knowledge. |
| Approach: | They propose a new paradigm in which QA models are trained to disentangle the two sources of knowledge. |
| Outcome: | The proposed model generates two answers for a given question based on parametric and contextual knowledge. |
Improving the Robustness of Question Answering Systems to Question Paraphrasing (P19-1)
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| Challenge: | Despite advancement of question answering systems, generalizability of QA models is a topic of concern. |
| Approach: | They propose to use a neural paraphrasing model to generate multiple paraphrased questions for a given source question and a set of paraphrase suggestions to re-train the models. |
| Outcome: | The proposed approach requires no human intervention to re-train the models for improved robustness to question paraphrasing. |
Knowledge Graph - Deep Learning: A Case Study in Question Answering in Aviation Safety Domain (2022.lrec-1)
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Ankush Agarwal, Raj Gite, Shreya Laddha, Pushpak Bhattacharyya, Satyanarayan Kar, Asif Ekbal, Prabhjit Thind, Rajesh Zele, Ravi Shankar
| Challenge: | Existing Question Answering systems for commercial aviation use a large number of documents . a Knowledge Graph (KG) guided Deep Learning (DL) based system can be used to query the documents based on accident reports . |
| Approach: | They propose a Knowledge Graph (KG) guided Deep Learning (DL) based Question Answering system to cater to these requirements. |
| Outcome: | The proposed system achieves 7% and 40% increase in accuracy over existing systems. |
Automatic Spanish Translation of SQuAD Dataset for Multi-lingual Question Answering (2020.lrec-1)
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| Challenge: | Existing methods to train multilingual QA systems are limited for other languages . cross-lingual learning is a technique that transfers knowledge from source to target language with fewer training data. |
| Approach: | They propose a translation method to translate the Stanford Question Answering Dataset to Spanish and a multilingual-BERT model to train Spanish QA systems. |
| Outcome: | The proposed method outperforms the previous benchmarks for cross-lingual extractive QA. |
mForms : Multimodal Form Filling with Question Answering (2024.lrec-main)
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| Challenge: | The paper presents a new approach to form-filling by reformulating the task as multimodal natural language Question Answering (QA). |
| Approach: | They propose a new approach to form-filling by reformulating the task as multimodal natural language Question Answering (QA) the paper introduces a multimodal form-filled dataset and an extension of the popular ATIS dataset to support future research and experimentation. |
| Outcome: | The proposed approach maintains robust accuracy for sparse training conditions and achieves state-of-the-art F1 of 0.97 on ATIS with approximately 1/10th the training data. |
Cross-lingual Open-Retrieval Question Answering for African Languages (2023.findings-emnlp)
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Odunayo Ogundepo, Tajuddeen Gwadabe, Clara Rivera, Jonathan Clark, Sebastian Ruder, David Adelani, Bonaventure Dossou, Abdou Diop, Claytone Sikasote, Gilles Hacheme, Happy Buzaaba, Ignatius Ezeani, Rooweither Mabuya, Salomey Osei, Chris Emezue, Albert Kahira, Shamsuddeen Muhammad, Akintunde Oladipo, Abraham Owodunni, Atnafu Tonja, Iyanuoluwa Shode, Akari Asai, Anuoluwapo Aremu, Ayodele Awokoya, Bernard Opoku, Chiamaka Chukwuneke, Christine Mwase, Clemencia Siro, Stephen Arthur, Tunde Ajayi, Verrah Otiende, Andre Rubungo, Boyd Sinkala, Daniel Ajisafe, Emeka Onwuegbuzia, Falalu Lawan, Ibrahim Ahmad, Jesujoba Alabi, Chinedu Mbonu, Mofetoluwa Adeyemi, Mofya Phiri, Orevaoghene Ahia, Ruqayya Iro, Sonia Adhiambo
| Challenge: | Our Dataset is the first cross-lingual QA dataset with a focus on African languages. |
| Approach: | They propose to use African languages as the only high-coverage source of answer content for cross-lingual open-retrieval question answering systems. |
| Outcome: | Our Dataset includes 12,000+ XOR QA examples across 10 African languages. |
Beyond Static Synthetic Noise: Assessing the Robustness of Large Language Models to Natural Context Variation in the Real World (2026.findings-acl)
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| Challenge: | Current robustness evaluation methods rely on static synthetic perturbations to stress-test models. |
| Approach: | They propose a framework for automatically evaluating QA models under naturally occurring textual perturbations by replacing context passages with revised Wikipedia edit histories. |
| Outcome: | The proposed framework replaces context passages with revised Wikipedia edit histories to improve model performance. |