Papers with SQA
Self-supervised Contrastive Cross-Modality Representation Learning for Spoken Question Answering (2021.findings-emnlp)
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| Challenge: | Experimental results show that our model achieves state-of-the-art results on three SQA benchmarks. |
| Approach: | They propose a self-supervised training stage and a contrastive representation learning stage for spoken question answering with auxiliary tasks and augmentation strategies. |
| Outcome: | The proposed model achieves state-of-the-art results on three SQA benchmarks. |
Understanding tables with intermediate pre-training (2020.findings-emnlp)
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| Challenge: | Textual entailment is well studied, but is less well studied for table enlargement . a new dataset of millions of examples is used to train the model . |
| Approach: | They adapt a table-based BERT model to recognize entailment from a dataset . they evaluate table pruning techniques as a pre-processing step to improve model efficiency . |
| Outcome: | The proposed model improves training and prediction efficiency at a moderate drop in accuracy. |
FastAdaSP: Multitask-Adapted Efficient Inference for Large Speech Language Model (2024.emnlp-industry)
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| Challenge: | Unlike other modalities, speech has unique temporal dependencies, making efficient inference methods unexplored. |
| Approach: | They propose a weighted token merging framework specifically designed for speech-related tasks to improve the trade-off between efficiency and performance. |
| Outcome: | The proposed method achieves state-of-the-art efficiency-performance trade-off on speech-related tasks. |
TableFormer: Robust Transformer Modeling for Table-Text Encoding (2022.acl-long)
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| Challenge: | Existing tables models require linearization of the table structure, where row or column order is encoded as an unwanted bias. |
| Approach: | They propose a robust and structurally aware table-text encoding architecture TableFormer where tabular structural biases are incorporated completely through learnable attention biase. |
| Outcome: | The proposed architecture outperforms strong baselines on SQA, WTQ and TabFact table reasoning datasets and achieves state-of-the-art performance on SQ. |
When Retriever-Reader Meets Scenario-Based Multiple-Choice Questions (2021.findings-emnlp)
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| Challenge: | Scenario-based question answering (SQA) requires retrieving and reading paragraphs from a large corpus to answer a question contextualized by a long scenario description. |
| Approach: | They propose a model where the retriever is implicitly supervised only using QA labels via a novel word weighting mechanism. |
| Outcome: | The proposed model outperforms strong baselines on multiple-choice questions in three datasets. |
SCITAT: A Question Answering Benchmark for Scientific Tables and Text Covering Diverse Reasoning Types (2025.findings-acl)
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Xuanliang Zhang, Dingzirui Wang, Baoxin Wang, Longxu Dou, Xinyuan Lu, Keyan Xu, Dayong Wu, Qingfu Zhu
| Challenge: | Existing scientific question answering datasets lack diverse reasoning types and neglect relevance between tables and text. |
| Approach: | They propose a scientific question answering benchmark for scientific tables and text with diverse reasoning types (SCITAT) to address these challenges, they propose QA benchmark which incorporates tables and texts to ensure that the questions encompass both tables and textes. |
| Outcome: | The proposed benchmark improves by 4.1% over baselines on SCITAT. |
RobuT: A Systematic Study of Table QA Robustness Against Human-Annotated Adversarial Perturbations (2023.acl-long)
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Yilun Zhao, Chen Zhao, Linyong Nan, Zhenting Qi, Wenlin Zhang, Xiangru Tang, Boyu Mi, Dragomir Radev
| Challenge: | Existing Table QA models are vulnerable to task-specific perturbations, such as replacing key question entities or shuffling table columns. |
| Approach: | They propose to use large language models to generate adversarial examples to enhance training, which significantly improves the robustness of Table QA models. |
| Outcome: | The proposed model significantly improves on existing Table QA models against human-annotated adversarial perturbations. |
TaPas: Weakly Supervised Table Parsing via Pre-training (2020.acl-main)
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| Challenge: | Answering natural language questions over tables is often seen as a semantic parsing task. |
| Approach: | They propose an approach to question answering over tables without generating logical forms by selecting table cells and optionally applying a corresponding aggregation operator. |
| Outcome: | The proposed approach outperforms or rivals existing models on three different datasets and performs on par with the state-of-the-art on WikiSQL and WikiTQ. |
Transfer Learning with Synthetic Corpora for Spatial Role Labeling and Reasoning (2022.emnlp-main)
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| Challenge: | Existing datasets on spatial language processing are either synthetic or at small scale. |
| Approach: | They propose a dataset for transfer learning on spatial question answering and spatial role labeling that includes a larger variety of spatial relation types and spatial expressions. |
| Outcome: | The proposed dataset can be used to evaluate spatial language processing models in real-world situations. |
DAPE-BR: Distance-Aware Positional Encoding for Mitigating Object Hallucination in LVLMs (2025.findings-emnlp)
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| Challenge: | Large Vision–Language Models (LVLMs) suffer from object hallucination, generating descriptions for objects that are absent from the image, which undermines reliability and hinders real-world deployment. |
| Approach: | They propose a positional-alignment scheme that preserves pretrained weight order while globally—- visual–text distances, embeds an isotropic fused patch-distance metric, and applies a patch-delay causal mask to enforce spatial causality. |
| Outcome: | Extensive experiments on POPE, MMStar and SQA show that DAPE-BR reduces hallucinations and boosts performance. |
Empowering Tree-structured Entailment Reasoning: Rhetorical Perception and LLM-driven Interpretability (2024.lrec-main)
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| Challenge: | Existing models for science question answering lack a framework for entailment trees . ambiguities and similarities between science facts complicate the fact retrieval process . |
| Approach: | They propose a framework for building entailment trees for science question answering . they propose to infuse knowledge that bridges the gap between reasoning types and rhetorical relations . |
| Outcome: | The proposed framework improves retrieval capabilities, understanding relationships and generating intermediate conclusions. |
GeoSQA: A Benchmark for Scenario-based Question Answering in the Geography Domain at High School Level (D19-1)
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| Challenge: | SQA is an emerging application of NLP in the medical, geography, and legal domains. |
| Approach: | They propose a dataset of 1,981 scenarios and 4,110 multiple-choice questions in geography domain at high school level. |
| Outcome: | The proposed dataset consists of 1,981 scenarios and 4,110 multiple-choice questions in the geography domain at high school level. |
RT-VQ2A2: Real Time Vector Quantized Question Answering with ASR (2024.lrec-main)
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| Challenge: | Existing frameworks for QA with large language models are difficult to implement due to noise, limited context length and latency. |
| Approach: | They propose a model-agnostic framework to address problems in QA with large language models. |
| Outcome: | The proposed framework reduces noise in the ASR output and the limited context length of LLMs and improves performance on the widely used Spoken-SQuAD dataset. |
From Scores to Preferences: Redefining Evaluation Paradigm for Speech Quality Reward Modeling (2026.findings-acl)
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Yifei Cao, Changhao Jiang, Jiabao Zhuang, Jiajun Sun, Ming Zhang, Zhiheng Xi, Hui Li, Shihan Dou, Yuran Wang, Yunke Zhang, Tao Ji, Tao Gui, Qi Zhang, Xuanjing Huang
| Challenge: | Experimental results show that the MOS-aware GRM significantly improves fine-grained speech quality discrimination. |
| Approach: | They propose a MOS-aware reward model that incorporates MOS gap into reward function during reinforcement learning. |
| Outcome: | The proposed model significantly improves fine-grained speech quality discrimination. |