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Learning question classifiers
Abstract
In order to respond correctly to a free form factual question given a large collection of texts, one needs to understand the question to a level that allows determining some of the constraints the question imposes on a possible answer. These constraints may include a semantic classification of the sought after answer and may even suggest using different strategies when looking for and verifying a candidate answer. This paper
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Citations
Cited by
Convolutional Neural Networks for Sentence Classification
Kim, Y. (2014) · Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1181 · Cited by 14,831
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Oord, A. V. D., Li, Y., & Vinyals, O. (2018) · arXiv · doi:10.48550/arxiv.1807.03748 · Cited by 6,242
“For the classification tasks we used the following datasets: movie review sentiment (MR) [43], customer product reviews (CR) [44], subjectivity/objectivity [45], opinion polarity (MPQA) [46] and question-type classification (TREC) [47].”
MethodA Convolutional Neural Network for Modelling Sentences
Kalchbrenner, N., Grefenstette, E., & Blunsom, P. (2014) · Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.3115/v1/p14-1062 · Cited by 3,601
Generating Sentences from a Continuous Space
Bowman, S. R., Vilnis, L., Vinyals, O., Dai, A., Jozefowicz, R., & Bengio, S. (2016) · Proceedings of The 20th SIGNLL Conference on Computational Natural Language Learning · doi:10.18653/v1/k16-1002 · Cited by 2,061
A Survey of the Usages of Deep Learning for Natural Language Processing
Otter, D. W., Medina, J. R., & Kalita, J. K. (2021) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2020.2979670 · Cited by 1,836
Trojaning Attack on Neural Networks
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Finetuned Language Models Are Zero-Shot Learners
Wei, J., Bosma, M., Zhao, V. Y., Guu, K., Yu, A. W., Lester, B., Du, N., Dai, A. M., & Le, Q. V. (2021) · arXiv · doi:10.48550/arxiv.2109.01652 · Cited by 1,101
WikiQA: A Challenge Dataset for Open-Domain Question Answering
Yang, Y., Yih, W.-T., & Meek, C. (2015) · Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d15-1237 · Cited by 829
DiSAN: Directional Self-Attention Network for RNN/CNN-Free Language Understanding
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Retrieval-Augmented Generation for Large Language Models: A Survey
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Multitask Prompted Training Enables Zero-Shot Task Generalization
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Muppet: Massive Multi-task Representations with Pre-Finetuning
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Information Leakage in Embedding Models
Song, C., & Raghunathan, A. (2020) · Proceedings of the 2020 ACM SIGSAC Conference on Computer and Communications Security · doi:10.1145/3372297.3417270 · Cited by 175
MetaICL: Learning to Learn In Context
Min, S., Lewis, M., Zettlemoyer, L., & Hajishirzi, H. (2022) · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · doi:10.18653/v1/2022.naacl-main.201 · Cited by 169
Surface Form Competition: Why the Highest Probability Answer Isn’t Always Right
Holtzman, A., West, P., Shwartz, V., Choi, Y., & Zettlemoyer, L. (2021) · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2021.emnlp-main.564 · Cited by 127
A La Carte Embedding: Cheap but Effective Induction of Semantic Feature Vectors
Khodak, M., Saunshi, N., Liang, Y., Ma, T., Stewart, B., & Arora, S. (2018) · Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p18-1002 · Cited by 111
Parallel Context Windows for Large Language Models
Ratner, N., Levine, Y., Belinkov, Y., Ram, O., Magar, I., Abend, O., Karpas, E., Shashua, A., Leyton-Brown, K., & Shoham, Y. (2023) · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2023.acl-long.352 · Cited by 111
Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks
Reimers, N., & Gurevych, I. (2019) · arXiv · doi:10.48550/arxiv.1908.10084 · Cited by 343
Augmenting interpretable models with large language models during training
Singh, C., Askari, A., Caruana, R., & Gao, J. (2023) · Nature Communications · doi:10.1038/s41467-023-43713-1 · Cited by 63
A Machine Learning-based Method for Question Type Classification in Biomedical Question Answering
El Alaoui, S. O., & Sarrouti, M. (2017) · Methods of Information in Medicine · doi:10.3414/me16-01-0116 · Cited by 47
Not All Attention Is Needed: Gated Attention Network for Sequence Data
Xue, L., Li, X., & Zhang, N. L. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i04.6129 · Cited by 43
Self-citationA Mathematical Exploration of Why Language Models Help Solve Downstream Tasks
Saunshi, N., Malladi, S., & Arora, S. (2020) · arXiv · doi:10.48550/arxiv.2010.03648 · Cited by 32
Extracting Weighted Finite Automata from Recurrent Neural Networks for Natural Languages
Wei, Z., Zhang, X., & Sun, M. (2022) · Lecture Notes in Computer Science · doi:10.1007/978-3-031-17244-1_22 · Cited by 6
ChunkKV: Semantic-Preserving KV Cache Compression for Efficient Long-Context LLM Inference
Liu, X., Tang, Z., Dong, P., Li, Z., Liu, Y., Li, B., Hu, X., & Chu, X. (2025) · arXiv · doi:10.48550/arxiv.2502.00299 · Cited by 4
“This benchmark was created for multi-task evaluation of long-context inputs and includes 17 datasets covering tasks such as single-document QA [87, 88], multi-document QA [89–92], summarization [93–96], few-shot learning [97–99], synthetic tasks and code generation [100, 101].”
PyramidKV: Dynamic KV Cache Compression based on Pyramidal Information Funneling
Cai, Z., Zhang, Y., Gao, B., Liu, Y., Li, Y., Liu, T., Lu, K., Xiong, W., Dong, Y., Hu, J., et al. (2024) · arXiv · doi:10.48550/arxiv.2406.02069 · Cited by 2
“It includes 17 datasets covering tasks such as single-document QA [20, 6], multi-document QA [33, 16], summarization [17, 35, 10], few-shot learning [21, 13, 19], synthetic, and code generation [14, 24].”
BackgroundHELMET: How to Evaluate Long-Context Language Models Effectively and Thoroughly
Yen, H., Gao, T., Hou, M., Ding, K., Fleischer, D., Izsak, P., Wasserblat, M., & Chen, D. (2024) · arXiv · Cited by 2
“In-context learning (ICL) is a key ability that enables LLMs to adapt to new tasks on the fly (Brown et al., 2020 2024), we focus on datasets with large label spaces: TREC-course, TREC-fine (Li & Roth, 2002), BANKING77 (Casanueva et al., 2020), CLINC150 (Larson et al., 2019), and NLU (Liu et al., 2019).”
BackgroundEvaluating Memory in LLM Agents via Incremental Multi-Turn Interactions
Hu, Y., Wang, Y., & McAuley, J. (2025) · arXiv · doi:10.48550/arxiv.2507.05257 · Cited by 1
“Datasets for Test-Time Learning (TTL) We evaluate TTL via two task categories: (1) Multi-Class Classification (MCC) : We adopt five classification datasets used in prior TTL work [5, 50]: BANKING77 [6], CLINC150 [21], TREC-Coarse, TREC-Fine [26], and NLU [28].”
Metadata
- Authors
- Li, X., & Roth, D.
- Venue
- Proceedings of the 19th international conference on Computational linguistics -
- Published
- 2002
- Volume
- 1
- Pages
- 1-7
- ISSN
- 1525-2477 (ISSN-L)
- OpenAlex
- W2070246124
- Cited by
- 1,234
- References
- 0
- FWCI
- 13.27
- Type
- conference paper
- Language
- en
- Open access
- closed
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