Papers with community question answering
Interactive Text Ranking with Bayesian Optimization: A Case Study on Community QA and Summarization (2020.tacl-1)
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| Challenge: | Existing methods that focus on learning a ranking across the whole candidate space are lacking user or task-specific training data. |
| Approach: | They propose an interactive ranking approach that actively selects pairs of candidates, from which the user selects the best. |
| Outcome: | The proposed approach outperforms existing methods in community question answering and extractive multidocument summarization and is an effective reward function for reinforcement learning. |
Extractive Headline Generation Based on Learning to Rank for Community Question Answering (C18-1)
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| Challenge: | Community question answering (CQA) forums do not always have appropriate headlines because of user-generated content. |
| Approach: | They propose an extractive headline generation method that extracts the most informative substring from each question as its headline. |
| Outcome: | The proposed method outperforms baselines including a prefix-based method . it uses the prefix of a question as its headline to create the most informative substring . |
Question Condensing Networks for Answer Selection in Community Question Answering (P18-1)
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| Challenge: | Community question answering (CQA) is a subtask of community question answering . previous researches ignored the difference between the two parts and concatenated them as the question representation . |
| Approach: | They propose a question condensing network that makes use of the subject-body relationship of community questions. |
| Outcome: | The proposed model outperforms existing models on two CQA datasets. |
MultiCQA: Zero-Shot Transfer of Self-Supervised Text Matching Models on a Massive Scale (2020.emnlp-main)
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| Challenge: | a new study examines the zero-shot transfer capabilities of text matching models on a massive scale. |
| Approach: | They propose to integrate self-supervised with supervised multi-task learning on all available source domains to study the zero-shot transfer capabilities of text matching models on a massive scale. |
| Outcome: | The proposed model outperforms in-domain BERT and the previous state of the art on six benchmarks. |
Divide and Conquer: Text Semantic Matching with Disentangled Keywords and Intents (2022.findings-acl)
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| Challenge: | Existing text semantic matching models do not provide granularity for text comparison. |
| Approach: | They propose a simple yet effective training strategy for text semantic matching by disentangling keywords from intents. |
| Outcome: | The proposed approach achieves stable performance improvements against a wide range of models on three benchmarks. |