| Challenge: | SemEval-16 and Semeval-17 community question answering shared tasks require complex pipelines and manual feature engineering to beat the IR baseline. |
| Approach: | They train a multi-task feed forward network on a bag of 14 distance measures for the input question pair and train it using language-independent features. |
| Outcome: | The proposed model outperforms the best shared task systems on the task of retrieving relevant previously asked questions. |
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| Challenge: | Existing methods for Question Answering to search for semantically similar questions are not suitable for new questions. |
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Neural Ranking with Weak Supervision for Open-Domain Question Answering : A Survey (2023.findings-eacl)
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| Challenge: | Neural ranking models require substantial amounts of relevance annotations, which is costly to scale. |
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Strong Baselines for Simple Question Answering over Knowledge Graphs with and without Neural Networks (N18-2)
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Strong and Efficient Baselines for Open Domain Conversational Question Answering (2023.findings-emnlp)
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How to Find Strong Summary Coherence Measures? A Toolbox and a Comparative Study for Summary Coherence Measure Evaluation (2022.coling-1)
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Retrieving Support to Rank Answers in Open-Domain Question Answering (2025.emnlp-main)
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| Challenge: | a novel question answering architecture retrieves content relevant to the combined pair . previous work on automatic claim verification has shown hallucinations . |
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An Empirical Study of Building a Strong Baseline for Constituency Parsing (P18-2)
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| Challenge: | Sequence-to-sequence models have been used for natural language generation tasks such as machine translation and summarization. |
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Deep Relevance Ranking Using Enhanced Document-Query Interactions (D18-1)
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Towards a Better Metric for Evaluating Question Generation Systems (D18-1)
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A Simple Baseline for Knowledge-Based Visual Question Answering (2023.emnlp-main)
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