Papers with question classification
Samvaadhana: A Telugu Dialogue System in Hospital Domain (D19-61)
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| Challenge: | a dialogue system for Hospital domain in Telugu is a resource-poor Dravidian language . the system handles various hospital and doctor related queries . |
| Approach: | They propose to model a dialogue system for Hospital domain in Telugu which is a resource-poor Dravidian language. |
| Outcome: | The proposed system achieves a high overall rating and a significantly accurate context-capturing method. |
Domain Adaptation for Question Answering via Question Classification (2022.coling-1)
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| Challenge: | Question answering systems often experience performance deterioration upon user-generated questions. |
| Approach: | They propose a question classification framework to help QA domains adapt to different domains. |
| Outcome: | The proposed framework improves on state-of-the-art datasets against multiple datasets. |
Enhancing Text-to-SQL with Question Classification and Multi-Agent Collaboration (2025.findings-naacl)
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| Challenge: | Existing research focuses on the optimization of prompts and improvements in workflow, with few studies delving into the exploration of the questions. |
| Approach: | They propose a text-to-SQL framework based on question classification and multi-agent collaboration (QCMA-Sql) they employ multiple cross-attention mechanisms to train a schema selector to classify questions and select the most suitable database schema. |
| Outcome: | The proposed framework outperforms state-of-the-art methods on the Spider dataset and achieves 87.4% execution accuracy. |
Leveraging Training Dynamics and Self-Training for Text Classification (2022.findings-emnlp)
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| Challenge: | Semi-supervised learning (SSL) is a promising technique for improving deep learning models when training data is scarce. |
| Approach: | They propose a semi-supervised learning approach that leverages training dynamics of unlabeled data. |
| Outcome: | The proposed method achieves an average increase in F1 score of 3.5% over baselines in low resource settings. |
Auditing Deep Learning processes through Kernel-based Explanatory Models (D19-1)
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| Challenge: | Existing nonlinearity of deep learning models can be a major drawback . ethical accountability of such systems is becoming a crucial issue . |
| Approach: | They propose to use Layerwise Relevance Propagation to trace back connections between linguistic properties of input instances and system decisions. |
| Outcome: | The proposed model evaluates the transparency and coherence of analogy-based explanations modeling an audit stage for the system. |
A Multilingual Approach to Question Classification (L18-1)
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| Challenge: | Existing work on questions has focused on understanding the structure of questions per se . a few approaches explicitly focus on information-seeking questions, but this work is either based on big data or crowdsourcing. |
| Approach: | They propose a dependency-parsed, parallel multilingual corpus of information-seeking and non-information-seeing questions . they employ a linguistically motivated rule-based system that uses linguistic cues from one language to help classify questions across other languages. |
| Outcome: | The proposed system correctly classifies questions in 79% of cases, compared to other systems. |
Sequential Learning of Convolutional Features for Effective Text Classification (D19-1)
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| Challenge: | Existing models for text classification have largely ignored convolution filters and max pooling . text classification is one of the major applications of natural language processing . |
| Approach: | They propose a convolutional attentive recurrent network model which uses convolution filters and max pooling to improve text classification. |
| Outcome: | The proposed model outperforms existing convolutional models on text classification tasks. |
FAME: Feature-Based Adversarial Meta-Embeddings for Robust Input Representations (2021.emnlp-main)
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| Challenge: | Recent work on word embeddings and pre-trained language models has shown the large impact of language representations on natural language processing (NLP) models across tasks and domains. |
| Approach: | They propose feature-based adversarial meta-embeddings with an attention function that is guided by word-specific properties, such as shape and frequency, to handle subword-based embeddings. |
| Outcome: | The proposed model improves performance in downstream tasks even with word embeddings from transformers. |
Multi-class Hierarchical Question Classification for Multiple Choice Science Exams (2020.lrec-1)
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Dongfang Xu, Peter Jansen, Jaycie Martin, Zhengnan Xie, Vikas Yadav, Harish Tayyar Madabushi, Oyvind Tafjord, Peter Clark
| Challenge: | Prior work has demonstrated that question classification (QC) can help answer a question more accurately. |
| Approach: | They propose to use a large dataset for question classification (QC) that contains 7,787 science exam questions paired with detailed classification labels from a fine-grained hierarchical taxonomy of 406 problem domains to train a BERT-based model. |
| Outcome: | The proposed model achieves a large (+0.12 MAP) gain while also achieving state-of-the-art performance on benchmark open-domain and biomedical QC datasets. |